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404

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+ + + + + + + + + + + diff --git a/assets/_markdown_exec_ansi.css b/assets/_markdown_exec_ansi.css new file mode 100644 index 00000000..68bcc480 --- /dev/null +++ b/assets/_markdown_exec_ansi.css @@ -0,0 +1,355 @@ +/* + Inspired by https://spec.draculatheme.com/ specification, they should work + decently with both dark and light themes. + */ +:root { + --ansi-red: #ff5555; + --ansi-green: #50fa7b; + --ansi-blue: #265285; + --ansi-yellow: #ffb86c; + --ansi-magenta: #bd93f9; + --ansi-cyan: #8be9fd; + --ansi-black: #282a36; + --ansi-white: #f8f8f2; +} + +.-Color-Green, +.-Color-Faint-Green, +.-Color-Bold-Green, +.-Color-BrightGreen { + color: var(--ansi-green); +} + +.-Color-Red, +.-Color-Faint-Red, +.-Color-Bold-Red, +.-Color-BrightRed { + color: var(--ansi-red); +} + +.-Color-Yellow, +.-Color-Faint-Yellow, +.-Color-Bold-Yellow, +.-Color-BrightYellow { + color: var(--ansi-yellow); +} + +.-Color-Blue, +.-Color-Faint-Blue, +.-Color-Bold-Blue, +.-Color-BrightBlue { + color: var(--ansi-blue); +} + +.-Color-Magenta, +.-Color-Faint-Magenta, +.-Color-Bold-Magenta, +.-Color-BrightMagenta { + color: var(--ansi-magenta); +} + +.-Color-Cyan, +.-Color-Faint-Cyan, +.-Color-Bold-Cyan, +.-Color-BrightCyan { + color: var(--ansi-cyan); +} + +.-Color-White, +.-Color-Faint-White, +.-Color-Bold-White, +.-Color-BrightWhite { + color: var(--ansi-white); +} + +.-Color-Black, +.-Color-Faint-Black, +.-Color-Bold-Black, +.-Color-BrightBlack { + color: var(--ansi-black); +} + +.-Color-Faint { + opacity: 0.5; +} + +.-Color-Bold { + font-weight: bold; +} + +.-Color-BGBlack, +.-Color-Black-BGBlack, +.-Color-Blue-BGBlack, +.-Color-Bold-BGBlack, +.-Color-BrightBGBlack, +.-Color-Bold-Black-BGBlack, +.-Color-BrightBlack-BGBlack, +.-Color-Bold-Green-BGBlack, +.-Color-BrightGreen-BGBlack, +.-Color-Bold-Cyan-BGBlack, +.-Color-BrightCyan-BGBlack, +.-Color-Bold-Blue-BGBlack, +.-Color-BrightBlue-BGBlack, +.-Color-Bold-Magenta-BGBlack, +.-Color-BrightMagenta-BGBlack, +.-Color-Bold-Red-BGBlack, +.-Color-BrightRed-BGBlack, +.-Color-Bold-White-BGBlack, +.-Color-BrightWhite-BGBlack, +.-Color-Bold-Yellow-BGBlack, +.-Color-BrightYellow-BGBlack, +.-Color-Cyan-BGBlack, +.-Color-Green-BGBlack, +.-Color-Magenta-BGBlack, +.-Color-Red-BGBlack, +.-Color-White-BGBlack, +.-Color-Yellow-BGBlack { + background-color: var(--ansi-black); +} + +.-Color-BGRed, +.-Color-Black-BGRed, +.-Color-Blue-BGRed, +.-Color-Bold-BGRed, +.-Color-BrightBGRed, +.-Color-Bold-Black-BGRed, +.-Color-BrightBlack-BGRed, +.-Color-Bold-Green-BGRed, +.-Color-BrightGreen-BGRed, +.-Color-Bold-Cyan-BGRed, +.-Color-BrightCyan-BGRed, +.-Color-Bold-Blue-BGRed, +.-Color-BrightBlue-BGRed, +.-Color-Bold-Magenta-BGRed, +.-Color-BrightMagenta-BGRed, +.-Color-Bold-Red-BGRed, +.-Color-BrightRed-BGRed, +.-Color-Bold-White-BGRed, +.-Color-BrightWhite-BGRed, +.-Color-Bold-Yellow-BGRed, +.-Color-BrightYellow-BGRed, +.-Color-Cyan-BGRed, +.-Color-Green-BGRed, +.-Color-Magenta-BGRed, +.-Color-Red-BGRed, +.-Color-White-BGRed, +.-Color-Yellow-BGRed { + background-color: var(--ansi-red); +} + +.-Color-BGGreen, +.-Color-Black-BGGreen, +.-Color-Blue-BGGreen, +.-Color-Bold-BGGreen, +.-Color-BrightBGGreen, +.-Color-Bold-Black-BGGreen, +.-Color-BrightBlack-BGGreen, +.-Color-Bold-Green-BGGreen, +.-Color-BrightGreen-BGGreen, +.-Color-Bold-Cyan-BGGreen, +.-Color-BrightCyan-BGGreen, +.-Color-Bold-Blue-BGGreen, +.-Color-BrightBlue-BGGreen, +.-Color-Bold-Magenta-BGGreen, +.-Color-BrightMagenta-BGGreen, +.-Color-Bold-Red-BGGreen, +.-Color-BrightRed-BGGreen, +.-Color-Bold-White-BGGreen, +.-Color-BrightWhite-BGGreen, +.-Color-Bold-Yellow-BGGreen, +.-Color-BrightYellow-BGGreen, +.-Color-Cyan-BGGreen, +.-Color-Green-BGGreen, +.-Color-Magenta-BGGreen, +.-Color-Red-BGGreen, +.-Color-White-BGGreen, +.-Color-Yellow-BGGreen { + background-color: var(--ansi-green); +} + +.-Color-BGYellow, +.-Color-Black-BGYellow, +.-Color-Blue-BGYellow, +.-Color-Bold-BGYellow, +.-Color-BrightBGYellow, +.-Color-Bold-Black-BGYellow, +.-Color-BrightBlack-BGYellow, +.-Color-Bold-Green-BGYellow, +.-Color-BrightGreen-BGYellow, +.-Color-Bold-Cyan-BGYellow, +.-Color-BrightCyan-BGYellow, +.-Color-Bold-Blue-BGYellow, +.-Color-BrightBlue-BGYellow, +.-Color-Bold-Magenta-BGYellow, +.-Color-BrightMagenta-BGYellow, +.-Color-Bold-Red-BGYellow, +.-Color-BrightRed-BGYellow, +.-Color-Bold-White-BGYellow, +.-Color-BrightWhite-BGYellow, +.-Color-Bold-Yellow-BGYellow, +.-Color-BrightYellow-BGYellow, +.-Color-Cyan-BGYellow, +.-Color-Green-BGYellow, +.-Color-Magenta-BGYellow, +.-Color-Red-BGYellow, +.-Color-White-BGYellow, +.-Color-Yellow-BGYellow { + background-color: var(--ansi-yellow); +} + +.-Color-BGBlue, +.-Color-Black-BGBlue, +.-Color-Blue-BGBlue, +.-Color-Bold-BGBlue, +.-Color-BrightBGBlue, +.-Color-Bold-Black-BGBlue, +.-Color-BrightBlack-BGBlue, +.-Color-Bold-Green-BGBlue, +.-Color-BrightGreen-BGBlue, +.-Color-Bold-Cyan-BGBlue, +.-Color-BrightCyan-BGBlue, +.-Color-Bold-Blue-BGBlue, +.-Color-BrightBlue-BGBlue, +.-Color-Bold-Magenta-BGBlue, +.-Color-BrightMagenta-BGBlue, +.-Color-Bold-Red-BGBlue, +.-Color-BrightRed-BGBlue, +.-Color-Bold-White-BGBlue, +.-Color-BrightWhite-BGBlue, +.-Color-Bold-Yellow-BGBlue, +.-Color-BrightYellow-BGBlue, +.-Color-Cyan-BGBlue, +.-Color-Green-BGBlue, +.-Color-Magenta-BGBlue, +.-Color-Red-BGBlue, +.-Color-White-BGBlue, +.-Color-Yellow-BGBlue { + background-color: var(--ansi-blue); +} + +.-Color-BGMagenta, +.-Color-Black-BGMagenta, +.-Color-Blue-BGMagenta, +.-Color-Bold-BGMagenta, +.-Color-BrightBGMagenta, +.-Color-Bold-Black-BGMagenta, +.-Color-BrightBlack-BGMagenta, +.-Color-Bold-Green-BGMagenta, +.-Color-BrightGreen-BGMagenta, +.-Color-Bold-Cyan-BGMagenta, +.-Color-BrightCyan-BGMagenta, +.-Color-Bold-Blue-BGMagenta, +.-Color-BrightBlue-BGMagenta, +.-Color-Bold-Magenta-BGMagenta, +.-Color-BrightMagenta-BGMagenta, +.-Color-Bold-Red-BGMagenta, +.-Color-BrightRed-BGMagenta, +.-Color-Bold-White-BGMagenta, +.-Color-BrightWhite-BGMagenta, +.-Color-Bold-Yellow-BGMagenta, +.-Color-BrightYellow-BGMagenta, +.-Color-Cyan-BGMagenta, +.-Color-Green-BGMagenta, +.-Color-Magenta-BGMagenta, +.-Color-Red-BGMagenta, +.-Color-White-BGMagenta, +.-Color-Yellow-BGMagenta { + background-color: var(--ansi-magenta); +} + +.-Color-BGCyan, +.-Color-Black-BGCyan, +.-Color-Blue-BGCyan, +.-Color-Bold-BGCyan, +.-Color-BrightBGCyan, +.-Color-Bold-Black-BGCyan, +.-Color-BrightBlack-BGCyan, +.-Color-Bold-Green-BGCyan, +.-Color-BrightGreen-BGCyan, +.-Color-Bold-Cyan-BGCyan, +.-Color-BrightCyan-BGCyan, +.-Color-Bold-Blue-BGCyan, +.-Color-BrightBlue-BGCyan, +.-Color-Bold-Magenta-BGCyan, +.-Color-BrightMagenta-BGCyan, +.-Color-Bold-Red-BGCyan, +.-Color-BrightRed-BGCyan, +.-Color-Bold-White-BGCyan, +.-Color-BrightWhite-BGCyan, +.-Color-Bold-Yellow-BGCyan, +.-Color-BrightYellow-BGCyan, +.-Color-Cyan-BGCyan, +.-Color-Green-BGCyan, +.-Color-Magenta-BGCyan, +.-Color-Red-BGCyan, +.-Color-White-BGCyan, +.-Color-Yellow-BGCyan { + background-color: var(--ansi-cyan); +} + +.-Color-BGWhite, +.-Color-Black-BGWhite, +.-Color-Blue-BGWhite, +.-Color-Bold-BGWhite, +.-Color-BrightBGWhite, +.-Color-Bold-Black-BGWhite, +.-Color-BrightBlack-BGWhite, +.-Color-Bold-Green-BGWhite, +.-Color-BrightGreen-BGWhite, +.-Color-Bold-Cyan-BGWhite, +.-Color-BrightCyan-BGWhite, +.-Color-Bold-Blue-BGWhite, +.-Color-BrightBlue-BGWhite, +.-Color-Bold-Magenta-BGWhite, +.-Color-BrightMagenta-BGWhite, +.-Color-Bold-Red-BGWhite, +.-Color-BrightRed-BGWhite, +.-Color-Bold-White-BGWhite, +.-Color-BrightWhite-BGWhite, +.-Color-Bold-Yellow-BGWhite, +.-Color-BrightYellow-BGWhite, +.-Color-Cyan-BGWhite, +.-Color-Green-BGWhite, +.-Color-Magenta-BGWhite, +.-Color-Red-BGWhite, +.-Color-White-BGWhite, +.-Color-Yellow-BGWhite { + background-color: var(--ansi-white); +} + +.-Color-Black, +.-Color-Bold-Black, +.-Color-BrightBlack, +.-Color-Black-BGBlack, +.-Color-Bold-Black-BGBlack, +.-Color-BrightBlack-BGBlack, +.-Color-Black-BGGreen, +.-Color-Red-BGRed, +.-Color-Bold-Red-BGRed, +.-Color-BrightRed-BGRed, +.-Color-Bold-Blue-BGBlue, +.-Color-BrightBlue-BGBlue, +.-Color-Blue-BGBlue { + text-shadow: 0 0 1px var(--ansi-white); +} + +.-Color-Bold-Cyan-BGCyan, +.-Color-BrightCyan-BGCyan, +.-Color-Bold-Magenta-BGMagenta, +.-Color-BrightMagenta-BGMagenta, +.-Color-Bold-White, +.-Color-BrightWhite, +.-Color-Bold-Yellow-BGYellow, +.-Color-BrightYellow-BGYellow, +.-Color-Bold-Green-BGGreen, +.-Color-BrightGreen-BGGreen, +.-Color-Cyan-BGCyan, +.-Color-Cyan-BGGreen, +.-Color-Green-BGCyan, +.-Color-Green-BGGreen, +.-Color-Magenta-BGMagenta, +.-Color-White, +.-Color-White-BGWhite, +.-Color-Yellow-BGYellow { + text-shadow: 0 0 1px var(--ansi-black); +} \ No newline at end of file diff --git a/assets/_markdown_exec_pyodide.css b/assets/_markdown_exec_pyodide.css new file mode 100644 index 00000000..71f9f285 --- /dev/null +++ b/assets/_markdown_exec_pyodide.css @@ -0,0 +1,50 @@ +html[data-theme="light"] { + @import "https://cdn.jsdelivr.net/npm/highlightjs-themes@1.0.0/tomorrow.css" +} + +html[data-theme="dark"] { + @import "https://cdn.jsdelivr.net/npm/highlightjs-themes@1.0.0/tomorrow-night-blue.min.css" +} + + +.ace_gutter { + z-index: 1; +} + +.pyodide-editor { + width: 100%; + min-height: 200px; + max-height: 400px; + font-size: .85em; +} + +.pyodide-editor-bar { + color: var(--md-primary-bg-color); + background-color: var(--md-primary-fg-color); + width: 100%; + font: monospace; + font-size: 0.75em; + padding: 2px 0 2px; +} + +.pyodide-bar-item { + padding: 0 18px 0; + display: inline-block; + width: 50%; +} + +.pyodide pre { + margin: 0; +} + +.pyodide-output { + width: 100%; + margin-bottom: -15px; + min-height: 46px; + max-height: 400px +} + +.pyodide-clickable { + cursor: pointer; + text-align: right; +} \ No newline at end of file diff --git a/assets/_markdown_exec_pyodide.js b/assets/_markdown_exec_pyodide.js new file mode 100644 index 00000000..1f6ae91b --- /dev/null +++ b/assets/_markdown_exec_pyodide.js @@ -0,0 +1,109 @@ +var _sessions = {}; + +function getSession(name, pyodide) { + if (!(name in _sessions)) { + _sessions[name] = pyodide.globals.get("dict")(); + } + return _sessions[name]; +} + +function writeOutput(element, string) { + element.innerHTML += string + '\n'; +} + +function clearOutput(element) { + element.innerHTML = ''; +} + +async function evaluatePython(pyodide, editor, output, session) { + pyodide.setStdout({ batched: (string) => { writeOutput(output, string); } }); + let result, code = editor.getValue(); + clearOutput(output); + try { + result = await pyodide.runPythonAsync(code, { globals: getSession(session, pyodide) }); + } catch (error) { + writeOutput(output, error); + } + if (result) writeOutput(output, result); + hljs.highlightElement(output); +} + +async function initPyodide() { + try { + let pyodide = await loadPyodide(); + await pyodide.loadPackage("micropip"); + return pyodide; + } catch(error) { + return null; + } +} + +function getTheme() { + return document.body.getAttribute('data-md-color-scheme'); +} + +function setTheme(editor, currentTheme, light, dark) { + // https://gist.github.com/RyanNutt/cb8d60997d97905f0b2aea6c3b5c8ee0 + if (currentTheme === "default") { + editor.setTheme("ace/theme/" + light); + document.querySelector(`link[title="light"]`).removeAttribute("disabled"); + document.querySelector(`link[title="dark"]`).setAttribute("disabled", "disabled"); + } else if (currentTheme === "slate") { + editor.setTheme("ace/theme/" + dark); + document.querySelector(`link[title="dark"]`).removeAttribute("disabled"); + document.querySelector(`link[title="light"]`).setAttribute("disabled", "disabled"); + } +} + +function updateTheme(editor, light, dark) { + // Create a new MutationObserver instance + const observer = new MutationObserver((mutations) => { + // Loop through the mutations that occurred + mutations.forEach((mutation) => { + // Check if the mutation was a change to the data-md-color-scheme attribute + if (mutation.attributeName === 'data-md-color-scheme') { + // Get the new value of the attribute + const newColorScheme = mutation.target.getAttribute('data-md-color-scheme'); + // Update the editor theme + setTheme(editor, newColorScheme, light, dark); + } + }); + }); + + // Configure the observer to watch for changes to the data-md-color-scheme attribute + observer.observe(document.body, { + attributes: true, + attributeFilter: ['data-md-color-scheme'], + }); +} + +async function setupPyodide(idPrefix, install = null, themeLight = 'tomorrow', themeDark = 'tomorrow_night', session = null) { + const editor = ace.edit(idPrefix + "editor"); + const run = document.getElementById(idPrefix + "run"); + const clear = document.getElementById(idPrefix + "clear"); + const output = document.getElementById(idPrefix + "output"); + + updateTheme(editor, themeLight, themeDark); + + editor.session.setMode("ace/mode/python"); + setTheme(editor, getTheme(), themeLight, themeDark); + + writeOutput(output, "Initializing..."); + let pyodide = await pyodidePromise; + if (install && install.length) { + micropip = pyodide.pyimport("micropip"); + for (const package of install) + await micropip.install(package); + } + clearOutput(output); + run.onclick = () => evaluatePython(pyodide, editor, output, session); + clear.onclick = () => clearOutput(output); + output.parentElement.parentElement.addEventListener("keydown", (event) => { + if (event.ctrlKey && event.key.toLowerCase() === 'enter') { + event.preventDefault(); + run.click(); + } + }); +} + +var pyodidePromise = initPyodide(); diff --git a/assets/_mkdocstrings.css b/assets/_mkdocstrings.css new file mode 100644 index 00000000..e69de29b diff --git a/batch/index.html b/batch/index.html new file mode 100644 index 00000000..4ff70e31 --- /dev/null +++ b/batch/index.html @@ -0,0 +1,667 @@ + + + + + + + + The Batch System - Introduction to Kebnekaise + + + + + + + + + + + + + + + + + +
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+
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    • +
  • The Batch System
    • +
  • +
    • +
+
+
+
+
+ +

The Batch System (SLURM)

+
+

Objectives

+
    +
  • Get information about what a batch system is and which one is used at HPC2N.
    • +
  • Learn basic commands for the batch system used at HPC2N.
    • +
  • How to create a basic batch script.
    • +
  • Managing your job: submitting, status, cancelling, checking…
    • +
  • Learn how to allocate specific parts of Kebnekaise: skylake, zen3/zen4, GPUs…
    • +
+
+
    +
  • Large/long/parallel jobs must be run through the batch system.
    • +
  • Kebnekaise is running Slurm.
    • +
  • Slurm is an Open Source job scheduler, which provides three key functions.
      +
    • Keeps track of available system resources.
      • +
    • Enforces local system resource usage and job scheduling policies.
      • +
    • Manages a job queue, distributing work across resources according to policies.
      • +
    +
    • +
  • In order to run a batch job, you need to create and submit a SLURM submit file (also called a batch submit file, a batch script, or a job script).
    • +
+
+

Note

+

Guides and documentation for the batch system at HPC2N here at: HPC2N’s batch system documentation.

+
+

Basic commands

+

Using a job script is often recommended.

+
    +
  • If you ask for the resources on the command line, you will wait for the program to run before you can use the window again (unless you can send it to the background with &).
    • +
  • If you use a job script you have an easy record of the commands you used, to reuse or edit for later use.
    • +
+
+

Note

+

When you submit a job, the system will return the Job ID. You can also get it with squeue -me. See below.

+
+

In the following, JOBSCRIPT is the name you have given your job script and JOBID is the job ID for your job, assigned by Slurm. USERNAME is your username.

+
    +
  • Submit job: sbatch JOBSCRIPT
    • +
  • Get list of your jobs: squeue -u USERNAME or squeue --me
    • +
  • Give the Slurm commands on the command line: srun commands-for-your-job/program
    • +
  • Check on a specific job: scontrol show job JOBID
    • +
  • Delete a specific job: scancel JOBID
    • +
  • Delete all your own jobs: scancel -u USERNAME
    • +
  • Request an interactive allocation: salloc -A PROJECT-ID .......
      +
    • Note that you will still be on the login node when the prompt returns and you MUST preface with srun to run on the allocated resources.
      • +
    • I.e. srun MYPROGRAM
      • +
    +
    • +
  • Get more detailed info about jobs:
    sacct -l -j JOBID -o jobname,NTasks,nodelist,MaxRSS,MaxVMSize
      +
    • More flags etc. can be found with man sacct
      • +
    • The output will be very wide. To view in a friendlier format, use
      sacct -l -j JOBID -o jobname,NTasks,nodelist,MaxRSS,MaxVMSize | less -S
        +
      • this makes it sideways scrollable, using the left/right arrow key
        • +
      +
      • +
    +
    • +
  • Web url with graphical info about a job: job-usage JOBID
    • +
  • More information: man sbatch, man srun, man ....
    • +
+
+

Example

+

Submit job with sbatch

+
b-an01 [~]$ sbatch simple.sh 
+Submitted batch job 27774852
+
+

Check status with squeue --me

+
b-an01 [~]$ squeue --me
+         JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
+      27774852  cpu_zen4 simple.s bbrydsoe  R       0:00      1 b-cn1701
+
+

Submit several jobs (here several instances of the same), check on the status

+
b-an01 [~]$ sbatch simple.sh 
+Submitted batch job 27774872
+b-an01 [~]$ sbatch simple.sh 
+Submitted batch job 27774873
+b-an01 [~]$ sbatch simple.sh 
+Submitted batch job 27774874
+b-an01 [~]$ squeue --me
+            JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
+         27774873  cpu_zen4 simple.s bbrydsoe  R       0:02      1 b-cn1702
+         27774874  cpu_zen4 simple.s bbrydsoe  R       0:02      1 b-cn1702
+         27774872  cpu_zen4 simple.s bbrydsoe  CG      0:04      1 b-cn1702
+
+

The status “R” means it is running. “CG” means completing. When a job is pending it has the state “PD”.

+

In these examples the jobs all ended up on nodes in the partition cpu_zen4. We will soon talk more about different types of nodes.

+
+

Job scripts and output

+

The official name for batch scripts in Slurm is Job Submission Files, but here we will use both names interchangeably. If you search the internet, you will find several other names used, including Slurm submit file, batch submit file, batch script, job script.

+

A job submission file can contain any of the commands that you would otherwise issue yourself from the command line. It is, for example, possible to both compile and run a program and also to set any necessary environment values (though remember that Slurm exports the environment variables in your shell per default, so you can also just set them all there before submitting the job).

+
+

Note

+

The results from compiling or running your programs can generally be seen after the job has completed, though as Slurm will write to the output file during the run, some results will be available quicker.

+
+

Outputs and any errors will per default be placed in the directory you are running from, though this can be changed.

+
+

Note

+

This directory should preferrably be placed under your project storage, since your home directory only has 25 GB of space.

+
+

The output file from the job run will default be named slurm-JOBID.out. It will contain both output as well as any errors. You can look at the content with vi, nano, emacs, cat, less…

+

The exception is if your program creates its own output files, or if you name the output file(s) differently within your jobscript.

+
+

Note

+

You can use Slurm commands within your job script to split the error and output in separate files, and name them as you want. It is highly recommended to include the environment variable %J (the job ID) in the name, as that is an easy way to get a new name for each time you run the script and thus avoiding the previous output being overwritten.

+

Example, using the environment variable %J:

+
    +
  • Error file: #SBATCH --error=job.%J.err
    • +
  • Output file: #SBATCH --output=job.%J.out
    • +
+
+

Job scripts

+

A job submission file can either be very simple, with most of the job attributes specified on the command line, or it may consist of several Slurm directives, comments and executable statements. A Slurm directive provides a way of specifying job attributes in addition to the command line options.

+

Naming: You can name your script anything, including the suffix. It does not matter. Just name it something that makes sense to you and helps you remember what the script is for. The standard is to name it with a suffix of .sbatch or .sh.

+

Simple, serial job script

+
#!/bin/bash
+# The name of the account you are running in, mandatory.
+#SBATCH -A hpc2nXXXX-YYY
+# Request resources - here for a serial job
+# tasks per core is 1 as default (can be changed with ``-c``)
+#SBATCH -n 1
+# Request runtime for the job (HHH:MM:SS) where 168 hours is the maximum. Here asking for 15 min. 
+#SBATCH --time=00:15:00 
+
+# Clear the environment from any previously loaded modules
+module purge > /dev/null 2>&1
+
+# Load the module environment suitable for the job - here foss/2022b 
+module load foss/2022b
+
+# And finally run the serial jobs 
+./my_serial_program
+
+
+

Note

+
    +
  • You have to always include #!/bin/bash at the beginning of the script, since bash is the only supported shell. Some things may work under other shells, but not everything.
    • +
  • All Slurm directives start with #SBATCH.
    • +
  • One (or more) # in front of a text line means it is a comment, with the exception of the string #SBATCH. In order to comment out the Slurm directives, you need to put one more # in front of the #SBATCH.
    • +
  • It is important to use capital letters for #SBATCH. Otherwise the line will be considered a comment, and ignored.
    • +
+
+

Let us go through the most commonly used arguments:

+
    +
  • -A PROJ-ID: The project that should be accounted. It is a simple conversion from the SUPR project id. You can also find your project account with the command projinfo. The PROJ-ID argument is of the form
      +
    • hpc2nXXXX-YYY (HPC2N local project)
      • +
    +
    • +
  • -N: number of nodes. If this is not given, enough will be allocated to fullfill the requirements of -n and/or -c. A range can be given. If you ask for, say, 1-1, then you will get 1 and only 1 node, no matter what you ask for otherwise. It will also assure that all the processors will be allocated on the same node.
    • +
  • -n: number of tasks.
    • +
  • -c: cores per task. Request that a specific number of cores be allocated to each task. This can be useful if the job is multi-threaded and requires more than one core per task for optimal performance. The default is one core per task.
    • +
+

Simple MPI program

+
#!/bin/bash
+# The name of the account you are running in, mandatory.
+#SBATCH -A hpc2nXXXX-YYY
+# Request resources - here for eight MPI tasks
+#SBATCH -n 8
+# Request runtime for the job (HHH:MM:SS) where 168 hours is the maximum. Here asking for 15 min. 
+#SBATCH --time=00:15:00 
+
+# Clear the environment from any previously loaded modules
+module purge > /dev/null 2>&1
+
+# Load the module environment suitable for the job - here foss/2022b 
+module load foss/2022b
+
+# And finally run the job - use srun for MPI jobs, but not for serial jobs 
+srun ./my_mpi_program
+
+

Exercises

+

If you have not already done so, clone the material from the website https://github.com/hpc2n/intro-course:

+
    +
  1. Change to the storage area you created under /proj/nobackup/intro-hpc2n/.
    2. +
  2. Clone the material: +
    git clone https://github.com/hpc2n/intro-course.git
+
    3. +
  3. Change to the subdirectory with the exercises: +
    cd intro-course/exercises/simple
+
    4. +
+

You will now find several small programs and batch scripts which are used in this section and the next, “Simple examples”.

+

In this section, we are just going to try submitting a few jobs, checking their status, cancelling a job, and looking at the output.

+
+

Preparations

+
    +
  1. Load the module foss/2022b (ml foss/2022b) on the regular login node. This module is available on all nodes.
    2. +
  2. Compile the following programs: hello.c, mpi_hello.c, mpi_greeting.c, and mpi_hi.c +
    gcc -o hello hello.c
+mpicc -o mpi_hello mpi_hello.c
+mpicc -o mpi_greeting mpi_greeting.c
+mpicc -o mpi_hi mpi_hi.c
+
    3. +
  3. If you compiled and named the executables as above, you should be able to submit the following batch scripts directly: simple.sh, mpi_greeting.sh, mpi_hello.sh, mpi_hi.sh, multiple-parallel-sequential.sh, multiple-parallel.sh, or multiple-parallel-simultaneous.sh.
    4. +
+
+
+

Exercise: sbatch and squeue

+

Submit (sbatch) one of the batch scripts listed in 3. under preparations. Check with squeue --me if it is running, pending, or completing.

+
+
+

Exercise: sbatch and scontrol show job

+

Submit a few instances of multiple-parallel.sh and multiple-parallel-sequential.sh (so they do not finish running before you have time to check on them).

+

Do scontrol show job JOBID on one or more of the job IDs. You should be able to see node assigned (unless the job has not yet had one allocated), expected runtime, etc. If the job is running, you can see how long it has run. You will also get paths to submit directory etc.

+
+
+

Exercise: sbatch and scancel

+

Submit a few instances of multiple-parallel.sh and multiple-parallel-sequential.sh (so they do not finish running before you have time to check on them).

+

Do squeue --me and see the jobs listed. Pick one and do scancel JOBID on it. Do squeue --me again to see it is no longer there.

+
+
+

Exercise: check output

+

Use nano to open one of the output files slurm-JOBID.out.

+

Try adding #SBATCH --error=job.%J.err and #SBATCH --output=job.%J.out to one of the batch scripts (you can edit it with nano). Submit the batch script again. See that the expected files get created.

+
+

Using the different parts of Kebnekaise

+

As mentioned under the introduction, Kebnekaise is a very heterogeneous system, comprised of several different types of CPUs and GPUs. The batch system reflects these several different types of resources.

+

At the top we have partitions, which are similar to queues. Each partition is made up of a specific set of nodes. At HPC2N we have three classes of partitions, one for CPU-only nodes, one for GPU nodes and one for large memory nodes. Each node type also has a set of features that can be used to select which node(s) the job should run on.

+

The three types of nodes also have corresponding resources one must apply for in SUPR to be able to use them.

+

While Kebnekaise has multiple partitions, one for each major type of resource, there is only a single partition, batch, that users can submit jobs to. The system then figures out which partition(s) the job should be sent to, based on the requested features.

+
+

Node overview

+

The “Type” can be used if you need a specific type of node. More about that later.

+

CPU-only nodes

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CPUMemory/corenumber nodesType
2 x 14 core Intel broadwell4460 MB48broadwell (intel_cpu)
2 x 14 core Intel skylake6785 MB52skylake (intel_cpu)
2 x 64 core AMD zen38020 MB1zen3 (amd_cpu)
2 x 128 core AMD zen42516 MB8zen4 (amd_cpu)
+

GPU enabled nodes

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CPUMemory/coreGPU cardnumber nodesType
2 x 14 core Intel broadwell9000 MB2 x Nvidia A404a40
2 x 14 core Intel skylake6785 MB2 x Nvidia V10010v100
2 x 24 core AMD zen310600 MB2 x Nvidia A1002a100
2 x 24 core AMD zen310600 MB2 x AMD MI1001mi100
2 x 24 core AMD zen46630 MB2 x Nvidia A60001a6000
2 x 24 core AMD zen46630 MB2 x Nvidia L40s10l40s
2 x 48 core AMD zen46630 MB4 x Nvidia H100 SXM52h100
+

Large memory nodes

+ + + + + + + + + + + + + + + + + +
CPUMemory/corenumber nodesType
4 x 18 core Intel broadwell41666 MB8largemem
+
+

Requesting features

+

To make it possible to target nodes in more detail there are a couple of features defined on each group of nodes. To select a feature one can use the -C option to sbatch or salloc. This sets constraints on the job.

+

There are several reasons why one might want to do that, including for benchmarks, to be able to replicate results (in some cases), because specific modules are only available for certain architectures, etc.

+

To constrain a job to a certain feature, use

+
#SBATCH -C Type
+
+
+

Note

+

Features can be combined using “and” (&) or “or” (|). They should be wrapped in '’s.

+

Example:

+
#SBATCH -C 'zen3|zen4'
+
+
+

List of constraints:

+
+

For selecting type of CPU

+

Type is:

+
    +
  • intel_cpu
    • +
  • broadwell
    • +
  • skylake
    • +
  • amd_cpu
    • +
  • zen3
    • +
  • zen4
    • +
+
+
+

For selecting type of GPU

+

Type is:

+
    +
  • v100
    • +
  • a40
    • +
  • a6000
    • +
  • a100
    • +
  • l40s
    • +
  • h100
    • +
  • mi100
    • +
+
+

For GPUs, the above GPU list of constraints can be used either as a specifier to --gpu=type:number or as a constraint together with an unspecified gpu request --gpu=number.

+
+

For selecting GPUs with certain features

+

Type is:

+
    +
  • nvidia_gpu (Any Nvidia GPU)
    • +
  • amd_gpu (Any AMD GPU)
    • +
  • GPU_SP (GPU with single precision capability)
    • +
  • GPU_DP (GPU with double precision capability)
    • +
  • GPU_AI (GPU with AI features, like half precisions and lower)
    • +
  • GPU_ML (GPU with ML features, like half precisions and lower)
    • +
+
+
+

For selecting large memory nodes

+

Type is:

+
    +
  • largemem
    • +
+
+

Examples, constraints

+
+

Only nodes with Zen4

+
#SBATCH -C zen4
+
+
+
+

Nodes with a combination of features: a Zen4 CPU and a GPU with AI features

+
#SBATCH -C 'zen4&GPU_AI'
+
+
+
+

Nodes with either a Zen3 CPU or a Zen4 CPU

+
#SBATCH -C 'zen3|zen4'
+
+
+

Examples, requesting GPUs

+

To use GPU resources one has to explicitly ask for one or more GPUs. Requests for GPUs can be done either in total for the job or per node of the job.

+
+

Ask for one GPU of any kind

+
#SBATCH --gpus=1
+
+
+
+

Another way to ask for one GPU of any kind

+
#SBATCH --gpus-per-node=1
+
+
+
+

Asking for a specific type of GPU

+

As mentioned before, for GPUs, constraints can be used either as a specifier to

+

--gpu=type:number

+

or as a constraint together with an unspecified gpu request

+

--gpu=number.

+
#SBATCH --gpus=Type:NUMBER
+
+

where Type is, as mentioned:

+
    +
  • v100
    • +
  • a40
    • +
  • a6000
    • +
  • a100
    • +
  • l40s
    • +
  • h100
    • +
  • mi100
    • +
+
+

Simple GPU Job - V100

+
#!/bin/bash
+#SBATCH -A hpc2nXXXX-YYY
+# Expected time for job to complete 
+#SBATCH --time=00:10:00
+# Number of GPU cards needed. Here asking for 2 V100 cards
+#SBATCH --gpu=v100:2
+
+# Clear the environment from any previously loaded modules
+module purge > /dev/null 2>&1
+# Load modules needed for your program - here fosscuda/2021b
+ml fosscuda/2021b
+
+./my-gpu-program
+
+
+

Important

+
    +
  • The course project has the following project ID: hpc2n2024-084
    • +
  • In order to use it in a batch job, add this to the batch script: #SBATCH -A hpc2n2024-084
    • +
  • We have a storage project linked to the compute project: intro-hpc2n.
      +
    • You find it in /proj/nobackup/intro-hpc2n.
      • +
    • Remember to create your own directory under it.
      • +
    +
    • +
+
+
+

Keypoints

+
    +
  • To submit a job, you first need to create a batch submit script, which you then submit with sbatch SUBMIT-SCRIPT.
    • +
  • You can get a list of your running and pending jobs with squeue --me.
    • +
  • Kebnekaise has many different nodes, both CPU and GPU. It is possible to constrain the the job to run only on specific types of nodes.
    • +
  • If your job is an MPI job, you need to use srun in front of your executable in the batch script (unless you use software which handles the parallelization itself).
    • +
+
+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + + + diff --git a/compilers/index.html b/compilers/index.html new file mode 100644 index 00000000..f547e6d4 --- /dev/null +++ b/compilers/index.html @@ -0,0 +1,483 @@ + + + + + + + + Compiling - Introduction to Kebnekaise + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
    +
    • +
  • Compiling
    • +
  • +
    • +
+
+
+
+
+ +

Compiling and Linking with Libraries

+
+

Objectives

+
    +
  • Learn about the compilers at HPC2N
      +
    • How to load the compiler toolchains
      • +
    • How to use the compilers
      • +
    • What are the popular flags
      • +
    +
    • +
  • How to link with libraries.
    • +
+
+

Installed compilers

+

There are compilers available for Fortran 77, Fortran 90, Fortran 95, C, and C++. The compilers can produce both general-purpose code and architecture-specific optimized code to improve performance (loop-level optimizations, inter-procedural analysis and cache optimizations).

+

Loading compilers

+
+

Note

+

You need to load a compiler suite (and possibly libraries, depending on what you need) before you can compile and link.

+
+

Use ml av to get a list of available compiler toolchains as mentioned in the modules - compiler toolchains section.

+

You load a compiler toolchain the same way you load any other module. They are always available directly, without the need to load prerequisites first.

+
+

Hint

+

Code-along!

+
+
+

Example: Loading foss/2023b

+

This compiler toolchain contains: GCC/13.2.0, BLAS (with LAPACK), ScaLAPACK, and FFTW.

+
b-an01 [~]$ ml foss/2023b
+b-an01 [~]$ ml
+
+Currently Loaded Modules:
+  1) snicenvironment (S)   7) numactl/2.0.16     13) libevent/2.1.12  19) FlexiBLAS/3.3.1
+  2) systemdefault   (S)   8) XZ/5.4.4           14) UCX/1.15.0       20) FFTW/3.3.10
+  3) GCCcore/13.2.0        9) libxml2/2.11.5     15) PMIx/4.2.6       21) FFTW.MPI/3.3.10
+  4) zlib/1.2.13          10) libpciaccess/0.17  16) UCC/1.2.0        22) ScaLAPACK/2.2.0-fb
+  5) binutils/2.40        11) hwloc/2.9.2        17) OpenMPI/4.1.6    23) foss/2023b
+  6) GCC/13.2.0           12) OpenSSL/1.1        18) OpenBLAS/0.3.24
+
+  Where:
+   S:  Module is Sticky, requires --force to unload or purge
+
+
+
+b-an01 [~]$ 
+
+
+

Compiling

+
+

Note

+

OpenMP: All compilers has this included, so it is enough to load the module for a specific compiler toolchain and then add the appropriate flag.

+
+
+

Note

+

If you do not name the executable (with the flag -o SOMENAME, it will be named a.out as default.

+

This also means that the next time you compile something, if you also do not name that executable, it will overwrite the previous a.out file.

+
+

Compiling with GCC

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
LanguageCompiler nameMPI
Fortran77gfortranmpif77
Fortran90gfortranmpif90
Fortran95gfortranN/A
Cgccmpicc
C++g++mpiCC
+

In order to access the MPI compilers, load a compiler toolchain which contains an MPI library.

+
+

Hint

+

Code-along!

+
+
+

Example: compiling a C program

+

You can find the file hello.c in the exercises directory, in the subdirectory “simple”. Or you can download it here: hello.c.

+

In this example we compile the C program hello.c and name the output (the executable) hello.

+
b-an01 [~]$ gcc hello.c -o hello 
+
+

You can run the executable with ./hello

+
+
+

Example: compiling an MPI C program

+

You can find the file mpi_hello.c in the exercises directory, in the subdirectory “simple”. Or you can download it here: mpi_hello.c.

+

In this example we compile the MPI C program mpi_hello.c and name the output (the executable) mpi_hello.

+
b-an01 [~]$ mpicc mpi_hello.c -o mpi_hello 
+
+

You then run with `mpirun mpi_hello

+
+
+

Important

+

If you later have loaded a different compiler than the one your program was compiled with, you should recompile your program before running it.

+
+
+

Exercise

+

Try loading foss/2023b and compiling mpi_hello.c, then unload the module and instead load the module intel/2023b and see what happens if you try to run with mpirun mpi_hello.

+
+

Flags

+
+

Note

+

List of commonly used flags:

+
    +
  • -o file Place output in file ‘file’.
    • +
  • -c Compile or assemble the source files, but do not link.
    • +
  • -fopenmp Enable handling of the OpenMP directives.
    • +
  • -g Produce debugging information in the operating systems native format.
    • +
  • -O or -O1 Optimize. The compiler tried to reduce code size and execution time.
    • +
  • -O2 Optimize even more. GCC performs nearly all supported optimizations that do not involve a space-speed tradeoff.
    • +
  • -O3 Optimize even more. The compiler will also do loop unrolling and function inlining. RECOMMENDED
    • +
  • -O0 Do not optimize. This is the default.
    • +
  • -Os Optimize for size.
    • +
  • -Ofast Disregard strict standards compliance. -Ofast enables all -O3 optimizations. It also enables optimizations that are not valid for all standard-compliant programs. It turns on -ffast-math and the Fortran-specific -fno-protect-parens and -fstack-arrays.
    • +
  • -ffast-math Sets the options -fno-math-errno, -funsafe-math-optimizations, -ffinite-math-only, -fno-rounding-math, -fno-signaling-nans and -fcx-limited-range.
    • +
  • -l library Search the library named ‘library’ when linking.
    • +
+
+
+

Hint

+

Code-along!

+
+
+

Example: compiling an OpenMP C program

+

You can find the file omp_hello.c in the exercises directory, in the subdirectory “simple”. Or you can download it here: omp_hello.c.

+

In this example we compile the OpenMP C program omp_hello.c and name the output (executable) omp_hello.

+
b-an01 [~]$ gcc -fopenmp omp_hello.c -o omp_hello
+
+
+
+

Note

+

You can change the number of threads with export OMP_NUM_THREADS=#threads

+
+
+

Hint

+

Code-along!

+
+
+

Example

+

Run the binary omp_hello that we got in the previous example. Set the number of threads to 4 and then rerun the binary.

+
b-an01 [~]$ ./omp_hello 
+Thread 0 says: Hello World
+Thread 0 reports: the number of threads are 1
+b-an01 [~]$ export OMP_NUM_THREADS=4
+b-an01 [~]$ ./omp_hello 
+Thread 1 says: Hello World
+Thread 0 says: Hello World
+Thread 0 reports: the number of threads are 4
+Thread 3 says: Hello World
+Thread 2 says: Hello World
+b-an01 [~]$ 
+
+
+
+

Exercise

+

Try yourself! Rerun with OMP_NUM_THREADS set to 1, 2, 4, 8.

+

NOTE: Normally you are not supposed to run anything on the command line, but these are very short and light-weight programs.

+
+
+

Exercise

+

You could try with a different toolchain (or version). Remember to unload/purge, load the new toolchain, compile the program again, and then run.

+
+

Compiling with Intel

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
LanguageCompiler nameMPI
Fortran77ifortmpiifort
Fortran90ifortmpiifort
Fortran95ifortN/A
Ciccmpiicc
C++icpcmpiicc
+

In order to access the MPI compilers, load a compiler toolchain which contains an MPI library.

+
+

Example: compiling a C program

+

We are again compiling the hello.c program from before. This time we name the executable hello_intel to not overwrite the previously created executable.

+
b-an01 [~]$ icc hello.c -o hello 
+
+
+

Flags

+
+

Note

+

List of commonly used flags:

+
    +
  • -fast This option maximizes speed across the entire program.
    • +
  • -g Produce symbolic debug information in an object file. The -g option changes the default optimization from -O2 to -O0. It is often a good idea to add -traceback also, so the compiler generates extra information in the object file to provide source file traceback information.
    • +
  • -debug all Enables generation of enhanced debugging information. You need to also specify -g
    • +
  • -O0 Disable optimizations. Use if you want to be certain of getting correct code. Otherwise use -O2 for speed.
    • +
  • -O Same as -O2
    • +
  • -O1 Optimize to favor code size and code locality. Disables loop unrolling. -O1 may improve performance for applications with very large code size, many branches, and execution time not dominated by code within loops. In most cases, -O2 is recommended over -O1.
    • +
  • -O2 (default) Optimize for code speed. This is the generally recommended optimization level.
    • +
  • -O3 Enable -O2 optimizations and in addition, enable more aggressive optimizations such as loop and memory access transformation, and prefetching. The -O3 option optimizes for maximum speed, but may not improve performance for some programs and may in some cases even slow down code.
    • +
  • -Os Enable speed optimizations, but disable some optimizations that increase code size for small speed benefit.
    • +
  • -fpe{0,1,3} Allows some control over floating-point exception (divide by zero, overflow, invalid operation, underflow, denormalized number, positive infinity, negative infinity or a NaN) handling for the main program at runtime. Fortran only.
    • +
  • -qopenmp Enable the parallelizer to generate multi-threaded code based on the OpenMP directives.
    • +
  • -parallel Enable the auto-parallelizer to generate multi-threaded code for loops that can be safely executed in parallel.
    • +
+
+

Linking

+

Build environment

+

Using a compiler toolchain by itself is possible but requires a fair bit of manual work, figuring out which paths to add to -I or -L for including files and libraries, and similar.

+

To make life as a software builder easier there is a special module available, buildenv, that can be loaded on top of any toolchain. If it is missing for some toolchain, send a mail to support@hpc2n.umu.se and let us know.

+

This module defines a large number of environment variables with the relevant settings for the used toolchain. Among other things it sets CC, CXX, F90, FC, MPICC, MPICXX, MPIF90, CFLAGS, FFLAGS, and much more.

+

To see all of them, after loading a toolchain do:

+
ml show buildenv
+
+

To use the environment variables, load buildenv:

+
ml buildenv
+
+

Using the environment variable (prefaced with $) for linking is highly recommended!

+
+

Example

+

Linking with LAPACK (gcc, C program).

+
gcc -o PROGRAM PROGRAM.c -lflexiblas -lgfortran
+
+

OR use the environment variable $LIBLAPACK:

+
gcc -o PROGRAM PROGRAM.c $LIBLAPACK
+
+
+
+

Note

+

You can see a list of all the libraries on Kebnekaise (June 2024) here: https://docs.hpc2n.umu.se/documentation/compiling/#libraries.

+
+
+

Keypoints

+
    +
  • In order to compile a program, you must first load a “compiler toolchain” module
    • +
  • Kebnekaise has both GCC and Intel compilers installed
    • +
  • The GCC compilers are:
      +
    • gfortran
      • +
    • gcc
      • +
    • g++
      • +
    +
    • +
  • The Intel compilers are:
      +
    • ifort
      • +
    • icc
      • +
    • icpc
      • +
    +
    • +
  • Compiling MPI programs can be done after loading a compiler toolchains which contains MPI libraries
    • +
  • The easiest way to figure out how to link with a library is to use ml show buildenv after loading a compiler toolchain
    • +
+
+ +
+
+ +
+
+ +
+ +
+ +
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Using flexbox, we + * achieve it in such a way that it will look like the following: + * + * [No repo_name] + * Next >> // On the first page + * << Previous Next >> // On all subsequent pages + * + * [With repo_name] + * Next >> // On the first page + * << Previous Next >> // On all subsequent pages + * + * https://github.com/mkdocs/mkdocs/issues/2012 + */ +.rst-versions .rst-current-version { + padding: 0 12px; + display: flex; + font-size: initial; + justify-content: space-between; + align-items: center; + line-height: 40px; +} + +/* + * Please note that this amendment also involves removing certain inline-styles + * from the file ./mkdocs/themes/readthedocs/versions.html. + * + * https://github.com/mkdocs/mkdocs/issues/2012 + */ +.rst-current-version span { + flex: 1; + text-align: center; +} diff --git a/filesystem/index.html b/filesystem/index.html new file mode 100644 index 00000000..82fb9f3d --- /dev/null +++ b/filesystem/index.html @@ -0,0 +1,303 @@ + + + + + + + + The File System - Introduction to Kebnekaise + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
    +
    • +
  • The File System
    • +
  • +
    • +
+
+
+
+
+ +

The File System

+
+

Objectives

+
    +
  • Learn about the file system on Kebnekaise
    • +
  • Find the project storage for this course and create your own subdirectory
    • +
+
+

Overview

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Project storage$HOME/scratch
Recommended for
batch jobs		YesNo (size)Yes
Backed upNoYesNo
Accessible by
batch system		YesYesYes (node only)
PerformanceHighHighMedium
Default readabilityGroup onlyOwnerOwner
Permissions
management		chmod, chgrp, ACLchmod, chgrp, ACLN/A for batch jobs
NotesStorage your group get
allocated through the
storage projects		Your home-directoryPer node
+

$HOME

+

This is your home-directory (pointed to by the $HOME variable). It has a quota limit of 25GB per default. Your home directory is backed up regularly.

+
+

Note

+

Since the home directory is quite small, it should not be used for most production jobs. These should instead be run from project storage directories.

+
+

To find the path to your home directory, either run pwd just after logging in, or do the following:

+
b-an01 [~/store]$ cd
+b-an01 [~]$ pwd
+/home/u/username
+b-an01 [~]$
+
+

Project storage

+

Project storage is where a project’s members have the majority of their storage. It is applied for through SUPR, as a storage project. While storage projects needs to be applied for separately, they are usually linked to a compute project.

+

This is where you should keep your data and run your batch jobs from. It offers high performance when accessed from the nodes making it suitable for storage that are to be accessed from parallel jobs, and your home directory (usually) has too little space.

+

Project storage is located below /proj/nobackup/ in the directory name selected during the creation of the proposal.

+
+

Note

+

The project storage is not intended for permanent storage and there is NO BACKUP of /proj/nobackup.

+
+

Using project storage

+
    +
  • If you have a storage project, you should use that to run your jobs.
    • +
  • You (your PI) will either choose a directory name when you/they apply for the storage project or get the project id as default name.
    • +
  • The location of the storage project in the file system is /proj/nobackup/NAME-YOU-PICKED
    • +
  • Since the storage project is shared between all users of the project, you should go to that directory and create a subdirectory for your things, which you will then be using.- For this course the storage is in + 
    /proj/nobackup/intro-hpc2n
+
    • +
+
+

Exercise

+

Go to the course project storage and create a subdirectory for yourself.

+
+

Now is a good time to prepare the course material and download the exercises. The easiest way to do so is by cloning the whole intro-course repository from GitHub.

+
+

Exercise

+
    +
  1. Go to the subdirectory you created under /proj/nobackup/intro-hpc2n
    2. +
  2. Clone the repository for the course: git clone https://github.com/hpc2n/intro-course.git
    3. +
+

You will get a directory called intro-course. Below it you will find a directory called “exercises” where the majority of the exercises for the batch system section is located.

+
+

Quota

+

The size of the storage depends on the allocation. There are small, medium, and large storage projects, each with their own requirements. You can read about this on SUPR. The quota limits are specific for the project as such, there are no user level quotas on that space.

+

/scratch

+

Our recommendation is that you use the project storage instead of /scratch when working on Compute nodes or Login nodes.

+

On the computers at HPC2N there is a directory called /scratch. It is a small local area split between the users using the node and it can be used for saving (temporary) files you create or need during your computations. Please do not save files in /scratch you don’t need when not running jobs on the machine, and please make sure your job removes any temporary files it creates.

+
+

Note

+

When anybody need more space than available on /scratch, we will remove the oldest/largest files without any notices.

+
+

More information about the file system, as well as archiving and compressing files, at the HPC2N documentation about File Systems.

+
+

Keypoints

+
    +
  • When you login to Kebnekaise, you will end up in your home-directory.
    • +
  • Your home-directory is in /home/u/username and is pointed to by the environment variable $HOME.
    • +
  • Your project storage is located in /proj/nobackup/NAME-YOU-PICKED
      +
    • For this course it is /proj/nobackup/intro-hpc2n.
      • +
    • The project storage is NOT backed up.
      • +
    +
    • +
  • You should run the batch jobs from your project storage.
    • +
+
+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
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/dev/null +++ b/images/memory.svg @@ -0,0 +1,363 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + Distributed memory + Shared memory + + + supercomputer + high-end workstation + small computing cluster + compute node + laptop + GPU(s) + + + memory + performance + + + + diff --git a/images/mid_sweden_university.png b/images/mid_sweden_university.png new file mode 100644 index 00000000..fd33e0f4 Binary files /dev/null and b/images/mid_sweden_university.png differ diff --git a/images/ml-show-buildenv-v2.png b/images/ml-show-buildenv-v2.png new file mode 100644 index 00000000..925c5405 Binary files /dev/null and b/images/ml-show-buildenv-v2.png differ diff --git a/images/naiss.png b/images/naiss.png new file mode 100644 index 00000000..59b7c673 Binary files /dev/null and b/images/naiss.png differ diff --git a/images/nano.png b/images/nano.png new file mode 100644 index 00000000..f6218c2b Binary files /dev/null and b/images/nano.png 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+ + + + + + + + + + + + batch + + + + + + + + Results + + + Faster process + - + + + Big data? + + + Assuming this code gives + you the correct results + + + CPU? + + + GPU? + + + Yes + + + No + + \ No newline at end of file diff --git a/images/permissions.png b/images/permissions.png new file mode 100644 index 00000000..83a1787b Binary files /dev/null and b/images/permissions.png differ diff --git a/images/prace.png b/images/prace.png new file mode 100644 index 00000000..5c191f07 Binary files /dev/null and b/images/prace.png differ diff --git a/images/putty-kebnekaise.png b/images/putty-kebnekaise.png new file mode 100644 index 00000000..0af8d9d2 Binary files /dev/null and b/images/putty-kebnekaise.png differ diff --git a/images/putty-login-kebnekaise.png b/images/putty-login-kebnekaise.png new file mode 100644 index 00000000..42d17391 Binary files /dev/null and b/images/putty-login-kebnekaise.png differ diff --git a/images/skills4eosc.png b/images/skills4eosc.png new file mode 100644 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+ + +
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    +
    • +
  • Home
    • +
  • +
    • +
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+ +

Welcome to the course: Introduction to Kebnekaise

+
+

This material

+

Here you will find the content of the workshop “Introduction to Kebnekaise”.

+

You can download the markdown files for the presentation as well as the exercises from https://github.com/hpc2n/intro-course

+
    +
  • Click the gren “Code” button
      +
    • Either copy the url for the repo under HTTPS and do git clone https://github.com/hpc2n/intro-course.git in a terminal window
      • +
    • OR pick “Download zip” to get a zip file with the content.
      • +
    +
    • +
+

Some useful links:

+ +
+
+

Prerequisites

+
    +
  • Basic knowledge about Linux (if you need a refresher, you could take the course “Introduction to Linux” which runs immediately before this course. Info and registration here: https://www.hpc2n.umu.se/events/courses/2024/fall/intro-linux.
    • +
  • An account at SUPR and at HPC2N. You should have already been contacted about getting these if you did not have them already.
    • +
+
+
+

Content

+
    +
  • This course aims to give a brief, but comprehensive introduction to Kebnekaise.
    • +
  • You will learn about
    • +
  • HPC2N, HPC, and Kebnekaise hardware
    • +
  • How to use our systems:
      +
    • Logging in & editors
      • +
    • The File System
      • +
    • The Module System
      • +
    • Compiling and linking
      • +
    • The Batch System
      • +
    +
    • +
  • Simple examples (batch system)
    • +
  • +

    Application examples (batch system)

    +
    • +
  • +

    This course will consist of lectures and type-alongs, as well as a few exercises where you get to try out what you have just learned.

    +
    • +
+
+

Instructors

+
    +
  • Birgitte Brydsö, HPC2N
    • +
  • Pedro Ojeda-May, HPC2N
    • +
+

Preliminary schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeTopicActivity
11:15Welcome+Syllabus
11:20Introduction to Kebnekaise and HPC2NLecture
11:45Projects and accountsLecture
11:50Logging in & editorsLecture+exercise
12:05The File SystemLecture+code along
12:15LUNCH BREAK
13:15The Module SystemLecture+code along+exercise
13:35CompilingLecture+code along+exercise
13:50The Batch SystemLecture+code along
14:10Simple ExamplesLecture+exercises
14:45COFFEE BREAK
15:00Application ExamplesLecture+code along+exercises
16:40Questions+Summary
17:00END OF COURSE
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+ +
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+ +
+ +
+ +
+ + + + + Next » + + +
+ + + + + + + + + + + + + diff --git a/intro/index.html b/intro/index.html new file mode 100644 index 00000000..f2899778 --- /dev/null +++ b/intro/index.html @@ -0,0 +1,765 @@ + + + + + + + + Introduction to Kebnekaise and HPC2N - Introduction to Kebnekaise + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
    +
    • +
  • Introduction to Kebnekaise and HPC2N
    • +
  • +
    • +
+
+
+
+
+ +

Introduction to HPC2N, Kebnekaise and HPC

+ + +

umu-logo +naiss-logo +hpc2n-logo

+ +

HPC2N

+
+

Note

+

High Performance Computing Center North (HPC2N) is

+
    +
  • a competence center for Scientific and Parallel Computing
    • +
  • part of National Academic Infrastructure for Super­computing in Sweden (NAISS)
    • +
+
+

HPC2N provides state-of-the-art resources and expertise:

+
    +
  • Scalable and parallel HPC
    • +
  • Large-scale storage facilities (Project storage (Lustre), SweStore, Tape)
    • +
  • Grid and cloud computing (WLCG NT1, Swedish Science Cloud)
    • +
  • National Data Science Node in ”Epidemiology and Biology of Infections” (DDLS)
    • +
  • Software for e-Science applications
    • +
  • All levels of user support
      +
    • Primary, advanced, dedicated
      • +
    • Application Experts (AEs)
      • +
    +
    • +
+
+

Primary objective

+

To raise the national and local level of HPC competence and transfer HPC knowledge and technology to new users in academia and industry.

+
+

HPC2N partners

+

HPC2N is hosted by:

+

umu-logo

+



+

Partners:

+

irf-logo +ltu-logo +miun-logo +slu-logo

+


+

HPC2N funding and collaborations

+

Funded mainly by Umeå University, with contributions from the other HPC2N partners.

+

Involved in several projects and collaborations:

+

essence-logo +prace-logo +algoryx-logo

+

WLCG-logo +eosc-nordic-logo +eiscat-logo

+

scilifelab-logo +skills4eosc

+


+

HPC2N training and other services

+
    +
  • User support (primary, advanced, dedicated)
      +
    • Research group meetings @ UmU
      • +
    • Also at the partner sites
      • +
    • Online “HPC2N fika”
      • +
    +
    • +
  • User training and education program
      +
    • 0.5 – 5 days; ready-to-run exercises
      • +
    • intro courses: our system, Linux, R, Python, Julia, Matlab, Git
      • +
    • intermediate courses
      • +
    • +
        +
      • Parallel programming and tools (OpenMP, MPI, debugging, perf. analyzers, Matlab, R, MD simulation, ML, GPU, …)
        • +
      +
      • +
    +
    • +
  • Courses this fall
      +
    • Introduction to Linux, 16 September 2024
      • +
    • Introduction to HPC2N and Kebnekaise, 16 September 2024
      • +
    • Basic Singularity, 16 October 2024
      • +
    • Introduction to running R, Python, Julia, and Matlab in HPC, 22-25 October 2024
      • +
    • Introduction to Git, 25-29 November 2024
      • +
    • Using Python in an HPC environment, 5-6 December 2024
      • +
    • Updated list: https://www.hpc2n.umu.se/events/courses
      • +
    +
    • +
  • Workshops and seminars
    • +
  • NGSSC / SeSE & university courses
    • +
+

HPC2N personnel

+

Management:

+
    +
  • Paolo Bientinesi, director
    • +
  • Björn Torkelsson, deputy director
    • +
  • Lena Hellman, administrator
    • +
+

Application experts:

+
    +
  • Jerry Eriksson
    • +
  • Pedro Ojeda May
    • +
  • Birgitte Brydsö
    • +
  • Åke Sandgren
    • +
+

Others:

+
    +
  • Mikael Rännar (WLCG coord)
    • +
  • Research Engineers under DDLS, HPC2N/SciLifeLab
      +
    • Paul Dulaud, System Developer, IT
      • +
    • Abdullah Aziz, Data Engineer
      • +
    • Nalina Hamsaiyni Venkatesh, Data Steward
      • +
    +
    • +
+

System and support:

+
    +
  • Erik Andersson
    • +
  • Birgitte Brydsö
    • +
  • Niklas Edmundsson (Tape coord)
    • +
  • My Karlsson
    • +
  • Roger Oscarsson
    • +
  • Åke Sandgren
    • +
  • Mattias Wadenstein (NeIC, Tier1)
    • +
+

HPC2N application experts

+
    +
  • HPC2N provides advanced and dedicated support in the form of Application Experts (AEs):
      +
    • Jerry Eriksson: Profiling, Machine learning (DNN), MPI, OpenMP, OpenACC
      • +
    • Pedro Ojeda May: Molecular dynamics, Profiling, QM/MM, NAMD, Amber, Gromacs, GAUSSIAN, R, Python
      • +
    • Åke Sandgren: General high level programming assistance, VASP, Gromacs, Amber
      • +
    • Birgitte Brydsö: General HPC, R, Python
      • +
    +
    • +
  • Contact through regular support
    • +
+

HPC2N users by discipline

+
    +
  • Users from several scientific disciplines:
      +
    • Biosciences and medicine
      • +
    • Chemistry
      • +
    • Computing science
      • +
    • Engineering
      • +
    • Materials science
      • +
    • Mathematics and statistics
      • +
    • Physics including space physics
      • +
    • ML, DL, and other AI
      • +
    +
    • +
+

HPC2N users by discipline, largest users

+
    +
  • Users from several scientific disciplines:
      +
    • Biosciences and medicine
      • +
    • Chemistry
      • +
    • Computing science
      • +
    • Engineering
      • +
    • Materials science
      • +
    • Mathematics and statistics
      • +
    • Physics including space physics
      • +
    • Machine learning and artificial intelligence (several new projects)
      • +
    +
    • +
+

HPC2N users by software

+

software-users

+

Kebnekaise

+

The current supercomputer at HPC2N. It is a very heterogeneous system.

+
    +
  • Named after a massif (contains some of Sweden’s highest mountain peaks)
    • +
  • +

    Kebnekaise was

    +
      +
    • delivered by Lenovo and
      • +
    • installed during the summer 2016
      • +
    • Opened up for general availability on November 7, 2016
      • +
    • In 2018, Kebnekaise was extended with
        +
      • 52 Intel Xeon Gold 6132 (Skylake) nodes, as well as
        • +
      • 10 NVidian V100 (Volta) GPU nodes
        • +
      +
      • +
    • In 2023, Kebnekaise was extended with
        +
      • 2 dual NVIDIA A100 GPU nodes
        • +
      • one many-core AMD Zen3 CPU node
        • +
      +
      • +
    +
    • +
  • +

    In 2024 Kebnekaise was extended with

    +
      +
    • 2 Dual socket GPU-nodes: Lenovo ThinkSystem SR675 V3
        +
      • 2 x AMD EPYC 9454 48C 290W 2.75GHz Processor
        • +
      • 768GB [24x 32GB TruDDR5 4800MHz RDIMM-A]
        • +
      • 1 x 3.84TB Read Intensive NVMe PCIe 4.0 x4 HS SSD
        • +
      • 1 x NVIDIA H100 SXM5 700W 80G HBM3 GPU Board
        • +
      +
      • +
    • 10 dual-socket GPU-nodes: ThinkSystem SR665 V3
        +
      • 2 x AMD EPYC 9254 24C 200W 2.9GHz Processor
        • +
      • 384GB [24x 16GB TruDDR5 4800MHz RDIMM-A]
        • +
      • 1 x 1.92TB Read Intensive NVMe PCIe 5.0 x4 HS SSD
        • +
      • 2 x NVIDIA L40S 48GB PCIe Gen4 Passive GPU
        • +
      +
      • +
    • 8 dual-socket CPU only: ThinkSystem SR645 V3
        +
      • 2 x AMD EPYC 9754 128C 360W 2.25GHz Processor
        • +
      • 768GB [24x 32GB TruDDR5 4800MHz RDIMM-A]
        • +
      • 1 x 1 3.84TB Read Intensive NVMe PCIe 4.0 x4 HS SSD
        • +
      +
      • +
    +
    • +
+

Kebnekaise will be continuosly upgraded, as old hardware gets retired.

+

Current hardware in Kebnekaise

+

Kebnekaise have CPU-only, GPU enabled and large memory nodes.

+

The CPU-only nodes are:

+
    +
  • 2 x 14 core Intel broadwell
      +
    • 4460 MB memory / core
      • +
    • 48 nodes
      • +
    • Total of 41.6 TFlops/s
      • +
    +
    • +
  • 2 x 14 core Intel skylake
      +
    • 6785 MB memory / core
      • +
    • 52 nodes
      • +
    • Total of 87 TFlops/s
      • +
    +
    • +
  • 2 x 64 core AMD zen3
      +
    • 8020 MB / core
      • +
    • 1 node
      • +
    • Total of 11 TFlops/s
      • +
    +
    • +
  • 2 x 128 core AMD zen4
      +
    • 2516 MB / core
      • +
    • 8 nodes
      • +
    • Total of 216 TFlops/s
      • +
    +
    • +
+

The GPU enabled nodes are:

+
    +
  • 2 x 14 core Intel broadwell
      +
    • 9000 MB memory / core
      • +
    • 2 x Nvidia A40
      • +
    • 4 nodes
      • +
    • Total of 83 TFlops/s
      • +
    +
    • +
  • 2 x 14 core Intel skylake
      +
    • 6785 MB memory / core
      • +
    • 2 x Nvidia V100
      • +
    • 10 nodes
      • +
    • Total of 75 TFlops/s
      • +
    +
    • +
  • 2 x 24 core AMD zen3
      +
    • 10600 MB / core
      • +
    • 2 x Nvidia A100
      • +
    • 2 nodes
      • +
    +
    • +
  • 2 x 24 core AMD zen3
      +
    • 10600 MB / core
      • +
    • 2 x AMD MI100
      • +
    • 1 node
      • +
    +
    • +
  • 2 x 24 core AMD zen4
      +
    • 6630 MB / core
      • +
    • 2 x Nvidia A6000
      • +
    • 1 node
      • +
    +
    • +
  • 2 x 24 core AMD zen4
      +
    • 6630 MB / core
      • +
    • 2 x Nvidia L40s
      • +
    • 10 nodes
      • +
    +
    • +
  • 2 x 48 core AMD zen4
      +
    • 6630 MB / core
      • +
    • 4 x Nvidia H100 SXM5
      • +
    • 2 nodes
      • +
    +
    • +
+

The large memory nodes are:

+
    +
  • 4 x 18 core Intel broadwell
      +
    • 41666 MB memory / core
      • +
    • 8 nodes
      • +
    • Total of 13.6 TFlops/s for all these nodes
      • +
    +
    • +
+

GPUs can have different types of cores:

+
    +
  • CUDA cores: General-purpose cores for a variety of parallel computing tasks. Not as efficicent as specizalized cores. CUDA cores is only on NVidia. The (mostly) equivalent is called stream processors on AMD.
    • +
  • Tensor cores: Made for matrix multiplications. Good for deep learning and AI workloads involving large matrix operations. Can be used for general-purpose as well, but less efficient for this. Tensor cores is the NVidia name. AMD has a somewhat equivalent core type called matrix cores.
    • +
  • RT (ray tracing) cores: Cores that are optimized for tasks involving ray tracing, like rendering images or video.
    • +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
GPU TypeCUDA cores / stream processorsTENSOR cores / matrix coresRT cores
A4010752336
V1005120640
A1006912432
MI1007680480
A600010752386
L40S18176568142
H10016896528
+

NOTE that just like you cannot really compare CPU cores directly (speed etc.) you also cannot just compare CUDA/TENSOR/RT etc. cores directly (more efficient design, faster, etc.)

+

Kebnekaise - HPC2N storage

+

Basically four types of storage are available at HPC2N:

+
    +
  • Home directory
      +
    • /home/X/Xyz, $HOME, ~
      • +
    • 25 GB, user owned
      • +
    +
    • +
  • Project storage
      +
    • /proj/nobackup/abc
      • +
    • Shared among project members
      • +
    +
    • +
  • Local scratch space
      +
    • $SNIC_TMP
      • +
    • SSD (170GB), per job, per node, “volatile”
      • +
    +
    • +
  • Tape Storage
      +
    • Backup
      • +
    • Long term storage
      • +
    +
    • +
+

Also

+
    +
  • SweStore — disk based (dCache)
      +
    • Research Data Storage Infrastructure, for active research data and operated by NAISS, WLCG
      • +
    +
    • +
+

Kebnekaise - projects

+
+

Compute projects

+

To use Kebnekaise, you must be a member of a compute project.

+
    +
  • A compute project has a certain number of core hours allocated for it per month
    • +
  • A regular CPU core cost 1 core hour per hour, other resources (e.g., GPUs) cost more
    • +
  • Not a hard limit but projects that go over the allocation get lower priority
    • +
+
+

A compute project contains a certain amount of storage. If more storage is required, you must be a member of a storage project.

+
+

Note

+

As Kebnekaise is a local cluster, you need to be affiliated with UmU, IRF, SLU, Miun, or LTU to use it.

+
+

Projects are applied for through SUPR (https://supr.naiss.se).

+

I will cover more details in a later section, where we go more into detail about HPC2N and Kebnekaise.

+

HPC

+
+

What is HPC?

+

High Performance Computing (definition)

+

“High Performance Computing most generally refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve large problems in science, engineering, or business.”

+

From: https://insidehpc.com/hpc-basic-training/what-is-hpc/

+
+

High Performance Computing - opening the definition

+

Aggregating computing power

+
    +
  • 147 nodes totalling 6808 CPU cores and 64 GPUs (totalling 751616 CUDA cores, 33472 TENSOR cores + 960 matrix cores, 284 RT cores)
      +
    • Compared to 4-8 cores in a common modern laptop
      • +
    +
    • +
+

Higher performance

+
    +
  • More than 527,000,000,000,000 arithmetical operations per second (527 trillion (billion)) in the CPU cores
      +
    • Compared to 200,000,000,000 Flops in a modern laptop (200 billion (milliard)
      • +
    +
    • +
+

Solve large problems

+
    +
  • When does a problem become large enough for HPC?
    • +
  • Are there other reasons for using HPC resources? (Memory, software, support, etc.)
    • +
+

High Performance Computing - large problems

+

A problem can be large for two main reasons:

+
    +
  • Execution time: The time required to form a solution to the problem is very long
    • +
  • Memory / storage use: The solution of the problem requires a lot of memory and/or storage
    • +
+

The former can be remedied by increasing the performance

+
    +
  • More cores, more nodes, GPUs, …
    • +
+

The latter by adding more memory / storage

+
    +
  • More memory per node (including large memory nodes), more nodes, …
      +
    • Kebnekaise: 128GB - 192GB, 384GB, 512GB, 768GB, 3TB
      • +
    +
    • +
  • Large storage solutions, …
    • +
+

High Performance Computing - what counts as HPC

+

hpc

+



+

High Performance Computing - other reasons

+
    +
  • Specialized (expensive) hardware
      +
    • GPUs, including those optimized for AI
        +
      • Kebnekaise has V100, A100, A40, MI100, A6000, L40S, H100
        • +
      +
      • +
    • High-end CPUs (AVX-512 etc) and ECC memory
      • +
    +
    • +
  • Software
      +
    • HPC2N holds licenses for several softwares
      • +
    • Software is pre-configured and ready-to-use
      • +
    +
    • +
  • Support and documentation
    • +
+

High Performance Computing - memory models

+

Two memory models are relevant for HPC:

+
    +
  • Shared memory: Single memory space for all data. +sm +

      +
    • Everyone can access the same data
      • +
    • Straightforward to use
      • +
    +
    • +
  • Distributed memory: Multiple distinct memory spaces. +dm +

      +
    • Everyone has direct access only to the local data
      • +
    • Requires communication
      • +
    +
    • +
+

memory +

+

High Performance Computing - programming models

+

The programming model changes when we aim for extra performance and/or memory:

+
    +
  • Single-core: Matlab, Python, C, Fortran, …
      +
    • Single stream of operations
      • +
    +
    • +
  • Multi-core: Vectorized Matlab, pthreads, OpenMP
      +
    • Multiple streams of operations
      • +
    • Work distribution, coordination (synchronization, etc), …
      • +
    +
    • +
  • Distributed memory: MPI, …
      +
    • Multiple streams of operations
      • +
    • Work distribution, coordination (synchronization, etc), …
      • +
    • Data distribution and communication
      • +
    +
    • +
  • GPUs: CUDA, OpenCL, OpenACC, OpenMP, …
      +
    • Many lightweight streams of operations
      • +
    • Work distribution, coordination (synchronization, etc), …
      • +
    • Data distribution across memory spaces and movement
      • +
    +
    • +
+

High Performance Computing - software

+

Complexity grows when we aim for extra performance and/or memory/storage:

+
    +
  1. Single-core: LAPACK, …
      +
    • Load correct toolchain etc
      • +
    +
    2. +
  2. Multi-core: LAPACK + parallel BLAS, …
      +
    • Load correct toolchain etc
      • +
    • Allocate correct number of cores, configure software to use correct number of cores, …
      • +
    +
    3. +
  3. Distributed memory}: ScaLAPACK, …
      +
    • Load correct toolchain etc
      • +
    • Allocate correct number of nodes and cores, configure software to use correct number of nodes and cores, …
      • +
    • Data distribution, storage, …
      • +
    +
    4. +
  4. GPUs: MAGMA, TensorFlow, …
      +
    • Load correct toolchain etc
      • +
    • Allocate correct number of cores and GPUs, configure software to use correct number of cores and GPUs, …
      • +
    +
    5. +
+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
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+ + +
+ +
+
+
    +
    • +
  • Logging in
    • +
  • +
    • +
+
+
+
+
+ +

Logging in

+

When you have your account, you can login to Kebnekaise. This can be done with any number of SSH clients or with ThinLinc (the easiest option if you need a graphical interface).

+
+

Objectives

+
    +
  • Login to Kebnekaise, either with ThinLinc or your SSH client of choice.
    • +
+
+

Kebnekaise login servers

+
+

Note

+
    +
  • The main login node of Kebnekaise: kebnekaise.hpc2n.umu.se
    • +
  • ThinLinc login node: kebnekaise-tl.hpc2n.umu.se
      +
    • ThinLinc through a browser (less features): https://kebnekaise-tl.hpc2n.umu.se:300/
      • +
    +
    • +
+
+

In addition, there is a login node for the AMD-based nodes. We will talk more about this later: kebnekaise-amd.hpc2n.umu.se. For ThinLinc access: kebnekaise-amd-tl.hpc2n.umu.se

+
+

ThinLinc is recommended for this course

+

ThinLinc: a cross-platform remote desktop server from Cendio AB. Especially useful when you need software with a graphical interface.

+

This is what we recommend you use for this course, unless you have a preferred SSH client.

+
+

Using ThinLinc

+
    +
  • Download the client from https://www.cendio.com/thinlinc/download.
      +
    • Install it.
        +
      • Windows: Run the downloaded .exe file to install.
        • +
      • macOS: Information on the ThinLinc macOS info page.
        • +
      • Linux Ubuntu: Download the .deb file. Run sudo dpkg -i PATH-TO-FILE/FILE-YOU-DOWNLOADED.deb
        • +
      +
      • +
    +
    • +
  • Start the client.
    • +
  • Enter the name of the server: kebnekaise-tl.hpc2n.umu.se. Enter your username. +
    +thinlinc +
    • +
  • Go to “Options” \(->\) “Security”. Check that authentication method is set to password.
    • +
  • Go to “Options” \(->\) “Screen”. Uncheck “Full screen mode”.
    • +
  • Enter your HPC2N password. Click “Connect”
    • +
  • Click “Continue” when you are being told that the server’s host key is not in the registry. Wait for the ThinLinc desktop to open.
    • +
+

Password

+

You get your first, temporary HPC2N password from this page: HPC2N passwords.

+

That page can also be used to reset your HPC2N password if you have forgotten it.

+

Note that you are authenticating through SUPR, using that service’s login credentials!

+
+

Warning

+

The HPC2N password and the SUPR password are separate! The HPC2N password and your university/department password are also separate!

+
+
+

Exercise

+

Login to Kebnekaise.

+
    +
  • If you are using ThinLinc, first install the ThinLinc client. If you are using another SSH client, install it first if you have not already done so.
    • +
+
+

Change password

+
+

Exercise: Change your password after first login

+

ONLY do this if you have logged in for the first time/is still using the termporary password you got from the HPC2N password reset service!

+

Changing password is done using the passwd command:

+
passwd
+
+

Use a good password that combines letters of different case. Do not use dictionary words. Avoid using the same password that you also use in other places.

+

It will first ask for your current password. Type in that and press enter. Then type in the new password, enter, and repeat. You have changed the password.

+
+

File transfers

+

We are not going to transfer any files as part of this course, but you may have to do so as part of your workflow when using Kebnekaise (or another HPC centre) for your research.

+

This section will only talk briefly about file transfers. You can find more information and examples on HPC2N’s File transfer documentation.

+

Linux, OS X

+

scp

+

SCP (Secure CoPy) is a simple way of transferring files between two machines that use the SSH (Secure SHell) protocol. You may use SCP to connect to any system where you have SSH (log-in) access.

+

These examples show how to use scp from the command-line. Graphical programs exists for doing scp transfer.

+

The command-lone scp program should already be installed.

+
+

Remote to local

+

Transfer a file from Kebnekaise to your local system, while on your local system

+
scp username@kebnekaise.hpc2n.umu.se:file .
+
+
+
+

Local to remote

+

Transfer a local file to Kebnekaise, while on your local system

+
scp file username@kebnekaise.hpc2n.umu.se:file
+
+
+
+

Recursive directory copy from a local system to a remote system

+

The directory sourcedirectory is here copied as a subdirectory to somedir

+
scp -r sourcedirectory/ username@kebnekaise.hpc2n.umu.se:somedir/
+
+
+

sftp

+

SFTP (SSH File Transfer Protocol or sometimes called Secure File Transfer Protocol) is a network protocol that provides file transfer over a reliable data stream.

+

SFTP is a command -line program on most Unix, Linux, and Mac OS X systems. It is also available as a protocol choice in some graphical file transfer programs.

+
+

Example: From a local system to a remote system

+
enterprise-d [~]$ sftp user@kebnekaise.hpc2n.umu.se
+Connecting to kebnekaise.hpc2n.umu.se...
+user@kebnekaise.hpc2n.umu.se's password:
+sftp> put file.c C/file.c
+Uploading file.c to /home/u/user/C/file.c
+file.c                          100%    1    0.0KB/s   00:00
+sftp> put -P irf.png pic/
+Uploading irf.png to /home/u/user/pic/irf.png
+irf.png                         100% 2100    2.1KB/s   00:00
+sftp>
+
+
+

Windows

+

Here you need to download a client: WinSCP, FileZilla (sftp), PSCP/PSFTP, …

+

You can transfer with sftp or scp.

+

There is documentation in HPC2N’s documentation pages for Windows file transfers.

+

Editors

+

Since the editors on a Linux system are different to those you may be familiar with from Windows or macOS, here follows a short overview.

+

There are command-line editors and graphical editors. If you are connecting with a regular SSH client, it will be simplest to use a command-line editor. If you are using ThinLinc, you can use command-line editors or graphical editors as you want.

+

Command-line

+

These are all good editors for using on the command line:

+ +

They are all installed on Kebnekaise.

+

Of these, vi/vim as well as emacs are probably the most powerful, though the latter is better in a GUI environment. The easiest editor to use if you are not familiar with any of them is nano.

+
+

Nano

+
    +
  1. Starting “nano”: Type nano FILENAME on the command line and press Enter. FILENAME is whatever you want to call your file.
    2. +
  2. If FILENAME is a file that already exists, nano will open the file. If it dows not exist, it will be created.
    3. +
  3. You now get an editor that looks like this: +nano editor
    4. +
  4. First thing to notice is that many of the commands are listed at the bottom.
    5. +
  5. The ^ before the letter-commands means you should press CTRL and then the letter (while keeping CTRL down).
    6. +
  6. Your prompt is in the editor window itself, and you can just type (or copy and paste) the content you want in your file.
    7. +
  7. When you want to exit (and possibly save), you press CTRL and then x while holding CTRL down (this is written CTRL-x or ^x). nano will ask you if you want to save the content of the buffer to the file. After that it will exit.
    8. +
+
+

There is a manual for nano here.

+

GUI

+

If you are connecting with ThinLinc, you will be presented with a graphical user interface (GUI).

+

From there you can either

+
    +
  • open a terminal window/shell (Applications -> System Tools -> MATE Terminal)
    • +
  • or you can choose editors from the menu by going to Applications -> Accessories. This gives several editor options, of which these have a graphical interface: +
    • +
+

If you are not familiar with any of these, a good recommendation would be to use Text Editor/gedit.

+
+

Text Editor/gedit

+
    +
  1. Starting “gedit”:
      +
    • From the menu, choose Applications -> Accessories -> Text Editor.
      • +
    +
    2. +
  2. You then get a window that looks like this: +gedit editor
    3. +
  3. You can open files by clicking “Open” in the top menu.
    4. +
  4. Clicking the small file icon with a green plus will create a new document.
    5. +
  5. Save by clicking “Save” in the menu.
    6. +
  6. The menu on the top right (the three horizontal lines) gives you several other options, including “Find” and “Find and Replace”.
    7. +
+
+
+

Keypoints

+
    +
  • You can login with ThinLinc or another SSH client
    • +
  • ThinLinc is easiest if you need a GUI
    • +
  • There are several command-line editors: vi/vim, nano, emacs, …
    • +
  • And several GUI editors, which works best when using ThinLinc: gedit, pluma, atom, emacs (gui), nedit, …
    • +
+
+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + + + diff --git a/modules/index.html b/modules/index.html new file mode 100644 index 00000000..c6d25730 --- /dev/null +++ b/modules/index.html @@ -0,0 +1,489 @@ + + + + + + + + The Module System - Introduction to Kebnekaise + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
    +
    • +
  • The Module System
    • +
  • +
    • +
+
+
+
+
+ +

The Module System (Lmod)

+
+

Objectives

+
    +
  • Learn the basics of the module system which is used to access most of the software on Kebnekaise
    • +
  • Try some of the most used commands for the module system:
      +
    • find/list software modules
      • +
    • load/unload software modules
      • +
    +
    • +
  • Learn about compiler toolchains
    • +
+
+

Most programs are accessed by first loading them as a ‘module’.

+

Modules are:

+
    +
  • used to set up your environment (paths to executables, libraries, etc.) for using a particular (set of) software package(s)
    • +
  • a tool to help users manage their Unix/Linux shell environment, allowing groups of related environment-variable settings to be made or removed dynamically
    • +
  • allows having multiple versions of a program or package available by just loading the proper module
    • +
  • are installed in a hierarchial layout. This means that some modules are only available after loading a specific compiler and/or MPI version.
    • +
+

Useful commands (Lmod)

+
    +
  • See which modules exists:
      +
    • module spider or ml spider
      • +
    +
    • +
  • See which versions exist of a specific module:
      +
    • module spider MODULE or ml spider MODULE
      • +
    +
    • +
  • See prerequisites and how to load a specfic version of a module:
      +
    • module spider MODULE/VERSION or ml spider MODULE/VERSION
      • +
    +
    • +
  • List modules depending only on what is currently loaded:
      +
    • module avail or ml av
      • +
    +
    • +
  • See which modules are currently loaded:
      +
    • module list or ml
      • +
    +
    • +
  • Loading a module:
      +
    • module load MODULE or ml MODULE
      • +
    +
    • +
  • Loading a specific version of a module:
      +
    • module load MODULE/VERSION or ml MODULE/VERSION
      • +
    +
    • +
  • Unload a module:
      +
    • module unload MODULE or ml -MODULE
      • +
    +
    • +
  • Get more information about a module:
      +
    • ml show MODULE or module show MODULE
      • +
    +
    • +
  • Unload all modules except the ‘sticky’ modules:
      +
    • module purge or ml purge
      • +
    +
    • +
+
+

Important!

+

Not all the modules (and versions) are the same on the skylake/broadwell nodes and the zen3/zen4 nodes.

+

The regular login node kebnekaise.hpc2n.umu.se has the modules available on skylake/broadwell nodes. (ThinLinc: kebnekaise-tl.hpc2n.umu.se)

+

In order to check if a module is available on the zen3/zen4 nodes, login to kebnekaise-amd.hpc2n.umu.se. (ThinLinc: kebnekaise-amd-tl.hpc2n.umu.se).

+
+
+

Hint

+

Code-along!

+
+
+Example: checking which versions exist of the module ‘Python’ on the regular login node +
b-an01 [~]$ ml spider Python
+
+---------------------------------------------------------------------------------------------------------
+  Python:
+---------------------------------------------------------------------------------------------------------
+    Description:
+      Python is a programming language that lets you work more quickly and integrate your systems more effectively.
+
+     Versions:
+        Python/2.7.15
+        Python/2.7.16
+        Python/2.7.18-bare
+        Python/2.7.18
+        Python/3.7.2
+        Python/3.7.4
+        Python/3.8.2
+        Python/3.8.6
+        Python/3.9.5-bare
+        Python/3.9.5
+        Python/3.9.6-bare
+        Python/3.9.6
+        Python/3.10.4-bare
+        Python/3.10.4
+        Python/3.10.8-bare
+        Python/3.10.8
+        Python/3.11.3
+        Python/3.11.5
+     Other possible modules matches:
+        Biopython  Boost.Python  Brotli-python  GitPython  IPython  Python-bundle-PyPI  flatbuffers-python  ...
+
+---------------------------------------------------------------------------------------------------------
+  To find other possible module matches execute:
+
+      $ module -r spider '.*Python.*'
+
+---------------------------------------------------------------------------------------------------------
+  For detailed information about a specific "Python" package (including how to load the modules) use the module's full name.
+  Note that names that have a trailing (E) are extensions provided by other modules.
+  For example:
+
+     $ module spider Python/3.11.5
+---------------------------------------------------------------------------------------------------------
+
+
+
+b-an01 [~]$ 
+
+
+
+Example: Check how to load a specific Python version (3.11.5 in this example) on the regular login node +
b-an01 [~]$ ml spider Python/3.11.5
+
+---------------------------------------------------------------------------------------------------------
+  Python: Python/3.11.5
+---------------------------------------------------------------------------------------------------------
+    Description:
+      Python is a programming language that lets you work more quickly and integrate your systems more effectively.
+
+    You will need to load all module(s) on any one of the lines below before the "Python/3.11.5" module is available to load.
+
+      GCCcore/13.2.0
+
+    This module provides the following extensions:
+
+       flit_core/3.9.0 (E), packaging/23.2 (E), pip/23.2.1 (E), setuptools-scm/8.0.4 (E), setuptools/68.2.2 (E), tomli/2.0.1 (E), typing_extensions/4.8.0 (E), wheel/0.41.2 (E)
+
+    Help:
+      Description
+      ===========
+      Python is a programming language that lets you work more quickly and integrate your systems more effectively.
+
+      More information
+      ================
+       - Homepage: https://python.org/
+
+
+      Included extensions
+      ===================
+      flit_core-3.9.0, packaging-23.2, pip-23.2.1, setuptools-68.2.2, setuptools-
+      scm-8.0.4, tomli-2.0.1, typing_extensions-4.8.0, wheel-0.41.2
+
+
+
+
+
+b-an01 [~]$ 
+
+
+
+Example: Load Python/3.11.5 and its prerequisite(s) (on the regular login node) +

Here we also show the loaded module before and after the load. For illustration, we use first ml and then module list:

+
b-an01 [~]$ ml
+
+Currently Loaded Modules:
+  1) snicenvironment (S)   2) systemdefault (S)
+
+ Where:
+   S:  Module is Sticky, requires --force to unload or purge
+
+
+
+b-an01 [~]$ module load GCCcore/13.2.0 Python/3.11.5
+b-an01 [~]$ module list
+
+Currently Loaded Modules:
+  1) snicenvironment (S)   4) zlib/1.2.13     7) ncurses/6.4      10) SQLite/3.43.1  13) OpenSSL/1.1
+  2) systemdefault   (S)   5) binutils/2.40   8) libreadline/8.2  11) XZ/5.4.4       14) Python/3.11.5
+  3) GCCcore/13.2.0        6) bzip2/1.0.8     9) Tcl/8.6.13       12) libffi/3.4.4
+
+  Where:
+   S:  Module is Sticky, requires --force to unload or purge
+
+
+
+b-an01 [~]$ 
+
+
+
+Example: Unloading the module Python/3.11.5 (on the regular login node) +

In this example we unload the module Python/3.11.5, but not the prerequisite GCCcore/13.2.0. We also look at the output of module list before and after.

+
b-an01 [~]$ module list
+
+Currently Loaded Modules:
+  1) snicenvironment (S)   4) zlib/1.2.13     7) ncurses/6.4      10) SQLite/3.43.1  13) OpenSSL/1.1
+  2) systemdefault   (S)   5) binutils/2.40   8) libreadline/8.2  11) XZ/5.4.4       14) Python/3.11.5
+  3) GCCcore/13.2.0        6) bzip2/1.0.8     9) Tcl/8.6.13       12) libffi/3.4.4
+
+  Where:
+   S:  Module is Sticky, requires --force to unload or purge
+
+
+b-an01 [~]$ ml unload Python/3.11.5
+b-an01 [~]$ module list
+
+Currently Loaded Modules:
+  1) snicenvironment (S)   2) systemdefault (S)   3) GCCcore/13.2.0
+
+  Where:
+   S:  Module is Sticky, requires --force to unload or purge
+
+
+
+b-an01 [~]$ 
+
+

As you can see, the prerequisite did not get unloaded. This is on purpose, because you may have other things loaded which uses the prerequisite.

+
+
+Example: unloading every module you have loaded, with module purge except the ‘sticky’ modules (some needed things for the environment) (on the regular login node) +

First we load some modules. Here Python 3.11.5, SciPy-bundle, and prerequisites for them. We also do module list after loading the modules and after using module purge.

+
b-an01 [~]$ ml GCC/13.2.0 
+b-an01 [~]$ ml Python/3.11.5 ml SciPy-bundle/2023.11 
+b-an01 [~]$ ml list
+
+Currently Loaded Modules:
+  1) snicenvironment (S)   7) bzip2/1.0.8      13) libffi/3.4.4     19) cffi/1.15.1
+  2) systemdefault   (S)   8) ncurses/6.4      14) OpenSSL/1.1      20) cryptography/41.0.5
+   3) GCCcore/13.2.0        9) libreadline/8.2  15) Python/3.11.5    21) virtualenv/20.24.6
+   4) zlib/1.2.13          10) Tcl/8.6.13       16) OpenBLAS/0.3.24  22) Python-bundle-PyPI/2023.10
+   5) binutils/2.40        11) SQLite/3.43.1    17) FlexiBLAS/3.3.1  23) pybind11/2.11.1
+   6) GCC/13.2.0           12) XZ/5.4.4         18) FFTW/3.3.10      24) SciPy-bundle/2023.11
+
+  Where:
+   S:  Module is Sticky, requires --force to unload or purge
+
+
+
+b-an01 [~]$ ml purge
+The following modules were not unloaded:
+  (Use "module --force purge" to unload all):
+
+  1) snicenvironment   2) systemdefault
+b-an01 [~]$ ml list
+
+Currently Loaded Modules:
+  1) snicenvironment (S)   2) systemdefault (S)
+
+  Where:
+   S:  Module is Sticky, requires --force to unload or purge
+
+
+
+b-an01 [~]$ 
+
+
+
+

Note

+
    +
  • You can do several module load on the same line. Or you can do them one at a time, as you want.
      +
    • The modules have to be loaded in order! You cannot list the prerequisite after the module that needs it!
      • +
    +
    • +
  • One advantage to loading modules one at a time is that you can then find compatible modules that depend on that version easily.
      +
    • Example: you have loaded GCC/13.2.0 and Python/3.11.5. You can now do ml av to see which versions of other modules you want to load, say SciPy-bundle, are compatible. If you know the name of the module you want, you can even start writing module load SciPy-bundle/ and press TAB - the system will then autocomplete to the compatible one(s).
      • +
    +
    • +
+
+
+

Exercise

+

Login to kebnekaise-amd (can be easily done with ssh kebnekaise-amd from a terminal window on the regular login node). Check if the versions of Python available differs from on the regular login node.

+
+

Compiler Toolchains

+

Compiler toolchains load bundles of software making up a complete environment for compiling/using a specific prebuilt software. Includes some/all of: compiler suite, MPI, BLAS, LAPACK, ScaLapack, FFTW, CUDA.

+

Some currently available toolchains (check ml av for versions and full, updated list):

+
    +
  • GCC: GCC only
    • +
  • gcccuda: GCC and CUDA
    • +
  • foss: GCC, OpenMPI, OpenBLAS/LAPACK, FFTW, ScaLAPACK
    • +
  • gompi: GCC, OpenMPI
    • +
  • gompic: GCC, OpenMPI, CUDA
    • +
  • gomkl: GCC, OpenMPI, MKL
    • +
  • iccifort: icc, ifort
    • +
  • iccifortcuda: icc, ifort, CUDA
    • +
  • iimpi: icc, ifort, IntelMPI
    • +
  • iimpic: iccifort, CUDA, impi
    • +
  • intel: icc, ifort, IntelMPI, IntelMKL
    • +
  • intel-compilers: icc, ifort (classic and oneAPI)
    • +
  • intelcuda: intel and CUDA
    • +
  • iompi: iccifort and OpenMPI
    • +
+
+

Exercise

+

Check which versions of the foss toolchain exist. Load one of them. Check which modules you now have loaded. Remove all the (non-sticky) modules.

+
+
+

Keypoints

+
    +
  • The software on Kebnekaise is mostly accessed through the module system.
    • +
  • The modules are arranged in a hierarchial layout; many modules have prerequisites that needs to be loaded first.
    • +
  • Important commands to the module system:
      +
    • Loading: module load MODULE
      • +
    • Unloading: module unload MODULE
      • +
    • Unload all modules: module purge
      • +
    • List all modules in the system: module spider
      • +
    • List versions available of a specific module: module spider MODULE
      • +
    • Show how to load a specific module and version: module spider MODULE/VERSION
      • +
    • List the modules you have currently loaded: module list
      • +
    +
    • +
  • Compiler toolchains are modules containing compiler suites and various libraries
    • +
+
+
+

More information

+ +
+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + + + diff --git a/projectsaccounts/index.html b/projectsaccounts/index.html new file mode 100644 index 00000000..719287a7 --- /dev/null +++ b/projectsaccounts/index.html @@ -0,0 +1,229 @@ + + + + + + + + Projects and Accounts - Introduction to Kebnekaise + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
    +
    • +
  • Projects and Accounts
    • +
  • +
    • +
+
+
+
+
+ +

Projects - compute and storage

+
+

Note

+

In order to have an account at HPC2N, you need to be a member of a compute project.

+

You can either join a project or apply for one yourself (if you fulfill the requirements).

+
+

There are both storage projects and compute projects. The storage projects are for when the amount of storage included with the compute project is not enough.

+
+

Important

+

You cannot have a storage project without a compute project!

+
+
+

Kebnekaise is only open for local project requests!

+
    +
  • The PI must be affiliated with UmU, LTU, IRF, MiUN, or SLU.
    • +
  • You can still add members (join) from anywhere.
    • +
+
+

Application process

+

Apply for compute projects in SUPR.

+ +
+

Info

+
    +
  • As default, you have 25GB in your home directory.
    • +
  • If you need more, you/your PI can accept the “default storage” you will be offered after applying for compute resources.
    • +
  • The default storage is 500GB.
    • +
  • If you need more than that, you/your PI will have to apply for a storage project.
    • +
  • When you have both, link them together. It is done from the storage project.
    • +
  • This way all members of the compute project also becomes members of the storage project.
    • +
+
+

After applying on SUPR, the project(s) will be reviewed.

+

Linking a compute project to a storage project

+
    +
  1. Before linking (SUPR):
    2. +
+

to-link +

+2. Pick a compute project to link:

+

choose +
+3. Showing linked projects:

+

linked +

+4. Members of the storage project after linking:

+

storage-members +

+

Accounts

+

When you have a project / have become member of a project, you can apply for an account at HPC2N. This is done in SUPR, under “Accounts”: https://supr.naiss.se/account/.

+

Your account request will be processed within a week. You will then get an email with information about logging in and links to getting started information.

+

More information on the account process can be found on HPC2N’s documentation pages: https://www.hpc2n.umu.se/documentation/access-and-accounts/users

+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + + + diff --git a/search.html b/search.html new file mode 100644 index 00000000..24b0986f --- /dev/null +++ b/search.html @@ -0,0 +1,155 @@ + + + + + + + + Introduction to Kebnekaise + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
    +
    • +
  • +
    • +
+
+
+
+
+ + +

Search Results

+ +
+ + +
+ +
+ Searching... +
+ + +
+
+ +
+
+ +
+ +
+ +
+ + + + + +
+ + + + + + + + + + + diff --git a/search/lunr.js b/search/lunr.js new file mode 100644 index 00000000..aca0a167 --- /dev/null +++ b/search/lunr.js @@ -0,0 +1,3475 @@ +/** + * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9 + * Copyright (C) 2020 Oliver Nightingale + * @license MIT + */ + +;(function(){ + +/** + * A convenience function for configuring and constructing + * a new lunr Index. + * + * A lunr.Builder instance is created and the pipeline setup + * with a trimmer, stop word filter and stemmer. + * + * This builder object is yielded to the configuration function + * that is passed as a parameter, allowing the list of fields + * and other builder parameters to be customised. + * + * All documents _must_ be added within the passed config function. + * + * @example + * var idx = lunr(function () { + * this.field('title') + * this.field('body') + * this.ref('id') + * + * documents.forEach(function (doc) { + * this.add(doc) + * }, this) + * }) + * + * @see {@link lunr.Builder} + * @see {@link lunr.Pipeline} + * @see {@link lunr.trimmer} + * @see {@link lunr.stopWordFilter} + * @see {@link lunr.stemmer} + * @namespace {function} lunr + */ +var lunr = function (config) { + var builder = new lunr.Builder + + builder.pipeline.add( + lunr.trimmer, + lunr.stopWordFilter, + lunr.stemmer + ) + + builder.searchPipeline.add( + lunr.stemmer + ) + + config.call(builder, builder) + return builder.build() +} + +lunr.version = "2.3.9" +/*! + * lunr.utils + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A namespace containing utils for the rest of the lunr library + * @namespace lunr.utils + */ +lunr.utils = {} + +/** + * Print a warning message to the console. + * + * @param {String} message The message to be printed. + * @memberOf lunr.utils + * @function + */ +lunr.utils.warn = (function (global) { + /* eslint-disable no-console */ + return function (message) { + if (global.console && console.warn) { + console.warn(message) + } + } + /* eslint-enable no-console */ +})(this) + +/** + * Convert an object to a string. + * + * In the case of `null` and `undefined` the function returns + * the empty string, in all other cases the result of calling + * `toString` on the passed object is returned. + * + * @param {Any} obj The object to convert to a string. + * @return {String} string representation of the passed object. + * @memberOf lunr.utils + */ +lunr.utils.asString = function (obj) { + if (obj === void 0 || obj === null) { + return "" + } else { + return obj.toString() + } +} + +/** + * Clones an object. + * + * Will create a copy of an existing object such that any mutations + * on the copy cannot affect the original. + * + * Only shallow objects are supported, passing a nested object to this + * function will cause a TypeError. + * + * Objects with primitives, and arrays of primitives are supported. + * + * @param {Object} obj The object to clone. + * @return {Object} a clone of the passed object. + * @throws {TypeError} when a nested object is passed. + * @memberOf Utils + */ +lunr.utils.clone = function (obj) { + if (obj === null || obj === undefined) { + return obj + } + + var clone = Object.create(null), + keys = Object.keys(obj) + + for (var i = 0; i < keys.length; i++) { + var key = keys[i], + val = obj[key] + + if (Array.isArray(val)) { + clone[key] = val.slice() + continue + } + + if (typeof val === 'string' || + typeof val === 'number' || + typeof val === 'boolean') { + clone[key] = val + continue + } + + throw new TypeError("clone is not deep and does not support nested objects") + } + + return clone +} +lunr.FieldRef = function (docRef, fieldName, stringValue) { + this.docRef = docRef + this.fieldName = fieldName + this._stringValue = stringValue +} + +lunr.FieldRef.joiner = "/" + +lunr.FieldRef.fromString = function (s) { + var n = s.indexOf(lunr.FieldRef.joiner) + + if (n === -1) { + throw "malformed field ref string" + } + + var fieldRef = s.slice(0, n), + docRef = s.slice(n + 1) + + return new lunr.FieldRef (docRef, fieldRef, s) +} + +lunr.FieldRef.prototype.toString = function () { + if (this._stringValue == undefined) { + this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef + } + + return this._stringValue +} +/*! + * lunr.Set + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A lunr set. + * + * @constructor + */ +lunr.Set = function (elements) { + this.elements = Object.create(null) + + if (elements) { + this.length = elements.length + + for (var i = 0; i < this.length; i++) { + this.elements[elements[i]] = true + } + } else { + this.length = 0 + } +} + +/** + * A complete set that contains all elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.complete = { + intersect: function (other) { + return other + }, + + union: function () { + return this + }, + + contains: function () { + return true + } +} + +/** + * An empty set that contains no elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.empty = { + intersect: function () { + return this + }, + + union: function (other) { + return other + }, + + contains: function () { + return false + } +} + +/** + * Returns true if this set contains the specified object. + * + * @param {object} object - Object whose presence in this set is to be tested. + * @returns {boolean} - True if this set contains the specified object. + */ +lunr.Set.prototype.contains = function (object) { + return !!this.elements[object] +} + +/** + * Returns a new set containing only the elements that are present in both + * this set and the specified set. + * + * @param {lunr.Set} other - set to intersect with this set. + * @returns {lunr.Set} a new set that is the intersection of this and the specified set. + */ + +lunr.Set.prototype.intersect = function (other) { + var a, b, elements, intersection = [] + + if (other === lunr.Set.complete) { + return this + } + + if (other === lunr.Set.empty) { + return other + } + + if (this.length < other.length) { + a = this + b = other + } else { + a = other + b = this + } + + elements = Object.keys(a.elements) + + for (var i = 0; i < elements.length; i++) { + var element = elements[i] + if (element in b.elements) { + intersection.push(element) + } + } + + return new lunr.Set (intersection) +} + +/** + * Returns a new set combining the elements of this and the specified set. + * + * @param {lunr.Set} other - set to union with this set. + * @return {lunr.Set} a new set that is the union of this and the specified set. + */ + +lunr.Set.prototype.union = function (other) { + if (other === lunr.Set.complete) { + return lunr.Set.complete + } + + if (other === lunr.Set.empty) { + return this + } + + return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements))) +} +/** + * A function to calculate the inverse document frequency for + * a posting. This is shared between the builder and the index + * + * @private + * @param {object} posting - The posting for a given term + * @param {number} documentCount - The total number of documents. + */ +lunr.idf = function (posting, documentCount) { + var documentsWithTerm = 0 + + for (var fieldName in posting) { + if (fieldName == '_index') continue // Ignore the term index, its not a field + documentsWithTerm += Object.keys(posting[fieldName]).length + } + + var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5) + + return Math.log(1 + Math.abs(x)) +} + +/** + * A token wraps a string representation of a token + * as it is passed through the text processing pipeline. + * + * @constructor + * @param {string} [str=''] - The string token being wrapped. + * @param {object} [metadata={}] - Metadata associated with this token. + */ +lunr.Token = function (str, metadata) { + this.str = str || "" + this.metadata = metadata || {} +} + +/** + * Returns the token string that is being wrapped by this object. + * + * @returns {string} + */ +lunr.Token.prototype.toString = function () { + return this.str +} + +/** + * A token update function is used when updating or optionally + * when cloning a token. + * + * @callback lunr.Token~updateFunction + * @param {string} str - The string representation of the token. + * @param {Object} metadata - All metadata associated with this token. + */ + +/** + * Applies the given function to the wrapped string token. + * + * @example + * token.update(function (str, metadata) { + * return str.toUpperCase() + * }) + * + * @param {lunr.Token~updateFunction} fn - A function to apply to the token string. + * @returns {lunr.Token} + */ +lunr.Token.prototype.update = function (fn) { + this.str = fn(this.str, this.metadata) + return this +} + +/** + * Creates a clone of this token. Optionally a function can be + * applied to the cloned token. + * + * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token. + * @returns {lunr.Token} + */ +lunr.Token.prototype.clone = function (fn) { + fn = fn || function (s) { return s } + return new lunr.Token (fn(this.str, this.metadata), this.metadata) +} +/*! + * lunr.tokenizer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A function for splitting a string into tokens ready to be inserted into + * the search index. Uses `lunr.tokenizer.separator` to split strings, change + * the value of this property to change how strings are split into tokens. + * + * This tokenizer will convert its parameter to a string by calling `toString` and + * then will split this string on the character in `lunr.tokenizer.separator`. + * Arrays will have their elements converted to strings and wrapped in a lunr.Token. + * + * Optional metadata can be passed to the tokenizer, this metadata will be cloned and + * added as metadata to every token that is created from the object to be tokenized. + * + * @static + * @param {?(string|object|object[])} obj - The object to convert into tokens + * @param {?object} metadata - Optional metadata to associate with every token + * @returns {lunr.Token[]} + * @see {@link lunr.Pipeline} + */ +lunr.tokenizer = function (obj, metadata) { + if (obj == null || obj == undefined) { + return [] + } + + if (Array.isArray(obj)) { + return obj.map(function (t) { + return new lunr.Token( + lunr.utils.asString(t).toLowerCase(), + lunr.utils.clone(metadata) + ) + }) + } + + var str = obj.toString().toLowerCase(), + len = str.length, + tokens = [] + + for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) { + var char = str.charAt(sliceEnd), + sliceLength = sliceEnd - sliceStart + + if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) { + + if (sliceLength > 0) { + var tokenMetadata = lunr.utils.clone(metadata) || {} + tokenMetadata["position"] = [sliceStart, sliceLength] + tokenMetadata["index"] = tokens.length + + tokens.push( + new lunr.Token ( + str.slice(sliceStart, sliceEnd), + tokenMetadata + ) + ) + } + + sliceStart = sliceEnd + 1 + } + + } + + return tokens +} + +/** + * The separator used to split a string into tokens. Override this property to change the behaviour of + * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens. + * + * @static + * @see lunr.tokenizer + */ +lunr.tokenizer.separator = /[\s\-]+/ +/*! + * lunr.Pipeline + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Pipelines maintain an ordered list of functions to be applied to all + * tokens in documents entering the search index and queries being ran against + * the index. + * + * An instance of lunr.Index created with the lunr shortcut will contain a + * pipeline with a stop word filter and an English language stemmer. Extra + * functions can be added before or after either of these functions or these + * default functions can be removed. + * + * When run the pipeline will call each function in turn, passing a token, the + * index of that token in the original list of all tokens and finally a list of + * all the original tokens. + * + * The output of functions in the pipeline will be passed to the next function + * in the pipeline. To exclude a token from entering the index the function + * should return undefined, the rest of the pipeline will not be called with + * this token. + * + * For serialisation of pipelines to work, all functions used in an instance of + * a pipeline should be registered with lunr.Pipeline. Registered functions can + * then be loaded. If trying to load a serialised pipeline that uses functions + * that are not registered an error will be thrown. + * + * If not planning on serialising the pipeline then registering pipeline functions + * is not necessary. + * + * @constructor + */ +lunr.Pipeline = function () { + this._stack = [] +} + +lunr.Pipeline.registeredFunctions = Object.create(null) + +/** + * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token + * string as well as all known metadata. A pipeline function can mutate the token string + * or mutate (or add) metadata for a given token. + * + * A pipeline function can indicate that the passed token should be discarded by returning + * null, undefined or an empty string. This token will not be passed to any downstream pipeline + * functions and will not be added to the index. + * + * Multiple tokens can be returned by returning an array of tokens. Each token will be passed + * to any downstream pipeline functions and all will returned tokens will be added to the index. + * + * Any number of pipeline functions may be chained together using a lunr.Pipeline. + * + * @interface lunr.PipelineFunction + * @param {lunr.Token} token - A token from the document being processed. + * @param {number} i - The index of this token in the complete list of tokens for this document/field. + * @param {lunr.Token[]} tokens - All tokens for this document/field. + * @returns {(?lunr.Token|lunr.Token[])} + */ + +/** + * Register a function with the pipeline. + * + * Functions that are used in the pipeline should be registered if the pipeline + * needs to be serialised, or a serialised pipeline needs to be loaded. + * + * Registering a function does not add it to a pipeline, functions must still be + * added to instances of the pipeline for them to be used when running a pipeline. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @param {String} label - The label to register this function with + */ +lunr.Pipeline.registerFunction = function (fn, label) { + if (label in this.registeredFunctions) { + lunr.utils.warn('Overwriting existing registered function: ' + label) + } + + fn.label = label + lunr.Pipeline.registeredFunctions[fn.label] = fn +} + +/** + * Warns if the function is not registered as a Pipeline function. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @private + */ +lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) { + var isRegistered = fn.label && (fn.label in this.registeredFunctions) + + if (!isRegistered) { + lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn) + } +} + +/** + * Loads a previously serialised pipeline. + * + * All functions to be loaded must already be registered with lunr.Pipeline. + * If any function from the serialised data has not been registered then an + * error will be thrown. + * + * @param {Object} serialised - The serialised pipeline to load. + * @returns {lunr.Pipeline} + */ +lunr.Pipeline.load = function (serialised) { + var pipeline = new lunr.Pipeline + + serialised.forEach(function (fnName) { + var fn = lunr.Pipeline.registeredFunctions[fnName] + + if (fn) { + pipeline.add(fn) + } else { + throw new Error('Cannot load unregistered function: ' + fnName) + } + }) + + return pipeline +} + +/** + * Adds new functions to the end of the pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline. + */ +lunr.Pipeline.prototype.add = function () { + var fns = Array.prototype.slice.call(arguments) + + fns.forEach(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + this._stack.push(fn) + }, this) +} + +/** + * Adds a single function after a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.after = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + pos = pos + 1 + this._stack.splice(pos, 0, newFn) +} + +/** + * Adds a single function before a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.before = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + this._stack.splice(pos, 0, newFn) +} + +/** + * Removes a function from the pipeline. + * + * @param {lunr.PipelineFunction} fn The function to remove from the pipeline. + */ +lunr.Pipeline.prototype.remove = function (fn) { + var pos = this._stack.indexOf(fn) + if (pos == -1) { + return + } + + this._stack.splice(pos, 1) +} + +/** + * Runs the current list of functions that make up the pipeline against the + * passed tokens. + * + * @param {Array} tokens The tokens to run through the pipeline. + * @returns {Array} + */ +lunr.Pipeline.prototype.run = function (tokens) { + var stackLength = this._stack.length + + for (var i = 0; i < stackLength; i++) { + var fn = this._stack[i] + var memo = [] + + for (var j = 0; j < tokens.length; j++) { + var result = fn(tokens[j], j, tokens) + + if (result === null || result === void 0 || result === '') continue + + if (Array.isArray(result)) { + for (var k = 0; k < result.length; k++) { + memo.push(result[k]) + } + } else { + memo.push(result) + } + } + + tokens = memo + } + + return tokens +} + +/** + * Convenience method for passing a string through a pipeline and getting + * strings out. This method takes care of wrapping the passed string in a + * token and mapping the resulting tokens back to strings. + * + * @param {string} str - The string to pass through the pipeline. + * @param {?object} metadata - Optional metadata to associate with the token + * passed to the pipeline. + * @returns {string[]} + */ +lunr.Pipeline.prototype.runString = function (str, metadata) { + var token = new lunr.Token (str, metadata) + + return this.run([token]).map(function (t) { + return t.toString() + }) +} + +/** + * Resets the pipeline by removing any existing processors. + * + */ +lunr.Pipeline.prototype.reset = function () { + this._stack = [] +} + +/** + * Returns a representation of the pipeline ready for serialisation. + * + * Logs a warning if the function has not been registered. + * + * @returns {Array} + */ +lunr.Pipeline.prototype.toJSON = function () { + return this._stack.map(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + + return fn.label + }) +} +/*! + * lunr.Vector + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A vector is used to construct the vector space of documents and queries. These + * vectors support operations to determine the similarity between two documents or + * a document and a query. + * + * Normally no parameters are required for initializing a vector, but in the case of + * loading a previously dumped vector the raw elements can be provided to the constructor. + * + * For performance reasons vectors are implemented with a flat array, where an elements + * index is immediately followed by its value. E.g. [index, value, index, value]. This + * allows the underlying array to be as sparse as possible and still offer decent + * performance when being used for vector calculations. + * + * @constructor + * @param {Number[]} [elements] - The flat list of element index and element value pairs. + */ +lunr.Vector = function (elements) { + this._magnitude = 0 + this.elements = elements || [] +} + + +/** + * Calculates the position within the vector to insert a given index. + * + * This is used internally by insert and upsert. If there are duplicate indexes then + * the position is returned as if the value for that index were to be updated, but it + * is the callers responsibility to check whether there is a duplicate at that index + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @returns {Number} + */ +lunr.Vector.prototype.positionForIndex = function (index) { + // For an empty vector the tuple can be inserted at the beginning + if (this.elements.length == 0) { + return 0 + } + + var start = 0, + end = this.elements.length / 2, + sliceLength = end - start, + pivotPoint = Math.floor(sliceLength / 2), + pivotIndex = this.elements[pivotPoint * 2] + + while (sliceLength > 1) { + if (pivotIndex < index) { + start = pivotPoint + } + + if (pivotIndex > index) { + end = pivotPoint + } + + if (pivotIndex == index) { + break + } + + sliceLength = end - start + pivotPoint = start + Math.floor(sliceLength / 2) + pivotIndex = this.elements[pivotPoint * 2] + } + + if (pivotIndex == index) { + return pivotPoint * 2 + } + + if (pivotIndex > index) { + return pivotPoint * 2 + } + + if (pivotIndex < index) { + return (pivotPoint + 1) * 2 + } +} + +/** + * Inserts an element at an index within the vector. + * + * Does not allow duplicates, will throw an error if there is already an entry + * for this index. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + */ +lunr.Vector.prototype.insert = function (insertIdx, val) { + this.upsert(insertIdx, val, function () { + throw "duplicate index" + }) +} + +/** + * Inserts or updates an existing index within the vector. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + * @param {function} fn - A function that is called for updates, the existing value and the + * requested value are passed as arguments + */ +lunr.Vector.prototype.upsert = function (insertIdx, val, fn) { + this._magnitude = 0 + var position = this.positionForIndex(insertIdx) + + if (this.elements[position] == insertIdx) { + this.elements[position + 1] = fn(this.elements[position + 1], val) + } else { + this.elements.splice(position, 0, insertIdx, val) + } +} + +/** + * Calculates the magnitude of this vector. + * + * @returns {Number} + */ +lunr.Vector.prototype.magnitude = function () { + if (this._magnitude) return this._magnitude + + var sumOfSquares = 0, + elementsLength = this.elements.length + + for (var i = 1; i < elementsLength; i += 2) { + var val = this.elements[i] + sumOfSquares += val * val + } + + return this._magnitude = Math.sqrt(sumOfSquares) +} + +/** + * Calculates the dot product of this vector and another vector. + * + * @param {lunr.Vector} otherVector - The vector to compute the dot product with. + * @returns {Number} + */ +lunr.Vector.prototype.dot = function (otherVector) { + var dotProduct = 0, + a = this.elements, b = otherVector.elements, + aLen = a.length, bLen = b.length, + aVal = 0, bVal = 0, + i = 0, j = 0 + + while (i < aLen && j < bLen) { + aVal = a[i], bVal = b[j] + if (aVal < bVal) { + i += 2 + } else if (aVal > bVal) { + j += 2 + } else if (aVal == bVal) { + dotProduct += a[i + 1] * b[j + 1] + i += 2 + j += 2 + } + } + + return dotProduct +} + +/** + * Calculates the similarity between this vector and another vector. + * + * @param {lunr.Vector} otherVector - The other vector to calculate the + * similarity with. + * @returns {Number} + */ +lunr.Vector.prototype.similarity = function (otherVector) { + return this.dot(otherVector) / this.magnitude() || 0 +} + +/** + * Converts the vector to an array of the elements within the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toArray = function () { + var output = new Array (this.elements.length / 2) + + for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) { + output[j] = this.elements[i] + } + + return output +} + +/** + * A JSON serializable representation of the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toJSON = function () { + return this.elements +} +/* eslint-disable */ +/*! + * lunr.stemmer + * Copyright (C) 2020 Oliver Nightingale + * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt + */ + +/** + * lunr.stemmer is an english language stemmer, this is a JavaScript + * implementation of the PorterStemmer taken from http://tartarus.org/~martin + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token - The string to stem + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + * @function + */ +lunr.stemmer = (function(){ + var step2list = { + "ational" : "ate", + "tional" : "tion", + "enci" : "ence", + "anci" : "ance", + "izer" : "ize", + "bli" : "ble", + "alli" : "al", + "entli" : "ent", + "eli" : "e", + "ousli" : "ous", + "ization" : "ize", + "ation" : "ate", + "ator" : "ate", + "alism" : "al", + "iveness" : "ive", + "fulness" : "ful", + "ousness" : "ous", + "aliti" : "al", + "iviti" : "ive", + "biliti" : "ble", + "logi" : "log" + }, + + step3list = { + "icate" : "ic", + "ative" : "", + "alize" : "al", + "iciti" : "ic", + "ical" : "ic", + "ful" : "", + "ness" : "" + }, + + c = "[^aeiou]", // consonant + v = "[aeiouy]", // vowel + C = c + "[^aeiouy]*", // consonant sequence + V = v + "[aeiou]*", // vowel sequence + + mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0 + meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1 + mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1 + s_v = "^(" + C + ")?" + v; // vowel in stem + + var re_mgr0 = new RegExp(mgr0); + var re_mgr1 = new RegExp(mgr1); + var re_meq1 = new RegExp(meq1); + var re_s_v = new RegExp(s_v); + + var re_1a = /^(.+?)(ss|i)es$/; + var re2_1a = /^(.+?)([^s])s$/; + var re_1b = /^(.+?)eed$/; + var re2_1b = /^(.+?)(ed|ing)$/; + var re_1b_2 = /.$/; + var re2_1b_2 = /(at|bl|iz)$/; + var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$"); + var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var re_1c = /^(.+?[^aeiou])y$/; + var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + + var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + + var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + var re2_4 = /^(.+?)(s|t)(ion)$/; + + var re_5 = /^(.+?)e$/; + var re_5_1 = /ll$/; + var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var porterStemmer = function porterStemmer(w) { + var stem, + suffix, + firstch, + re, + re2, + re3, + re4; + + if (w.length < 3) { return w; } + + firstch = w.substr(0,1); + if (firstch == "y") { + w = firstch.toUpperCase() + w.substr(1); + } + + // Step 1a + re = re_1a + re2 = re2_1a; + + if (re.test(w)) { w = w.replace(re,"$1$2"); } + else if (re2.test(w)) { w = w.replace(re2,"$1$2"); } + + // Step 1b + re = re_1b; + re2 = re2_1b; + if (re.test(w)) { + var fp = re.exec(w); + re = re_mgr0; + if (re.test(fp[1])) { + re = re_1b_2; + w = w.replace(re,""); + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = re_s_v; + if (re2.test(stem)) { + w = stem; + re2 = re2_1b_2; + re3 = re3_1b_2; + re4 = re4_1b_2; + if (re2.test(w)) { w = w + "e"; } + else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); } + else if (re4.test(w)) { w = w + "e"; } + } + } + + // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say) + re = re_1c; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + w = stem + "i"; + } + + // Step 2 + re = re_2; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step2list[suffix]; + } + } + + // Step 3 + re = re_3; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step3list[suffix]; + } + } + + // Step 4 + re = re_4; + re2 = re2_4; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + if (re.test(stem)) { + w = stem; + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = re_mgr1; + if (re2.test(stem)) { + w = stem; + } + } + + // Step 5 + re = re_5; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + re2 = re_meq1; + re3 = re3_5; + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) { + w = stem; + } + } + + re = re_5_1; + re2 = re_mgr1; + if (re.test(w) && re2.test(w)) { + re = re_1b_2; + w = w.replace(re,""); + } + + // and turn initial Y back to y + + if (firstch == "y") { + w = firstch.toLowerCase() + w.substr(1); + } + + return w; + }; + + return function (token) { + return token.update(porterStemmer); + } +})(); + +lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer') +/*! + * lunr.stopWordFilter + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.generateStopWordFilter builds a stopWordFilter function from the provided + * list of stop words. + * + * The built in lunr.stopWordFilter is built using this generator and can be used + * to generate custom stopWordFilters for applications or non English languages. + * + * @function + * @param {Array} token The token to pass through the filter + * @returns {lunr.PipelineFunction} + * @see lunr.Pipeline + * @see lunr.stopWordFilter + */ +lunr.generateStopWordFilter = function (stopWords) { + var words = stopWords.reduce(function (memo, stopWord) { + memo[stopWord] = stopWord + return memo + }, {}) + + return function (token) { + if (token && words[token.toString()] !== token.toString()) return token + } +} + +/** + * lunr.stopWordFilter is an English language stop word list filter, any words + * contained in the list will not be passed through the filter. + * + * This is intended to be used in the Pipeline. If the token does not pass the + * filter then undefined will be returned. + * + * @function + * @implements {lunr.PipelineFunction} + * @params {lunr.Token} token - A token to check for being a stop word. + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + */ +lunr.stopWordFilter = lunr.generateStopWordFilter([ + 'a', + 'able', + 'about', + 'across', + 'after', + 'all', + 'almost', + 'also', + 'am', + 'among', + 'an', + 'and', + 'any', + 'are', + 'as', + 'at', + 'be', + 'because', + 'been', + 'but', + 'by', + 'can', + 'cannot', + 'could', + 'dear', + 'did', + 'do', + 'does', + 'either', + 'else', + 'ever', + 'every', + 'for', + 'from', + 'get', + 'got', + 'had', + 'has', + 'have', + 'he', + 'her', + 'hers', + 'him', + 'his', + 'how', + 'however', + 'i', + 'if', + 'in', + 'into', + 'is', + 'it', + 'its', + 'just', + 'least', + 'let', + 'like', + 'likely', + 'may', + 'me', + 'might', + 'most', + 'must', + 'my', + 'neither', + 'no', + 'nor', + 'not', + 'of', + 'off', + 'often', + 'on', + 'only', + 'or', + 'other', + 'our', + 'own', + 'rather', + 'said', + 'say', + 'says', + 'she', + 'should', + 'since', + 'so', + 'some', + 'than', + 'that', + 'the', + 'their', + 'them', + 'then', + 'there', + 'these', + 'they', + 'this', + 'tis', + 'to', + 'too', + 'twas', + 'us', + 'wants', + 'was', + 'we', + 'were', + 'what', + 'when', + 'where', + 'which', + 'while', + 'who', + 'whom', + 'why', + 'will', + 'with', + 'would', + 'yet', + 'you', + 'your' +]) + +lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter') +/*! + * lunr.trimmer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.trimmer is a pipeline function for trimming non word + * characters from the beginning and end of tokens before they + * enter the index. + * + * This implementation may not work correctly for non latin + * characters and should either be removed or adapted for use + * with languages with non-latin characters. + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token The token to pass through the filter + * @returns {lunr.Token} + * @see lunr.Pipeline + */ +lunr.trimmer = function (token) { + return token.update(function (s) { + return s.replace(/^\W+/, '').replace(/\W+$/, '') + }) +} + +lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer') +/*! + * lunr.TokenSet + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A token set is used to store the unique list of all tokens + * within an index. Token sets are also used to represent an + * incoming query to the index, this query token set and index + * token set are then intersected to find which tokens to look + * up in the inverted index. + * + * A token set can hold multiple tokens, as in the case of the + * index token set, or it can hold a single token as in the + * case of a simple query token set. + * + * Additionally token sets are used to perform wildcard matching. + * Leading, contained and trailing wildcards are supported, and + * from this edit distance matching can also be provided. + * + * Token sets are implemented as a minimal finite state automata, + * where both common prefixes and suffixes are shared between tokens. + * This helps to reduce the space used for storing the token set. + * + * @constructor + */ +lunr.TokenSet = function () { + this.final = false + this.edges = {} + this.id = lunr.TokenSet._nextId + lunr.TokenSet._nextId += 1 +} + +/** + * Keeps track of the next, auto increment, identifier to assign + * to a new tokenSet. + * + * TokenSets require a unique identifier to be correctly minimised. + * + * @private + */ +lunr.TokenSet._nextId = 1 + +/** + * Creates a TokenSet instance from the given sorted array of words. + * + * @param {String[]} arr - A sorted array of strings to create the set from. + * @returns {lunr.TokenSet} + * @throws Will throw an error if the input array is not sorted. + */ +lunr.TokenSet.fromArray = function (arr) { + var builder = new lunr.TokenSet.Builder + + for (var i = 0, len = arr.length; i < len; i++) { + builder.insert(arr[i]) + } + + builder.finish() + return builder.root +} + +/** + * Creates a token set from a query clause. + * + * @private + * @param {Object} clause - A single clause from lunr.Query. + * @param {string} clause.term - The query clause term. + * @param {number} [clause.editDistance] - The optional edit distance for the term. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromClause = function (clause) { + if ('editDistance' in clause) { + return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance) + } else { + return lunr.TokenSet.fromString(clause.term) + } +} + +/** + * Creates a token set representing a single string with a specified + * edit distance. + * + * Insertions, deletions, substitutions and transpositions are each + * treated as an edit distance of 1. + * + * Increasing the allowed edit distance will have a dramatic impact + * on the performance of both creating and intersecting these TokenSets. + * It is advised to keep the edit distance less than 3. + * + * @param {string} str - The string to create the token set from. + * @param {number} editDistance - The allowed edit distance to match. + * @returns {lunr.Vector} + */ +lunr.TokenSet.fromFuzzyString = function (str, editDistance) { + var root = new lunr.TokenSet + + var stack = [{ + node: root, + editsRemaining: editDistance, + str: str + }] + + while (stack.length) { + var frame = stack.pop() + + // no edit + if (frame.str.length > 0) { + var char = frame.str.charAt(0), + noEditNode + + if (char in frame.node.edges) { + noEditNode = frame.node.edges[char] + } else { + noEditNode = new lunr.TokenSet + frame.node.edges[char] = noEditNode + } + + if (frame.str.length == 1) { + noEditNode.final = true + } + + stack.push({ + node: noEditNode, + editsRemaining: frame.editsRemaining, + str: frame.str.slice(1) + }) + } + + if (frame.editsRemaining == 0) { + continue + } + + // insertion + if ("*" in frame.node.edges) { + var insertionNode = frame.node.edges["*"] + } else { + var insertionNode = new lunr.TokenSet + frame.node.edges["*"] = insertionNode + } + + if (frame.str.length == 0) { + insertionNode.final = true + } + + stack.push({ + node: insertionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str + }) + + // deletion + // can only do a deletion if we have enough edits remaining + // and if there are characters left to delete in the string + if (frame.str.length > 1) { + stack.push({ + node: frame.node, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // deletion + // just removing the last character from the str + if (frame.str.length == 1) { + frame.node.final = true + } + + // substitution + // can only do a substitution if we have enough edits remaining + // and if there are characters left to substitute + if (frame.str.length >= 1) { + if ("*" in frame.node.edges) { + var substitutionNode = frame.node.edges["*"] + } else { + var substitutionNode = new lunr.TokenSet + frame.node.edges["*"] = substitutionNode + } + + if (frame.str.length == 1) { + substitutionNode.final = true + } + + stack.push({ + node: substitutionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // transposition + // can only do a transposition if there are edits remaining + // and there are enough characters to transpose + if (frame.str.length > 1) { + var charA = frame.str.charAt(0), + charB = frame.str.charAt(1), + transposeNode + + if (charB in frame.node.edges) { + transposeNode = frame.node.edges[charB] + } else { + transposeNode = new lunr.TokenSet + frame.node.edges[charB] = transposeNode + } + + if (frame.str.length == 1) { + transposeNode.final = true + } + + stack.push({ + node: transposeNode, + editsRemaining: frame.editsRemaining - 1, + str: charA + frame.str.slice(2) + }) + } + } + + return root +} + +/** + * Creates a TokenSet from a string. + * + * The string may contain one or more wildcard characters (*) + * that will allow wildcard matching when intersecting with + * another TokenSet. + * + * @param {string} str - The string to create a TokenSet from. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromString = function (str) { + var node = new lunr.TokenSet, + root = node + + /* + * Iterates through all characters within the passed string + * appending a node for each character. + * + * When a wildcard character is found then a self + * referencing edge is introduced to continually match + * any number of any characters. + */ + for (var i = 0, len = str.length; i < len; i++) { + var char = str[i], + final = (i == len - 1) + + if (char == "*") { + node.edges[char] = node + node.final = final + + } else { + var next = new lunr.TokenSet + next.final = final + + node.edges[char] = next + node = next + } + } + + return root +} + +/** + * Converts this TokenSet into an array of strings + * contained within the TokenSet. + * + * This is not intended to be used on a TokenSet that + * contains wildcards, in these cases the results are + * undefined and are likely to cause an infinite loop. + * + * @returns {string[]} + */ +lunr.TokenSet.prototype.toArray = function () { + var words = [] + + var stack = [{ + prefix: "", + node: this + }] + + while (stack.length) { + var frame = stack.pop(), + edges = Object.keys(frame.node.edges), + len = edges.length + + if (frame.node.final) { + /* In Safari, at this point the prefix is sometimes corrupted, see: + * https://github.com/olivernn/lunr.js/issues/279 Calling any + * String.prototype method forces Safari to "cast" this string to what + * it's supposed to be, fixing the bug. */ + frame.prefix.charAt(0) + words.push(frame.prefix) + } + + for (var i = 0; i < len; i++) { + var edge = edges[i] + + stack.push({ + prefix: frame.prefix.concat(edge), + node: frame.node.edges[edge] + }) + } + } + + return words +} + +/** + * Generates a string representation of a TokenSet. + * + * This is intended to allow TokenSets to be used as keys + * in objects, largely to aid the construction and minimisation + * of a TokenSet. As such it is not designed to be a human + * friendly representation of the TokenSet. + * + * @returns {string} + */ +lunr.TokenSet.prototype.toString = function () { + // NOTE: Using Object.keys here as this.edges is very likely + // to enter 'hash-mode' with many keys being added + // + // avoiding a for-in loop here as it leads to the function + // being de-optimised (at least in V8). From some simple + // benchmarks the performance is comparable, but allowing + // V8 to optimize may mean easy performance wins in the future. + + if (this._str) { + return this._str + } + + var str = this.final ? '1' : '0', + labels = Object.keys(this.edges).sort(), + len = labels.length + + for (var i = 0; i < len; i++) { + var label = labels[i], + node = this.edges[label] + + str = str + label + node.id + } + + return str +} + +/** + * Returns a new TokenSet that is the intersection of + * this TokenSet and the passed TokenSet. + * + * This intersection will take into account any wildcards + * contained within the TokenSet. + * + * @param {lunr.TokenSet} b - An other TokenSet to intersect with. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.prototype.intersect = function (b) { + var output = new lunr.TokenSet, + frame = undefined + + var stack = [{ + qNode: b, + output: output, + node: this + }] + + while (stack.length) { + frame = stack.pop() + + // NOTE: As with the #toString method, we are using + // Object.keys and a for loop instead of a for-in loop + // as both of these objects enter 'hash' mode, causing + // the function to be de-optimised in V8 + var qEdges = Object.keys(frame.qNode.edges), + qLen = qEdges.length, + nEdges = Object.keys(frame.node.edges), + nLen = nEdges.length + + for (var q = 0; q < qLen; q++) { + var qEdge = qEdges[q] + + for (var n = 0; n < nLen; n++) { + var nEdge = nEdges[n] + + if (nEdge == qEdge || qEdge == '*') { + var node = frame.node.edges[nEdge], + qNode = frame.qNode.edges[qEdge], + final = node.final && qNode.final, + next = undefined + + if (nEdge in frame.output.edges) { + // an edge already exists for this character + // no need to create a new node, just set the finality + // bit unless this node is already final + next = frame.output.edges[nEdge] + next.final = next.final || final + + } else { + // no edge exists yet, must create one + // set the finality bit and insert it + // into the output + next = new lunr.TokenSet + next.final = final + frame.output.edges[nEdge] = next + } + + stack.push({ + qNode: qNode, + output: next, + node: node + }) + } + } + } + } + + return output +} +lunr.TokenSet.Builder = function () { + this.previousWord = "" + this.root = new lunr.TokenSet + this.uncheckedNodes = [] + this.minimizedNodes = {} +} + +lunr.TokenSet.Builder.prototype.insert = function (word) { + var node, + commonPrefix = 0 + + if (word < this.previousWord) { + throw new Error ("Out of order word insertion") + } + + for (var i = 0; i < word.length && i < this.previousWord.length; i++) { + if (word[i] != this.previousWord[i]) break + commonPrefix++ + } + + this.minimize(commonPrefix) + + if (this.uncheckedNodes.length == 0) { + node = this.root + } else { + node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child + } + + for (var i = commonPrefix; i < word.length; i++) { + var nextNode = new lunr.TokenSet, + char = word[i] + + node.edges[char] = nextNode + + this.uncheckedNodes.push({ + parent: node, + char: char, + child: nextNode + }) + + node = nextNode + } + + node.final = true + this.previousWord = word +} + +lunr.TokenSet.Builder.prototype.finish = function () { + this.minimize(0) +} + +lunr.TokenSet.Builder.prototype.minimize = function (downTo) { + for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) { + var node = this.uncheckedNodes[i], + childKey = node.child.toString() + + if (childKey in this.minimizedNodes) { + node.parent.edges[node.char] = this.minimizedNodes[childKey] + } else { + // Cache the key for this node since + // we know it can't change anymore + node.child._str = childKey + + this.minimizedNodes[childKey] = node.child + } + + this.uncheckedNodes.pop() + } +} +/*! + * lunr.Index + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * An index contains the built index of all documents and provides a query interface + * to the index. + * + * Usually instances of lunr.Index will not be created using this constructor, instead + * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be + * used to load previously built and serialized indexes. + * + * @constructor + * @param {Object} attrs - The attributes of the built search index. + * @param {Object} attrs.invertedIndex - An index of term/field to document reference. + * @param {Object} attrs.fieldVectors - Field vectors + * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens. + * @param {string[]} attrs.fields - The names of indexed document fields. + * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms. + */ +lunr.Index = function (attrs) { + this.invertedIndex = attrs.invertedIndex + this.fieldVectors = attrs.fieldVectors + this.tokenSet = attrs.tokenSet + this.fields = attrs.fields + this.pipeline = attrs.pipeline +} + +/** + * A result contains details of a document matching a search query. + * @typedef {Object} lunr.Index~Result + * @property {string} ref - The reference of the document this result represents. + * @property {number} score - A number between 0 and 1 representing how similar this document is to the query. + * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match. + */ + +/** + * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple + * query language which itself is parsed into an instance of lunr.Query. + * + * For programmatically building queries it is advised to directly use lunr.Query, the query language + * is best used for human entered text rather than program generated text. + * + * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported + * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello' + * or 'world', though those that contain both will rank higher in the results. + * + * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can + * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding + * wildcards will increase the number of documents that will be found but can also have a negative + * impact on query performance, especially with wildcards at the beginning of a term. + * + * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term + * hello in the title field will match this query. Using a field not present in the index will lead + * to an error being thrown. + * + * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term + * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported + * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2. + * Avoid large values for edit distance to improve query performance. + * + * Each term also supports a presence modifier. By default a term's presence in document is optional, however + * this can be changed to either required or prohibited. For a term's presence to be required in a document the + * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and + * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not + * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'. + * + * To escape special characters the backslash character '\' can be used, this allows searches to include + * characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead + * of attempting to apply a boost of 2 to the search term "foo". + * + * @typedef {string} lunr.Index~QueryString + * @example Simple single term query + * hello + * @example Multiple term query + * hello world + * @example term scoped to a field + * title:hello + * @example term with a boost of 10 + * hello^10 + * @example term with an edit distance of 2 + * hello~2 + * @example terms with presence modifiers + * -foo +bar baz + */ + +/** + * Performs a search against the index using lunr query syntax. + * + * Results will be returned sorted by their score, the most relevant results + * will be returned first. For details on how the score is calculated, please see + * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}. + * + * For more programmatic querying use lunr.Index#query. + * + * @param {lunr.Index~QueryString} queryString - A string containing a lunr query. + * @throws {lunr.QueryParseError} If the passed query string cannot be parsed. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.search = function (queryString) { + return this.query(function (query) { + var parser = new lunr.QueryParser(queryString, query) + parser.parse() + }) +} + +/** + * A query builder callback provides a query object to be used to express + * the query to perform on the index. + * + * @callback lunr.Index~queryBuilder + * @param {lunr.Query} query - The query object to build up. + * @this lunr.Query + */ + +/** + * Performs a query against the index using the yielded lunr.Query object. + * + * If performing programmatic queries against the index, this method is preferred + * over lunr.Index#search so as to avoid the additional query parsing overhead. + * + * A query object is yielded to the supplied function which should be used to + * express the query to be run against the index. + * + * Note that although this function takes a callback parameter it is _not_ an + * asynchronous operation, the callback is just yielded a query object to be + * customized. + * + * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.query = function (fn) { + // for each query clause + // * process terms + // * expand terms from token set + // * find matching documents and metadata + // * get document vectors + // * score documents + + var query = new lunr.Query(this.fields), + matchingFields = Object.create(null), + queryVectors = Object.create(null), + termFieldCache = Object.create(null), + requiredMatches = Object.create(null), + prohibitedMatches = Object.create(null) + + /* + * To support field level boosts a query vector is created per + * field. An empty vector is eagerly created to support negated + * queries. + */ + for (var i = 0; i < this.fields.length; i++) { + queryVectors[this.fields[i]] = new lunr.Vector + } + + fn.call(query, query) + + for (var i = 0; i < query.clauses.length; i++) { + /* + * Unless the pipeline has been disabled for this term, which is + * the case for terms with wildcards, we need to pass the clause + * term through the search pipeline. A pipeline returns an array + * of processed terms. Pipeline functions may expand the passed + * term, which means we may end up performing multiple index lookups + * for a single query term. + */ + var clause = query.clauses[i], + terms = null, + clauseMatches = lunr.Set.empty + + if (clause.usePipeline) { + terms = this.pipeline.runString(clause.term, { + fields: clause.fields + }) + } else { + terms = [clause.term] + } + + for (var m = 0; m < terms.length; m++) { + var term = terms[m] + + /* + * Each term returned from the pipeline needs to use the same query + * clause object, e.g. the same boost and or edit distance. The + * simplest way to do this is to re-use the clause object but mutate + * its term property. + */ + clause.term = term + + /* + * From the term in the clause we create a token set which will then + * be used to intersect the indexes token set to get a list of terms + * to lookup in the inverted index + */ + var termTokenSet = lunr.TokenSet.fromClause(clause), + expandedTerms = this.tokenSet.intersect(termTokenSet).toArray() + + /* + * If a term marked as required does not exist in the tokenSet it is + * impossible for the search to return any matches. We set all the field + * scoped required matches set to empty and stop examining any further + * clauses. + */ + if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = lunr.Set.empty + } + + break + } + + for (var j = 0; j < expandedTerms.length; j++) { + /* + * For each term get the posting and termIndex, this is required for + * building the query vector. + */ + var expandedTerm = expandedTerms[j], + posting = this.invertedIndex[expandedTerm], + termIndex = posting._index + + for (var k = 0; k < clause.fields.length; k++) { + /* + * For each field that this query term is scoped by (by default + * all fields are in scope) we need to get all the document refs + * that have this term in that field. + * + * The posting is the entry in the invertedIndex for the matching + * term from above. + */ + var field = clause.fields[k], + fieldPosting = posting[field], + matchingDocumentRefs = Object.keys(fieldPosting), + termField = expandedTerm + "/" + field, + matchingDocumentsSet = new lunr.Set(matchingDocumentRefs) + + /* + * if the presence of this term is required ensure that the matching + * documents are added to the set of required matches for this clause. + * + */ + if (clause.presence == lunr.Query.presence.REQUIRED) { + clauseMatches = clauseMatches.union(matchingDocumentsSet) + + if (requiredMatches[field] === undefined) { + requiredMatches[field] = lunr.Set.complete + } + } + + /* + * if the presence of this term is prohibited ensure that the matching + * documents are added to the set of prohibited matches for this field, + * creating that set if it does not yet exist. + */ + if (clause.presence == lunr.Query.presence.PROHIBITED) { + if (prohibitedMatches[field] === undefined) { + prohibitedMatches[field] = lunr.Set.empty + } + + prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet) + + /* + * Prohibited matches should not be part of the query vector used for + * similarity scoring and no metadata should be extracted so we continue + * to the next field + */ + continue + } + + /* + * The query field vector is populated using the termIndex found for + * the term and a unit value with the appropriate boost applied. + * Using upsert because there could already be an entry in the vector + * for the term we are working with. In that case we just add the scores + * together. + */ + queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b }) + + /** + * If we've already seen this term, field combo then we've already collected + * the matching documents and metadata, no need to go through all that again + */ + if (termFieldCache[termField]) { + continue + } + + for (var l = 0; l < matchingDocumentRefs.length; l++) { + /* + * All metadata for this term/field/document triple + * are then extracted and collected into an instance + * of lunr.MatchData ready to be returned in the query + * results + */ + var matchingDocumentRef = matchingDocumentRefs[l], + matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field), + metadata = fieldPosting[matchingDocumentRef], + fieldMatch + + if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) { + matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata) + } else { + fieldMatch.add(expandedTerm, field, metadata) + } + + } + + termFieldCache[termField] = true + } + } + } + + /** + * If the presence was required we need to update the requiredMatches field sets. + * We do this after all fields for the term have collected their matches because + * the clause terms presence is required in _any_ of the fields not _all_ of the + * fields. + */ + if (clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = requiredMatches[field].intersect(clauseMatches) + } + } + } + + /** + * Need to combine the field scoped required and prohibited + * matching documents into a global set of required and prohibited + * matches + */ + var allRequiredMatches = lunr.Set.complete, + allProhibitedMatches = lunr.Set.empty + + for (var i = 0; i < this.fields.length; i++) { + var field = this.fields[i] + + if (requiredMatches[field]) { + allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field]) + } + + if (prohibitedMatches[field]) { + allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field]) + } + } + + var matchingFieldRefs = Object.keys(matchingFields), + results = [], + matches = Object.create(null) + + /* + * If the query is negated (contains only prohibited terms) + * we need to get _all_ fieldRefs currently existing in the + * index. This is only done when we know that the query is + * entirely prohibited terms to avoid any cost of getting all + * fieldRefs unnecessarily. + * + * Additionally, blank MatchData must be created to correctly + * populate the results. + */ + if (query.isNegated()) { + matchingFieldRefs = Object.keys(this.fieldVectors) + + for (var i = 0; i < matchingFieldRefs.length; i++) { + var matchingFieldRef = matchingFieldRefs[i] + var fieldRef = lunr.FieldRef.fromString(matchingFieldRef) + matchingFields[matchingFieldRef] = new lunr.MatchData + } + } + + for (var i = 0; i < matchingFieldRefs.length; i++) { + /* + * Currently we have document fields that match the query, but we + * need to return documents. The matchData and scores are combined + * from multiple fields belonging to the same document. + * + * Scores are calculated by field, using the query vectors created + * above, and combined into a final document score using addition. + */ + var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]), + docRef = fieldRef.docRef + + if (!allRequiredMatches.contains(docRef)) { + continue + } + + if (allProhibitedMatches.contains(docRef)) { + continue + } + + var fieldVector = this.fieldVectors[fieldRef], + score = queryVectors[fieldRef.fieldName].similarity(fieldVector), + docMatch + + if ((docMatch = matches[docRef]) !== undefined) { + docMatch.score += score + docMatch.matchData.combine(matchingFields[fieldRef]) + } else { + var match = { + ref: docRef, + score: score, + matchData: matchingFields[fieldRef] + } + matches[docRef] = match + results.push(match) + } + } + + /* + * Sort the results objects by score, highest first. + */ + return results.sort(function (a, b) { + return b.score - a.score + }) +} + +/** + * Prepares the index for JSON serialization. + * + * The schema for this JSON blob will be described in a + * separate JSON schema file. + * + * @returns {Object} + */ +lunr.Index.prototype.toJSON = function () { + var invertedIndex = Object.keys(this.invertedIndex) + .sort() + .map(function (term) { + return [term, this.invertedIndex[term]] + }, this) + + var fieldVectors = Object.keys(this.fieldVectors) + .map(function (ref) { + return [ref, this.fieldVectors[ref].toJSON()] + }, this) + + return { + version: lunr.version, + fields: this.fields, + fieldVectors: fieldVectors, + invertedIndex: invertedIndex, + pipeline: this.pipeline.toJSON() + } +} + +/** + * Loads a previously serialized lunr.Index + * + * @param {Object} serializedIndex - A previously serialized lunr.Index + * @returns {lunr.Index} + */ +lunr.Index.load = function (serializedIndex) { + var attrs = {}, + fieldVectors = {}, + serializedVectors = serializedIndex.fieldVectors, + invertedIndex = Object.create(null), + serializedInvertedIndex = serializedIndex.invertedIndex, + tokenSetBuilder = new lunr.TokenSet.Builder, + pipeline = lunr.Pipeline.load(serializedIndex.pipeline) + + if (serializedIndex.version != lunr.version) { + lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'") + } + + for (var i = 0; i < serializedVectors.length; i++) { + var tuple = serializedVectors[i], + ref = tuple[0], + elements = tuple[1] + + fieldVectors[ref] = new lunr.Vector(elements) + } + + for (var i = 0; i < serializedInvertedIndex.length; i++) { + var tuple = serializedInvertedIndex[i], + term = tuple[0], + posting = tuple[1] + + tokenSetBuilder.insert(term) + invertedIndex[term] = posting + } + + tokenSetBuilder.finish() + + attrs.fields = serializedIndex.fields + + attrs.fieldVectors = fieldVectors + attrs.invertedIndex = invertedIndex + attrs.tokenSet = tokenSetBuilder.root + attrs.pipeline = pipeline + + return new lunr.Index(attrs) +} +/*! + * lunr.Builder + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Builder performs indexing on a set of documents and + * returns instances of lunr.Index ready for querying. + * + * All configuration of the index is done via the builder, the + * fields to index, the document reference, the text processing + * pipeline and document scoring parameters are all set on the + * builder before indexing. + * + * @constructor + * @property {string} _ref - Internal reference to the document reference field. + * @property {string[]} _fields - Internal reference to the document fields to index. + * @property {object} invertedIndex - The inverted index maps terms to document fields. + * @property {object} documentTermFrequencies - Keeps track of document term frequencies. + * @property {object} documentLengths - Keeps track of the length of documents added to the index. + * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing. + * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing. + * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index. + * @property {number} documentCount - Keeps track of the total number of documents indexed. + * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75. + * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2. + * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space. + * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index. + */ +lunr.Builder = function () { + this._ref = "id" + this._fields = Object.create(null) + this._documents = Object.create(null) + this.invertedIndex = Object.create(null) + this.fieldTermFrequencies = {} + this.fieldLengths = {} + this.tokenizer = lunr.tokenizer + this.pipeline = new lunr.Pipeline + this.searchPipeline = new lunr.Pipeline + this.documentCount = 0 + this._b = 0.75 + this._k1 = 1.2 + this.termIndex = 0 + this.metadataWhitelist = [] +} + +/** + * Sets the document field used as the document reference. Every document must have this field. + * The type of this field in the document should be a string, if it is not a string it will be + * coerced into a string by calling toString. + * + * The default ref is 'id'. + * + * The ref should _not_ be changed during indexing, it should be set before any documents are + * added to the index. Changing it during indexing can lead to inconsistent results. + * + * @param {string} ref - The name of the reference field in the document. + */ +lunr.Builder.prototype.ref = function (ref) { + this._ref = ref +} + +/** + * A function that is used to extract a field from a document. + * + * Lunr expects a field to be at the top level of a document, if however the field + * is deeply nested within a document an extractor function can be used to extract + * the right field for indexing. + * + * @callback fieldExtractor + * @param {object} doc - The document being added to the index. + * @returns {?(string|object|object[])} obj - The object that will be indexed for this field. + * @example Extracting a nested field + * function (doc) { return doc.nested.field } + */ + +/** + * Adds a field to the list of document fields that will be indexed. Every document being + * indexed should have this field. Null values for this field in indexed documents will + * not cause errors but will limit the chance of that document being retrieved by searches. + * + * All fields should be added before adding documents to the index. Adding fields after + * a document has been indexed will have no effect on already indexed documents. + * + * Fields can be boosted at build time. This allows terms within that field to have more + * importance when ranking search results. Use a field boost to specify that matches within + * one field are more important than other fields. + * + * @param {string} fieldName - The name of a field to index in all documents. + * @param {object} attributes - Optional attributes associated with this field. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this field. + * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document. + * @throws {RangeError} fieldName cannot contain unsupported characters '/' + */ +lunr.Builder.prototype.field = function (fieldName, attributes) { + if (/\//.test(fieldName)) { + throw new RangeError ("Field '" + fieldName + "' contains illegal character '/'") + } + + this._fields[fieldName] = attributes || {} +} + +/** + * A parameter to tune the amount of field length normalisation that is applied when + * calculating relevance scores. A value of 0 will completely disable any normalisation + * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b + * will be clamped to the range 0 - 1. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.b = function (number) { + if (number < 0) { + this._b = 0 + } else if (number > 1) { + this._b = 1 + } else { + this._b = number + } +} + +/** + * A parameter that controls the speed at which a rise in term frequency results in term + * frequency saturation. The default value is 1.2. Setting this to a higher value will give + * slower saturation levels, a lower value will result in quicker saturation. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.k1 = function (number) { + this._k1 = number +} + +/** + * Adds a document to the index. + * + * Before adding fields to the index the index should have been fully setup, with the document + * ref and all fields to index already having been specified. + * + * The document must have a field name as specified by the ref (by default this is 'id') and + * it should have all fields defined for indexing, though null or undefined values will not + * cause errors. + * + * Entire documents can be boosted at build time. Applying a boost to a document indicates that + * this document should rank higher in search results than other documents. + * + * @param {object} doc - The document to add to the index. + * @param {object} attributes - Optional attributes associated with this document. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this document. + */ +lunr.Builder.prototype.add = function (doc, attributes) { + var docRef = doc[this._ref], + fields = Object.keys(this._fields) + + this._documents[docRef] = attributes || {} + this.documentCount += 1 + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i], + extractor = this._fields[fieldName].extractor, + field = extractor ? extractor(doc) : doc[fieldName], + tokens = this.tokenizer(field, { + fields: [fieldName] + }), + terms = this.pipeline.run(tokens), + fieldRef = new lunr.FieldRef (docRef, fieldName), + fieldTerms = Object.create(null) + + this.fieldTermFrequencies[fieldRef] = fieldTerms + this.fieldLengths[fieldRef] = 0 + + // store the length of this field for this document + this.fieldLengths[fieldRef] += terms.length + + // calculate term frequencies for this field + for (var j = 0; j < terms.length; j++) { + var term = terms[j] + + if (fieldTerms[term] == undefined) { + fieldTerms[term] = 0 + } + + fieldTerms[term] += 1 + + // add to inverted index + // create an initial posting if one doesn't exist + if (this.invertedIndex[term] == undefined) { + var posting = Object.create(null) + posting["_index"] = this.termIndex + this.termIndex += 1 + + for (var k = 0; k < fields.length; k++) { + posting[fields[k]] = Object.create(null) + } + + this.invertedIndex[term] = posting + } + + // add an entry for this term/fieldName/docRef to the invertedIndex + if (this.invertedIndex[term][fieldName][docRef] == undefined) { + this.invertedIndex[term][fieldName][docRef] = Object.create(null) + } + + // store all whitelisted metadata about this token in the + // inverted index + for (var l = 0; l < this.metadataWhitelist.length; l++) { + var metadataKey = this.metadataWhitelist[l], + metadata = term.metadata[metadataKey] + + if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) { + this.invertedIndex[term][fieldName][docRef][metadataKey] = [] + } + + this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata) + } + } + + } +} + +/** + * Calculates the average document length for this index + * + * @private + */ +lunr.Builder.prototype.calculateAverageFieldLengths = function () { + + var fieldRefs = Object.keys(this.fieldLengths), + numberOfFields = fieldRefs.length, + accumulator = {}, + documentsWithField = {} + + for (var i = 0; i < numberOfFields; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + field = fieldRef.fieldName + + documentsWithField[field] || (documentsWithField[field] = 0) + documentsWithField[field] += 1 + + accumulator[field] || (accumulator[field] = 0) + accumulator[field] += this.fieldLengths[fieldRef] + } + + var fields = Object.keys(this._fields) + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i] + accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName] + } + + this.averageFieldLength = accumulator +} + +/** + * Builds a vector space model of every document using lunr.Vector + * + * @private + */ +lunr.Builder.prototype.createFieldVectors = function () { + var fieldVectors = {}, + fieldRefs = Object.keys(this.fieldTermFrequencies), + fieldRefsLength = fieldRefs.length, + termIdfCache = Object.create(null) + + for (var i = 0; i < fieldRefsLength; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + fieldName = fieldRef.fieldName, + fieldLength = this.fieldLengths[fieldRef], + fieldVector = new lunr.Vector, + termFrequencies = this.fieldTermFrequencies[fieldRef], + terms = Object.keys(termFrequencies), + termsLength = terms.length + + + var fieldBoost = this._fields[fieldName].boost || 1, + docBoost = this._documents[fieldRef.docRef].boost || 1 + + for (var j = 0; j < termsLength; j++) { + var term = terms[j], + tf = termFrequencies[term], + termIndex = this.invertedIndex[term]._index, + idf, score, scoreWithPrecision + + if (termIdfCache[term] === undefined) { + idf = lunr.idf(this.invertedIndex[term], this.documentCount) + termIdfCache[term] = idf + } else { + idf = termIdfCache[term] + } + + score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf) + score *= fieldBoost + score *= docBoost + scoreWithPrecision = Math.round(score * 1000) / 1000 + // Converts 1.23456789 to 1.234. + // Reducing the precision so that the vectors take up less + // space when serialised. Doing it now so that they behave + // the same before and after serialisation. Also, this is + // the fastest approach to reducing a number's precision in + // JavaScript. + + fieldVector.insert(termIndex, scoreWithPrecision) + } + + fieldVectors[fieldRef] = fieldVector + } + + this.fieldVectors = fieldVectors +} + +/** + * Creates a token set of all tokens in the index using lunr.TokenSet + * + * @private + */ +lunr.Builder.prototype.createTokenSet = function () { + this.tokenSet = lunr.TokenSet.fromArray( + Object.keys(this.invertedIndex).sort() + ) +} + +/** + * Builds the index, creating an instance of lunr.Index. + * + * This completes the indexing process and should only be called + * once all documents have been added to the index. + * + * @returns {lunr.Index} + */ +lunr.Builder.prototype.build = function () { + this.calculateAverageFieldLengths() + this.createFieldVectors() + this.createTokenSet() + + return new lunr.Index({ + invertedIndex: this.invertedIndex, + fieldVectors: this.fieldVectors, + tokenSet: this.tokenSet, + fields: Object.keys(this._fields), + pipeline: this.searchPipeline + }) +} + +/** + * Applies a plugin to the index builder. + * + * A plugin is a function that is called with the index builder as its context. + * Plugins can be used to customise or extend the behaviour of the index + * in some way. A plugin is just a function, that encapsulated the custom + * behaviour that should be applied when building the index. + * + * The plugin function will be called with the index builder as its argument, additional + * arguments can also be passed when calling use. The function will be called + * with the index builder as its context. + * + * @param {Function} plugin The plugin to apply. + */ +lunr.Builder.prototype.use = function (fn) { + var args = Array.prototype.slice.call(arguments, 1) + args.unshift(this) + fn.apply(this, args) +} +/** + * Contains and collects metadata about a matching document. + * A single instance of lunr.MatchData is returned as part of every + * lunr.Index~Result. + * + * @constructor + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + * @property {object} metadata - A cloned collection of metadata associated with this document. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData = function (term, field, metadata) { + var clonedMetadata = Object.create(null), + metadataKeys = Object.keys(metadata || {}) + + // Cloning the metadata to prevent the original + // being mutated during match data combination. + // Metadata is kept in an array within the inverted + // index so cloning the data can be done with + // Array#slice + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + clonedMetadata[key] = metadata[key].slice() + } + + this.metadata = Object.create(null) + + if (term !== undefined) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = clonedMetadata + } +} + +/** + * An instance of lunr.MatchData will be created for every term that matches a + * document. However only one instance is required in a lunr.Index~Result. This + * method combines metadata from another instance of lunr.MatchData with this + * objects metadata. + * + * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData.prototype.combine = function (otherMatchData) { + var terms = Object.keys(otherMatchData.metadata) + + for (var i = 0; i < terms.length; i++) { + var term = terms[i], + fields = Object.keys(otherMatchData.metadata[term]) + + if (this.metadata[term] == undefined) { + this.metadata[term] = Object.create(null) + } + + for (var j = 0; j < fields.length; j++) { + var field = fields[j], + keys = Object.keys(otherMatchData.metadata[term][field]) + + if (this.metadata[term][field] == undefined) { + this.metadata[term][field] = Object.create(null) + } + + for (var k = 0; k < keys.length; k++) { + var key = keys[k] + + if (this.metadata[term][field][key] == undefined) { + this.metadata[term][field][key] = otherMatchData.metadata[term][field][key] + } else { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key]) + } + + } + } + } +} + +/** + * Add metadata for a term/field pair to this instance of match data. + * + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + */ +lunr.MatchData.prototype.add = function (term, field, metadata) { + if (!(term in this.metadata)) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = metadata + return + } + + if (!(field in this.metadata[term])) { + this.metadata[term][field] = metadata + return + } + + var metadataKeys = Object.keys(metadata) + + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + + if (key in this.metadata[term][field]) { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key]) + } else { + this.metadata[term][field][key] = metadata[key] + } + } +} +/** + * A lunr.Query provides a programmatic way of defining queries to be performed + * against a {@link lunr.Index}. + * + * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method + * so the query object is pre-initialized with the right index fields. + * + * @constructor + * @property {lunr.Query~Clause[]} clauses - An array of query clauses. + * @property {string[]} allFields - An array of all available fields in a lunr.Index. + */ +lunr.Query = function (allFields) { + this.clauses = [] + this.allFields = allFields +} + +/** + * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause. + * + * This allows wildcards to be added to the beginning and end of a term without having to manually do any string + * concatenation. + * + * The wildcard constants can be bitwise combined to select both leading and trailing wildcards. + * + * @constant + * @default + * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour + * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists + * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with trailing wildcard + * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING }) + * @example query term with leading and trailing wildcard + * query.term('foo', { + * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING + * }) + */ + +lunr.Query.wildcard = new String ("*") +lunr.Query.wildcard.NONE = 0 +lunr.Query.wildcard.LEADING = 1 +lunr.Query.wildcard.TRAILING = 2 + +/** + * Constants for indicating what kind of presence a term must have in matching documents. + * + * @constant + * @enum {number} + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with required presence + * query.term('foo', { presence: lunr.Query.presence.REQUIRED }) + */ +lunr.Query.presence = { + /** + * Term's presence in a document is optional, this is the default value. + */ + OPTIONAL: 1, + + /** + * Term's presence in a document is required, documents that do not contain + * this term will not be returned. + */ + REQUIRED: 2, + + /** + * Term's presence in a document is prohibited, documents that do contain + * this term will not be returned. + */ + PROHIBITED: 3 +} + +/** + * A single clause in a {@link lunr.Query} contains a term and details on how to + * match that term against a {@link lunr.Index}. + * + * @typedef {Object} lunr.Query~Clause + * @property {string[]} fields - The fields in an index this clause should be matched against. + * @property {number} [boost=1] - Any boost that should be applied when matching this clause. + * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be. + * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline. + * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended. + * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents. + */ + +/** + * Adds a {@link lunr.Query~Clause} to this query. + * + * Unless the clause contains the fields to be matched all fields will be matched. In addition + * a default boost of 1 is applied to the clause. + * + * @param {lunr.Query~Clause} clause - The clause to add to this query. + * @see lunr.Query~Clause + * @returns {lunr.Query} + */ +lunr.Query.prototype.clause = function (clause) { + if (!('fields' in clause)) { + clause.fields = this.allFields + } + + if (!('boost' in clause)) { + clause.boost = 1 + } + + if (!('usePipeline' in clause)) { + clause.usePipeline = true + } + + if (!('wildcard' in clause)) { + clause.wildcard = lunr.Query.wildcard.NONE + } + + if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) { + clause.term = "*" + clause.term + } + + if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) { + clause.term = "" + clause.term + "*" + } + + if (!('presence' in clause)) { + clause.presence = lunr.Query.presence.OPTIONAL + } + + this.clauses.push(clause) + + return this +} + +/** + * A negated query is one in which every clause has a presence of + * prohibited. These queries require some special processing to return + * the expected results. + * + * @returns boolean + */ +lunr.Query.prototype.isNegated = function () { + for (var i = 0; i < this.clauses.length; i++) { + if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) { + return false + } + } + + return true +} + +/** + * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause} + * to the list of clauses that make up this query. + * + * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion + * to a token or token-like string should be done before calling this method. + * + * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an + * array, each term in the array will share the same options. + * + * @param {object|object[]} term - The term(s) to add to the query. + * @param {object} [options] - Any additional properties to add to the query clause. + * @returns {lunr.Query} + * @see lunr.Query#clause + * @see lunr.Query~Clause + * @example adding a single term to a query + * query.term("foo") + * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard + * query.term("foo", { + * fields: ["title"], + * boost: 10, + * wildcard: lunr.Query.wildcard.TRAILING + * }) + * @example using lunr.tokenizer to convert a string to tokens before using them as terms + * query.term(lunr.tokenizer("foo bar")) + */ +lunr.Query.prototype.term = function (term, options) { + if (Array.isArray(term)) { + term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this) + return this + } + + var clause = options || {} + clause.term = term.toString() + + this.clause(clause) + + return this +} +lunr.QueryParseError = function (message, start, end) { + this.name = "QueryParseError" + this.message = message + this.start = start + this.end = end +} + +lunr.QueryParseError.prototype = new Error +lunr.QueryLexer = function (str) { + this.lexemes = [] + this.str = str + this.length = str.length + this.pos = 0 + this.start = 0 + this.escapeCharPositions = [] +} + +lunr.QueryLexer.prototype.run = function () { + var state = lunr.QueryLexer.lexText + + while (state) { + state = state(this) + } +} + +lunr.QueryLexer.prototype.sliceString = function () { + var subSlices = [], + sliceStart = this.start, + sliceEnd = this.pos + + for (var i = 0; i < this.escapeCharPositions.length; i++) { + sliceEnd = this.escapeCharPositions[i] + subSlices.push(this.str.slice(sliceStart, sliceEnd)) + sliceStart = sliceEnd + 1 + } + + subSlices.push(this.str.slice(sliceStart, this.pos)) + this.escapeCharPositions.length = 0 + + return subSlices.join('') +} + +lunr.QueryLexer.prototype.emit = function (type) { + this.lexemes.push({ + type: type, + str: this.sliceString(), + start: this.start, + end: this.pos + }) + + this.start = this.pos +} + +lunr.QueryLexer.prototype.escapeCharacter = function () { + this.escapeCharPositions.push(this.pos - 1) + this.pos += 1 +} + +lunr.QueryLexer.prototype.next = function () { + if (this.pos >= this.length) { + return lunr.QueryLexer.EOS + } + + var char = this.str.charAt(this.pos) + this.pos += 1 + return char +} + +lunr.QueryLexer.prototype.width = function () { + return this.pos - this.start +} + +lunr.QueryLexer.prototype.ignore = function () { + if (this.start == this.pos) { + this.pos += 1 + } + + this.start = this.pos +} + +lunr.QueryLexer.prototype.backup = function () { + this.pos -= 1 +} + +lunr.QueryLexer.prototype.acceptDigitRun = function () { + var char, charCode + + do { + char = this.next() + charCode = char.charCodeAt(0) + } while (charCode > 47 && charCode < 58) + + if (char != lunr.QueryLexer.EOS) { + this.backup() + } +} + +lunr.QueryLexer.prototype.more = function () { + return this.pos < this.length +} + +lunr.QueryLexer.EOS = 'EOS' +lunr.QueryLexer.FIELD = 'FIELD' +lunr.QueryLexer.TERM = 'TERM' +lunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE' +lunr.QueryLexer.BOOST = 'BOOST' +lunr.QueryLexer.PRESENCE = 'PRESENCE' + +lunr.QueryLexer.lexField = function (lexer) { + lexer.backup() + lexer.emit(lunr.QueryLexer.FIELD) + lexer.ignore() + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexTerm = function (lexer) { + if (lexer.width() > 1) { + lexer.backup() + lexer.emit(lunr.QueryLexer.TERM) + } + + lexer.ignore() + + if (lexer.more()) { + return lunr.QueryLexer.lexText + } +} + +lunr.QueryLexer.lexEditDistance = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.EDIT_DISTANCE) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexBoost = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.BOOST) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexEOS = function (lexer) { + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } +} + +// This matches the separator used when tokenising fields +// within a document. These should match otherwise it is +// not possible to search for some tokens within a document. +// +// It is possible for the user to change the separator on the +// tokenizer so it _might_ clash with any other of the special +// characters already used within the search string, e.g. :. +// +// This means that it is possible to change the separator in +// such a way that makes some words unsearchable using a search +// string. +lunr.QueryLexer.termSeparator = lunr.tokenizer.separator + +lunr.QueryLexer.lexText = function (lexer) { + while (true) { + var char = lexer.next() + + if (char == lunr.QueryLexer.EOS) { + return lunr.QueryLexer.lexEOS + } + + // Escape character is '\' + if (char.charCodeAt(0) == 92) { + lexer.escapeCharacter() + continue + } + + if (char == ":") { + return lunr.QueryLexer.lexField + } + + if (char == "~") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexEditDistance + } + + if (char == "^") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexBoost + } + + // "+" indicates term presence is required + // checking for length to ensure that only + // leading "+" are considered + if (char == "+" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + // "-" indicates term presence is prohibited + // checking for length to ensure that only + // leading "-" are considered + if (char == "-" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + if (char.match(lunr.QueryLexer.termSeparator)) { + return lunr.QueryLexer.lexTerm + } + } +} + +lunr.QueryParser = function (str, query) { + this.lexer = new lunr.QueryLexer (str) + this.query = query + this.currentClause = {} + this.lexemeIdx = 0 +} + +lunr.QueryParser.prototype.parse = function () { + this.lexer.run() + this.lexemes = this.lexer.lexemes + + var state = lunr.QueryParser.parseClause + + while (state) { + state = state(this) + } + + return this.query +} + +lunr.QueryParser.prototype.peekLexeme = function () { + return this.lexemes[this.lexemeIdx] +} + +lunr.QueryParser.prototype.consumeLexeme = function () { + var lexeme = this.peekLexeme() + this.lexemeIdx += 1 + return lexeme +} + +lunr.QueryParser.prototype.nextClause = function () { + var completedClause = this.currentClause + this.query.clause(completedClause) + this.currentClause = {} +} + +lunr.QueryParser.parseClause = function (parser) { + var lexeme = parser.peekLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.type) { + case lunr.QueryLexer.PRESENCE: + return lunr.QueryParser.parsePresence + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expected either a field or a term, found " + lexeme.type + + if (lexeme.str.length >= 1) { + errorMessage += " with value '" + lexeme.str + "'" + } + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } +} + +lunr.QueryParser.parsePresence = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.str) { + case "-": + parser.currentClause.presence = lunr.Query.presence.PROHIBITED + break + case "+": + parser.currentClause.presence = lunr.Query.presence.REQUIRED + break + default: + var errorMessage = "unrecognised presence operator'" + lexeme.str + "'" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term or field, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term or field, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseField = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + if (parser.query.allFields.indexOf(lexeme.str) == -1) { + var possibleFields = parser.query.allFields.map(function (f) { return "'" + f + "'" }).join(', '), + errorMessage = "unrecognised field '" + lexeme.str + "', possible fields: " + possibleFields + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.fields = [lexeme.str] + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseTerm = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + parser.currentClause.term = lexeme.str.toLowerCase() + + if (lexeme.str.indexOf("*") != -1) { + parser.currentClause.usePipeline = false + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseEditDistance = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var editDistance = parseInt(lexeme.str, 10) + + if (isNaN(editDistance)) { + var errorMessage = "edit distance must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.editDistance = editDistance + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseBoost = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var boost = parseInt(lexeme.str, 10) + + if (isNaN(boost)) { + var errorMessage = "boost must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.boost = boost + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + + /** + * export the module via AMD, CommonJS or as a browser global + * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js + */ + ;(function (root, factory) { + if (typeof define === 'function' && define.amd) { + // AMD. Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like environments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + root.lunr = factory() + } + }(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + return lunr + })) +})(); diff --git a/search/main.js b/search/main.js new file mode 100644 index 00000000..a5e469d7 --- /dev/null +++ b/search/main.js @@ -0,0 +1,109 @@ +function getSearchTermFromLocation() { + var sPageURL = window.location.search.substring(1); + var sURLVariables = sPageURL.split('&'); + for (var i = 0; i < sURLVariables.length; i++) { + var sParameterName = sURLVariables[i].split('='); + if (sParameterName[0] == 'q') { + return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20')); + } + } +} + +function joinUrl (base, path) { + if (path.substring(0, 1) === "/") { + // path starts with `/`. Thus it is absolute. + return path; + } + if (base.substring(base.length-1) === "/") { + // base ends with `/` + return base + path; + } + return base + "/" + path; +} + +function escapeHtml (value) { + return value.replace(/&/g, '&') + .replace(/"/g, '"') + .replace(//g, '>'); +} + +function formatResult (location, title, summary) { + return ''; +} + +function displayResults (results) { + var search_results = document.getElementById("mkdocs-search-results"); + while (search_results.firstChild) { + search_results.removeChild(search_results.firstChild); + } + if (results.length > 0){ + for (var i=0; i < results.length; i++){ + var result = results[i]; + var html = formatResult(result.location, result.title, result.summary); + search_results.insertAdjacentHTML('beforeend', html); + } + } else { + var noResultsText = search_results.getAttribute('data-no-results-text'); + if (!noResultsText) { + noResultsText = "No results found"; + } + search_results.insertAdjacentHTML('beforeend', '

' + noResultsText + '

'); + } +} + +function doSearch () { + var query = document.getElementById('mkdocs-search-query').value; + if (query.length > min_search_length) { + if (!window.Worker) { + displayResults(search(query)); + } else { + searchWorker.postMessage({query: query}); + } + } else { + // Clear results for short queries + displayResults([]); + } +} + +function initSearch () { + var search_input = document.getElementById('mkdocs-search-query'); + if (search_input) { + search_input.addEventListener("keyup", doSearch); + } + var term = getSearchTermFromLocation(); + if (term) { + search_input.value = term; + doSearch(); + } +} + +function onWorkerMessage (e) { + if (e.data.allowSearch) { + initSearch(); + } else if (e.data.results) { + var results = e.data.results; + displayResults(results); + } else if (e.data.config) { + min_search_length = e.data.config.min_search_length-1; + } +} + +if (!window.Worker) { + console.log('Web Worker API not supported'); + // load index in main thread + $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { + console.log('Loaded worker'); + init(); + window.postMessage = function (msg) { + onWorkerMessage({data: msg}); + }; + }).fail(function (jqxhr, settings, exception) { + console.error('Could not load worker.js'); + }); +} else { + // Wrap search in a web worker + var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); + searchWorker.postMessage({init: true}); + searchWorker.onmessage = onWorkerMessage; +} diff --git a/search/search_index.json b/search/search_index.json new file mode 100644 index 00000000..548a84b2 --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Welcome to the course: Introduction to Kebnekaise \u00b6 This material Here you will find the content of the workshop \u201cIntroduction to Kebnekaise\u201d. You can download the markdown files for the presentation as well as the exercises from https://github.com/hpc2n/intro-course Click the gren \u201cCode\u201d button Either copy the url for the repo under HTTPS and do git clone https://github.com/hpc2n/intro-course.git in a terminal window OR pick \u201cDownload zip\u201d to get a zip file with the content. Some useful links: Documentation about Linux at HPC2N: https://docs.hpc2n.umu.se/tutorials/linuxguide/ Get started guide: https://docs.hpc2n.umu.se/tutorials/quickstart/ Documentation pages at HPC2N: https://docs.hpc2n.umu.se/ Prerequisites Basic knowledge about Linux (if you need a refresher, you could take the course \u201cIntroduction to Linux\u201d which runs immediately before this course. Info and registration here: https://www.hpc2n.umu.se/events/courses/2024/fall/intro-linux . An account at SUPR and at HPC2N. You should have already been contacted about getting these if you did not have them already. Content This course aims to give a brief, but comprehensive introduction to Kebnekaise. You will learn about HPC2N, HPC, and Kebnekaise hardware How to use our systems: Logging in & editors The File System The Module System Compiling and linking The Batch System Simple examples (batch system) Application examples (batch system) This course will consist of lectures and type-alongs, as well as a few exercises where you get to try out what you have just learned. Instructors Birgitte Bryds\u00f6, HPC2N Pedro Ojeda-May, HPC2N Preliminary schedule \u00b6 Time Topic Activity 11:15 Welcome+Syllabus 11:20 Introduction to Kebnekaise and HPC2N Lecture 11:45 Projects and accounts Lecture 11:50 Logging in & editors Lecture+exercise 12:05 The File System Lecture+code along 12:15 LUNCH BREAK 13:15 The Module System Lecture+code along+exercise 13:35 Compiling Lecture+code along+exercise 13:50 The Batch System Lecture+code along 14:10 Simple Examples Lecture+exercises 14:45 COFFEE BREAK 15:00 Application Examples Lecture+code along+exercises 16:40 Questions+Summary 17:00 END OF COURSE","title":"Home"},{"location":"#welcome__to__the__course__introduction__to__kebnekaise","text":"This material Here you will find the content of the workshop \u201cIntroduction to Kebnekaise\u201d. You can download the markdown files for the presentation as well as the exercises from https://github.com/hpc2n/intro-course Click the gren \u201cCode\u201d button Either copy the url for the repo under HTTPS and do git clone https://github.com/hpc2n/intro-course.git in a terminal window OR pick \u201cDownload zip\u201d to get a zip file with the content. Some useful links: Documentation about Linux at HPC2N: https://docs.hpc2n.umu.se/tutorials/linuxguide/ Get started guide: https://docs.hpc2n.umu.se/tutorials/quickstart/ Documentation pages at HPC2N: https://docs.hpc2n.umu.se/ Prerequisites Basic knowledge about Linux (if you need a refresher, you could take the course \u201cIntroduction to Linux\u201d which runs immediately before this course. Info and registration here: https://www.hpc2n.umu.se/events/courses/2024/fall/intro-linux . An account at SUPR and at HPC2N. You should have already been contacted about getting these if you did not have them already. Content This course aims to give a brief, but comprehensive introduction to Kebnekaise. You will learn about HPC2N, HPC, and Kebnekaise hardware How to use our systems: Logging in & editors The File System The Module System Compiling and linking The Batch System Simple examples (batch system) Application examples (batch system) This course will consist of lectures and type-alongs, as well as a few exercises where you get to try out what you have just learned. Instructors Birgitte Bryds\u00f6, HPC2N Pedro Ojeda-May, HPC2N","title":"Welcome to the course: Introduction to Kebnekaise"},{"location":"#preliminary__schedule","text":"Time Topic Activity 11:15 Welcome+Syllabus 11:20 Introduction to Kebnekaise and HPC2N Lecture 11:45 Projects and accounts Lecture 11:50 Logging in & editors Lecture+exercise 12:05 The File System Lecture+code along 12:15 LUNCH BREAK 13:15 The Module System Lecture+code along+exercise 13:35 Compiling Lecture+code along+exercise 13:50 The Batch System Lecture+code along 14:10 Simple Examples Lecture+exercises 14:45 COFFEE BREAK 15:00 Application Examples Lecture+code along+exercises 16:40 Questions+Summary 17:00 END OF COURSE","title":"Preliminary schedule"},{"location":"batch/","text":"The Batch System (SLURM) \u00b6 Objectives Get information about what a batch system is and which one is used at HPC2N. Learn basic commands for the batch system used at HPC2N. How to create a basic batch script. Managing your job: submitting, status, cancelling, checking\u2026 Learn how to allocate specific parts of Kebnekaise: skylake, zen3/zen4, GPUs\u2026 Large/long/parallel jobs must be run through the batch system. Kebnekaise is running Slurm . Slurm is an Open Source job scheduler, which provides three key functions. Keeps track of available system resources. Enforces local system resource usage and job scheduling policies. Manages a job queue, distributing work across resources according to policies. In order to run a batch job, you need to create and submit a SLURM submit file (also called a batch submit file, a batch script, or a job script). Note Guides and documentation for the batch system at HPC2N here at: HPC2N\u2019s batch system documentation . Basic commands \u00b6 Using a job script is often recommended. If you ask for the resources on the command line, you will wait for the program to run before you can use the window again (unless you can send it to the background with &). If you use a job script you have an easy record of the commands you used, to reuse or edit for later use. Note When you submit a job, the system will return the Job ID. You can also get it with squeue -me . See below. In the following, JOBSCRIPT is the name you have given your job script and JOBID is the job ID for your job, assigned by Slurm. USERNAME is your username. Submit job : sbatch JOBSCRIPT Get list of your jobs : squeue -u USERNAME or squeue --me Give the Slurm commands on the command line : srun commands-for-your-job/program Check on a specific job : scontrol show job JOBID Delete a specific job : scancel JOBID Delete all your own jobs : scancel -u USERNAME Request an interactive allocation : salloc -A PROJECT-ID ....... Note that you will still be on the login node when the prompt returns and you MUST preface with srun to run on the allocated resources. I.e. srun MYPROGRAM Get more detailed info about jobs : sacct -l -j JOBID -o jobname,NTasks,nodelist,MaxRSS,MaxVMSize More flags etc. can be found with man sacct The output will be very wide. To view in a friendlier format, use sacct -l -j JOBID -o jobname,NTasks,nodelist,MaxRSS,MaxVMSize | less -S this makes it sideways scrollable, using the left/right arrow key Web url with graphical info about a job: job-usage JOBID More information: man sbatch , man srun , man .... Example Submit job with sbatch b-an01 [ ~ ] $ sbatch simple.sh Submitted batch job 27774852 Check status with squeue --me b-an01 [ ~ ] $ squeue --me JOBID PARTITION NAME USER ST TIME NODES NODELIST ( REASON ) 27774852 cpu_zen4 simple.s bbrydsoe R 0 :00 1 b-cn1701 Submit several jobs (here several instances of the same), check on the status b-an01 [ ~ ] $ sbatch simple.sh Submitted batch job 27774872 b-an01 [ ~ ] $ sbatch simple.sh Submitted batch job 27774873 b-an01 [ ~ ] $ sbatch simple.sh Submitted batch job 27774874 b-an01 [ ~ ] $ squeue --me JOBID PARTITION NAME USER ST TIME NODES NODELIST ( REASON ) 27774873 cpu_zen4 simple.s bbrydsoe R 0 :02 1 b-cn1702 27774874 cpu_zen4 simple.s bbrydsoe R 0 :02 1 b-cn1702 27774872 cpu_zen4 simple.s bbrydsoe CG 0 :04 1 b-cn1702 The status \u201cR\u201d means it is running. \u201cCG\u201d means completing. When a job is pending it has the state \u201cPD\u201d. In these examples the jobs all ended up on nodes in the partition cpu_zen4. We will soon talk more about different types of nodes. Job scripts and output \u00b6 The official name for batch scripts in Slurm is Job Submission Files, but here we will use both names interchangeably. If you search the internet, you will find several other names used, including Slurm submit file, batch submit file, batch script, job script. A job submission file can contain any of the commands that you would otherwise issue yourself from the command line. It is, for example, possible to both compile and run a program and also to set any necessary environment values (though remember that Slurm exports the environment variables in your shell per default, so you can also just set them all there before submitting the job). Note The results from compiling or running your programs can generally be seen after the job has completed, though as Slurm will write to the output file during the run, some results will be available quicker. Outputs and any errors will per default be placed in the directory you are running from, though this can be changed. Note This directory should preferrably be placed under your project storage, since your home directory only has 25 GB of space. The output file from the job run will default be named slurm-JOBID.out . It will contain both output as well as any errors. You can look at the content with vi , nano , emacs , cat , less \u2026 The exception is if your program creates its own output files, or if you name the output file(s) differently within your jobscript. Note You can use Slurm commands within your job script to split the error and output in separate files, and name them as you want. It is highly recommended to include the environment variable %J (the job ID) in the name, as that is an easy way to get a new name for each time you run the script and thus avoiding the previous output being overwritten. Example, using the environment variable %J : Error file: #SBATCH --error=job.%J.err Output file: #SBATCH --output=job.%J.out Job scripts \u00b6 A job submission file can either be very simple, with most of the job attributes specified on the command line, or it may consist of several Slurm directives, comments and executable statements. A Slurm directive provides a way of specifying job attributes in addition to the command line options. Naming : You can name your script anything, including the suffix. It does not matter. Just name it something that makes sense to you and helps you remember what the script is for. The standard is to name it with a suffix of .sbatch or .sh . Simple, serial job script #!/bin/bash # The name of the account you are running in, mandatory. #SBATCH -A hpc2nXXXX-YYY # Request resources - here for a serial job # tasks per core is 1 as default (can be changed with ``-c``) #SBATCH -n 1 # Request runtime for the job (HHH:MM:SS) where 168 hours is the maximum. Here asking for 15 min. #SBATCH --time=00:15:00 # Clear the environment from any previously loaded modules module purge > /dev/null 2 > & 1 # Load the module environment suitable for the job - here foss/2022b module load foss/2022b # And finally run the serial jobs ./my_serial_program Note You have to always include #!/bin/bash at the beginning of the script, since bash is the only supported shell. Some things may work under other shells, but not everything. All Slurm directives start with #SBATCH . One (or more) # in front of a text line means it is a comment, with the exception of the string #SBATCH . In order to comment out the Slurm directives, you need to put one more # in front of the #SBATCH . It is important to use capital letters for #SBATCH . Otherwise the line will be considered a comment, and ignored. Let us go through the most commonly used arguments: -A PROJ-ID : The project that should be accounted. It is a simple conversion from the SUPR project id. You can also find your project account with the command projinfo . The PROJ-ID argument is of the form hpc2nXXXX-YYY (HPC2N local project) -N : number of nodes. If this is not given, enough will be allocated to fullfill the requirements of -n and/or -c. A range can be given. If you ask for, say, 1-1, then you will get 1 and only 1 node, no matter what you ask for otherwise. It will also assure that all the processors will be allocated on the same node. -n : number of tasks. -c : cores per task. Request that a specific number of cores be allocated to each task. This can be useful if the job is multi-threaded and requires more than one core per task for optimal performance. The default is one core per task. Simple MPI program #!/bin/bash # The name of the account you are running in, mandatory. #SBATCH -A hpc2nXXXX-YYY # Request resources - here for eight MPI tasks #SBATCH -n 8 # Request runtime for the job (HHH:MM:SS) where 168 hours is the maximum. Here asking for 15 min. #SBATCH --time=00:15:00 # Clear the environment from any previously loaded modules module purge > /dev/null 2 > & 1 # Load the module environment suitable for the job - here foss/2022b module load foss/2022b # And finally run the job - use srun for MPI jobs, but not for serial jobs srun ./my_mpi_program Exercises \u00b6 If you have not already done so, clone the material from the website https://github.com/hpc2n/intro-course : Change to the storage area you created under /proj/nobackup/intro-hpc2n/ . Clone the material: git clone https://github.com/hpc2n/intro-course.git Change to the subdirectory with the exercises: cd intro-course/exercises/simple You will now find several small programs and batch scripts which are used in this section and the next, \u201cSimple examples\u201d. In this section, we are just going to try submitting a few jobs, checking their status, cancelling a job, and looking at the output. Preparations Load the module foss/2022b ( ml foss/2022b ) on the regular login node. This module is available on all nodes. Compile the following programs: hello.c , mpi_hello.c , mpi_greeting.c , and mpi_hi.c gcc -o hello hello.c mpicc -o mpi_hello mpi_hello.c mpicc -o mpi_greeting mpi_greeting.c mpicc -o mpi_hi mpi_hi.c If you compiled and named the executables as above, you should be able to submit the following batch scripts directly: simple.sh , mpi_greeting.sh , mpi_hello.sh , mpi_hi.sh , multiple-parallel-sequential.sh , multiple-parallel.sh , or multiple-parallel-simultaneous.sh . Exercise: sbatch and squeue Submit ( sbatch ) one of the batch scripts listed in 3. under preparations. Check with squeue --me if it is running, pending, or completing. Exercise: sbatch and scontrol show job Submit a few instances of multiple-parallel.sh and multiple-parallel-sequential.sh (so they do not finish running before you have time to check on them). Do scontrol show job JOBID on one or more of the job IDs. You should be able to see node assigned (unless the job has not yet had one allocated), expected runtime, etc. If the job is running, you can see how long it has run. You will also get paths to submit directory etc. Exercise: sbatch and scancel Submit a few instances of multiple-parallel.sh and multiple-parallel-sequential.sh (so they do not finish running before you have time to check on them). Do squeue --me and see the jobs listed. Pick one and do scancel JOBID on it. Do squeue --me again to see it is no longer there. Exercise: check output Use nano to open one of the output files slurm-JOBID.out . Try adding #SBATCH --error=job.%J.err and #SBATCH --output=job.%J.out to one of the batch scripts (you can edit it with nano ). Submit the batch script again. See that the expected files get created. Using the different parts of Kebnekaise \u00b6 As mentioned under the introduction, Kebnekaise is a very heterogeneous system, comprised of several different types of CPUs and GPUs. The batch system reflects these several different types of resources. At the top we have partitions, which are similar to queues. Each partition is made up of a specific set of nodes. At HPC2N we have three classes of partitions, one for CPU-only nodes, one for GPU nodes and one for large memory nodes. Each node type also has a set of features that can be used to select which node(s) the job should run on. The three types of nodes also have corresponding resources one must apply for in SUPR to be able to use them. While Kebnekaise has multiple partitions, one for each major type of resource, there is only a single partition, batch , that users can submit jobs to. The system then figures out which partition(s) the job should be sent to, based on the requested features. Node overview The \u201cType\u201d can be used if you need a specific type of node. More about that later. CPU-only nodes CPU Memory/core number nodes Type 2 x 14 core Intel broadwell 4460 MB 48 broadwell (intel_cpu) 2 x 14 core Intel skylake 6785 MB 52 skylake (intel_cpu) 2 x 64 core AMD zen3 8020 MB 1 zen3 (amd_cpu) 2 x 128 core AMD zen4 2516 MB 8 zen4 (amd_cpu) GPU enabled nodes CPU Memory/core GPU card number nodes Type 2 x 14 core Intel broadwell 9000 MB 2 x Nvidia A40 4 a40 2 x 14 core Intel skylake 6785 MB 2 x Nvidia V100 10 v100 2 x 24 core AMD zen3 10600 MB 2 x Nvidia A100 2 a100 2 x 24 core AMD zen3 10600 MB 2 x AMD MI100 1 mi100 2 x 24 core AMD zen4 6630 MB 2 x Nvidia A6000 1 a6000 2 x 24 core AMD zen4 6630 MB 2 x Nvidia L40s 10 l40s 2 x 48 core AMD zen4 6630 MB 4 x Nvidia H100 SXM5 2 h100 Large memory nodes CPU Memory/core number nodes Type 4 x 18 core Intel broadwell 41666 MB 8 largemem Requesting features \u00b6 To make it possible to target nodes in more detail there are a couple of features defined on each group of nodes. To select a feature one can use the -C option to sbatch or salloc . This sets constraints on the job. There are several reasons why one might want to do that, including for benchmarks, to be able to replicate results (in some cases), because specific modules are only available for certain architectures, etc. To constrain a job to a certain feature, use #SBATCH -C Type Note Features can be combined using \u201cand\u201d ( & ) or \u201cor\u201d ( | ). They should be wrapped in ' \u2019s. Example: #SBATCH -C 'zen3|zen4' List of constraints: For selecting type of CPU Type is: intel_cpu broadwell skylake amd_cpu zen3 zen4 For selecting type of GPU Type is: v100 a40 a6000 a100 l40s h100 mi100 For GPUs, the above GPU list of constraints can be used either as a specifier to --gpu=type:number or as a constraint together with an unspecified gpu request --gpu=number . For selecting GPUs with certain features Type is: nvidia_gpu (Any Nvidia GPU) amd_gpu (Any AMD GPU) GPU_SP (GPU with single precision capability) GPU_DP (GPU with double precision capability) GPU_AI (GPU with AI features, like half precisions and lower) GPU_ML (GPU with ML features, like half precisions and lower) For selecting large memory nodes Type is: largemem Examples, constraints \u00b6 Only nodes with Zen4 #SBATCH -C zen4 Nodes with a combination of features: a Zen4 CPU and a GPU with AI features #SBATCH -C 'zen4&GPU_AI' Nodes with either a Zen3 CPU or a Zen4 CPU #SBATCH -C 'zen3|zen4' Examples, requesting GPUs \u00b6 To use GPU resources one has to explicitly ask for one or more GPUs. Requests for GPUs can be done either in total for the job or per node of the job. Ask for one GPU of any kind #SBATCH --gpus=1 Another way to ask for one GPU of any kind #SBATCH --gpus-per-node=1 Asking for a specific type of GPU As mentioned before, for GPUs, constraints can be used either as a specifier to --gpu=type:number or as a constraint together with an unspecified gpu request --gpu=number . #SBATCH --gpus=Type:NUMBER where Type is, as mentioned: v100 a40 a6000 a100 l40s h100 mi100 Simple GPU Job - V100 #!/bin/bash #SBATCH -A hpc2nXXXX-YYY # Expected time for job to complete #SBATCH --time=00:10:00 # Number of GPU cards needed. Here asking for 2 V100 cards #SBATCH --gpu=v100:2 # Clear the environment from any previously loaded modules module purge > /dev/null 2 > & 1 # Load modules needed for your program - here fosscuda/2021b ml fosscuda/2021b ./my-gpu-program Important The course project has the following project ID: hpc2n2024-084 In order to use it in a batch job, add this to the batch script: #SBATCH -A hpc2n2024-084 We have a storage project linked to the compute project: intro-hpc2n . You find it in /proj/nobackup/intro-hpc2n . Remember to create your own directory under it. Keypoints To submit a job, you first need to create a batch submit script, which you then submit with sbatch SUBMIT-SCRIPT . You can get a list of your running and pending jobs with squeue --me . Kebnekaise has many different nodes, both CPU and GPU. It is possible to constrain the the job to run only on specific types of nodes. If your job is an MPI job, you need to use srun in front of your executable in the batch script (unless you use software which handles the parallelization itself).","title":"The Batch System"},{"location":"batch/#the__batch__system__slurm","text":"Objectives Get information about what a batch system is and which one is used at HPC2N. Learn basic commands for the batch system used at HPC2N. How to create a basic batch script. Managing your job: submitting, status, cancelling, checking\u2026 Learn how to allocate specific parts of Kebnekaise: skylake, zen3/zen4, GPUs\u2026 Large/long/parallel jobs must be run through the batch system. Kebnekaise is running Slurm . Slurm is an Open Source job scheduler, which provides three key functions. Keeps track of available system resources. Enforces local system resource usage and job scheduling policies. Manages a job queue, distributing work across resources according to policies. In order to run a batch job, you need to create and submit a SLURM submit file (also called a batch submit file, a batch script, or a job script). Note Guides and documentation for the batch system at HPC2N here at: HPC2N\u2019s batch system documentation .","title":"The Batch System (SLURM)"},{"location":"batch/#basic__commands","text":"Using a job script is often recommended. If you ask for the resources on the command line, you will wait for the program to run before you can use the window again (unless you can send it to the background with &). If you use a job script you have an easy record of the commands you used, to reuse or edit for later use. Note When you submit a job, the system will return the Job ID. You can also get it with squeue -me . See below. In the following, JOBSCRIPT is the name you have given your job script and JOBID is the job ID for your job, assigned by Slurm. USERNAME is your username. Submit job : sbatch JOBSCRIPT Get list of your jobs : squeue -u USERNAME or squeue --me Give the Slurm commands on the command line : srun commands-for-your-job/program Check on a specific job : scontrol show job JOBID Delete a specific job : scancel JOBID Delete all your own jobs : scancel -u USERNAME Request an interactive allocation : salloc -A PROJECT-ID ....... Note that you will still be on the login node when the prompt returns and you MUST preface with srun to run on the allocated resources. I.e. srun MYPROGRAM Get more detailed info about jobs : sacct -l -j JOBID -o jobname,NTasks,nodelist,MaxRSS,MaxVMSize More flags etc. can be found with man sacct The output will be very wide. To view in a friendlier format, use sacct -l -j JOBID -o jobname,NTasks,nodelist,MaxRSS,MaxVMSize | less -S this makes it sideways scrollable, using the left/right arrow key Web url with graphical info about a job: job-usage JOBID More information: man sbatch , man srun , man .... Example Submit job with sbatch b-an01 [ ~ ] $ sbatch simple.sh Submitted batch job 27774852 Check status with squeue --me b-an01 [ ~ ] $ squeue --me JOBID PARTITION NAME USER ST TIME NODES NODELIST ( REASON ) 27774852 cpu_zen4 simple.s bbrydsoe R 0 :00 1 b-cn1701 Submit several jobs (here several instances of the same), check on the status b-an01 [ ~ ] $ sbatch simple.sh Submitted batch job 27774872 b-an01 [ ~ ] $ sbatch simple.sh Submitted batch job 27774873 b-an01 [ ~ ] $ sbatch simple.sh Submitted batch job 27774874 b-an01 [ ~ ] $ squeue --me JOBID PARTITION NAME USER ST TIME NODES NODELIST ( REASON ) 27774873 cpu_zen4 simple.s bbrydsoe R 0 :02 1 b-cn1702 27774874 cpu_zen4 simple.s bbrydsoe R 0 :02 1 b-cn1702 27774872 cpu_zen4 simple.s bbrydsoe CG 0 :04 1 b-cn1702 The status \u201cR\u201d means it is running. \u201cCG\u201d means completing. When a job is pending it has the state \u201cPD\u201d. In these examples the jobs all ended up on nodes in the partition cpu_zen4. We will soon talk more about different types of nodes.","title":"Basic commands"},{"location":"batch/#job__scripts__and__output","text":"The official name for batch scripts in Slurm is Job Submission Files, but here we will use both names interchangeably. If you search the internet, you will find several other names used, including Slurm submit file, batch submit file, batch script, job script. A job submission file can contain any of the commands that you would otherwise issue yourself from the command line. It is, for example, possible to both compile and run a program and also to set any necessary environment values (though remember that Slurm exports the environment variables in your shell per default, so you can also just set them all there before submitting the job). Note The results from compiling or running your programs can generally be seen after the job has completed, though as Slurm will write to the output file during the run, some results will be available quicker. Outputs and any errors will per default be placed in the directory you are running from, though this can be changed. Note This directory should preferrably be placed under your project storage, since your home directory only has 25 GB of space. The output file from the job run will default be named slurm-JOBID.out . It will contain both output as well as any errors. You can look at the content with vi , nano , emacs , cat , less \u2026 The exception is if your program creates its own output files, or if you name the output file(s) differently within your jobscript. Note You can use Slurm commands within your job script to split the error and output in separate files, and name them as you want. It is highly recommended to include the environment variable %J (the job ID) in the name, as that is an easy way to get a new name for each time you run the script and thus avoiding the previous output being overwritten. Example, using the environment variable %J : Error file: #SBATCH --error=job.%J.err Output file: #SBATCH --output=job.%J.out","title":"Job scripts and output"},{"location":"batch/#job__scripts","text":"A job submission file can either be very simple, with most of the job attributes specified on the command line, or it may consist of several Slurm directives, comments and executable statements. A Slurm directive provides a way of specifying job attributes in addition to the command line options. Naming : You can name your script anything, including the suffix. It does not matter. Just name it something that makes sense to you and helps you remember what the script is for. The standard is to name it with a suffix of .sbatch or .sh . Simple, serial job script #!/bin/bash # The name of the account you are running in, mandatory. #SBATCH -A hpc2nXXXX-YYY # Request resources - here for a serial job # tasks per core is 1 as default (can be changed with ``-c``) #SBATCH -n 1 # Request runtime for the job (HHH:MM:SS) where 168 hours is the maximum. Here asking for 15 min. #SBATCH --time=00:15:00 # Clear the environment from any previously loaded modules module purge > /dev/null 2 > & 1 # Load the module environment suitable for the job - here foss/2022b module load foss/2022b # And finally run the serial jobs ./my_serial_program Note You have to always include #!/bin/bash at the beginning of the script, since bash is the only supported shell. Some things may work under other shells, but not everything. All Slurm directives start with #SBATCH . One (or more) # in front of a text line means it is a comment, with the exception of the string #SBATCH . In order to comment out the Slurm directives, you need to put one more # in front of the #SBATCH . It is important to use capital letters for #SBATCH . Otherwise the line will be considered a comment, and ignored. Let us go through the most commonly used arguments: -A PROJ-ID : The project that should be accounted. It is a simple conversion from the SUPR project id. You can also find your project account with the command projinfo . The PROJ-ID argument is of the form hpc2nXXXX-YYY (HPC2N local project) -N : number of nodes. If this is not given, enough will be allocated to fullfill the requirements of -n and/or -c. A range can be given. If you ask for, say, 1-1, then you will get 1 and only 1 node, no matter what you ask for otherwise. It will also assure that all the processors will be allocated on the same node. -n : number of tasks. -c : cores per task. Request that a specific number of cores be allocated to each task. This can be useful if the job is multi-threaded and requires more than one core per task for optimal performance. The default is one core per task. Simple MPI program #!/bin/bash # The name of the account you are running in, mandatory. #SBATCH -A hpc2nXXXX-YYY # Request resources - here for eight MPI tasks #SBATCH -n 8 # Request runtime for the job (HHH:MM:SS) where 168 hours is the maximum. Here asking for 15 min. #SBATCH --time=00:15:00 # Clear the environment from any previously loaded modules module purge > /dev/null 2 > & 1 # Load the module environment suitable for the job - here foss/2022b module load foss/2022b # And finally run the job - use srun for MPI jobs, but not for serial jobs srun ./my_mpi_program","title":"Job scripts"},{"location":"batch/#exercises","text":"If you have not already done so, clone the material from the website https://github.com/hpc2n/intro-course : Change to the storage area you created under /proj/nobackup/intro-hpc2n/ . Clone the material: git clone https://github.com/hpc2n/intro-course.git Change to the subdirectory with the exercises: cd intro-course/exercises/simple You will now find several small programs and batch scripts which are used in this section and the next, \u201cSimple examples\u201d. In this section, we are just going to try submitting a few jobs, checking their status, cancelling a job, and looking at the output. Preparations Load the module foss/2022b ( ml foss/2022b ) on the regular login node. This module is available on all nodes. Compile the following programs: hello.c , mpi_hello.c , mpi_greeting.c , and mpi_hi.c gcc -o hello hello.c mpicc -o mpi_hello mpi_hello.c mpicc -o mpi_greeting mpi_greeting.c mpicc -o mpi_hi mpi_hi.c If you compiled and named the executables as above, you should be able to submit the following batch scripts directly: simple.sh , mpi_greeting.sh , mpi_hello.sh , mpi_hi.sh , multiple-parallel-sequential.sh , multiple-parallel.sh , or multiple-parallel-simultaneous.sh . Exercise: sbatch and squeue Submit ( sbatch ) one of the batch scripts listed in 3. under preparations. Check with squeue --me if it is running, pending, or completing. Exercise: sbatch and scontrol show job Submit a few instances of multiple-parallel.sh and multiple-parallel-sequential.sh (so they do not finish running before you have time to check on them). Do scontrol show job JOBID on one or more of the job IDs. You should be able to see node assigned (unless the job has not yet had one allocated), expected runtime, etc. If the job is running, you can see how long it has run. You will also get paths to submit directory etc. Exercise: sbatch and scancel Submit a few instances of multiple-parallel.sh and multiple-parallel-sequential.sh (so they do not finish running before you have time to check on them). Do squeue --me and see the jobs listed. Pick one and do scancel JOBID on it. Do squeue --me again to see it is no longer there. Exercise: check output Use nano to open one of the output files slurm-JOBID.out . Try adding #SBATCH --error=job.%J.err and #SBATCH --output=job.%J.out to one of the batch scripts (you can edit it with nano ). Submit the batch script again. See that the expected files get created.","title":"Exercises"},{"location":"batch/#using__the__different__parts__of__kebnekaise","text":"As mentioned under the introduction, Kebnekaise is a very heterogeneous system, comprised of several different types of CPUs and GPUs. The batch system reflects these several different types of resources. At the top we have partitions, which are similar to queues. Each partition is made up of a specific set of nodes. At HPC2N we have three classes of partitions, one for CPU-only nodes, one for GPU nodes and one for large memory nodes. Each node type also has a set of features that can be used to select which node(s) the job should run on. The three types of nodes also have corresponding resources one must apply for in SUPR to be able to use them. While Kebnekaise has multiple partitions, one for each major type of resource, there is only a single partition, batch , that users can submit jobs to. The system then figures out which partition(s) the job should be sent to, based on the requested features. Node overview The \u201cType\u201d can be used if you need a specific type of node. More about that later. CPU-only nodes CPU Memory/core number nodes Type 2 x 14 core Intel broadwell 4460 MB 48 broadwell (intel_cpu) 2 x 14 core Intel skylake 6785 MB 52 skylake (intel_cpu) 2 x 64 core AMD zen3 8020 MB 1 zen3 (amd_cpu) 2 x 128 core AMD zen4 2516 MB 8 zen4 (amd_cpu) GPU enabled nodes CPU Memory/core GPU card number nodes Type 2 x 14 core Intel broadwell 9000 MB 2 x Nvidia A40 4 a40 2 x 14 core Intel skylake 6785 MB 2 x Nvidia V100 10 v100 2 x 24 core AMD zen3 10600 MB 2 x Nvidia A100 2 a100 2 x 24 core AMD zen3 10600 MB 2 x AMD MI100 1 mi100 2 x 24 core AMD zen4 6630 MB 2 x Nvidia A6000 1 a6000 2 x 24 core AMD zen4 6630 MB 2 x Nvidia L40s 10 l40s 2 x 48 core AMD zen4 6630 MB 4 x Nvidia H100 SXM5 2 h100 Large memory nodes CPU Memory/core number nodes Type 4 x 18 core Intel broadwell 41666 MB 8 largemem","title":"Using the different parts of Kebnekaise"},{"location":"batch/#requesting__features","text":"To make it possible to target nodes in more detail there are a couple of features defined on each group of nodes. To select a feature one can use the -C option to sbatch or salloc . This sets constraints on the job. There are several reasons why one might want to do that, including for benchmarks, to be able to replicate results (in some cases), because specific modules are only available for certain architectures, etc. To constrain a job to a certain feature, use #SBATCH -C Type Note Features can be combined using \u201cand\u201d ( & ) or \u201cor\u201d ( | ). They should be wrapped in ' \u2019s. Example: #SBATCH -C 'zen3|zen4' List of constraints: For selecting type of CPU Type is: intel_cpu broadwell skylake amd_cpu zen3 zen4 For selecting type of GPU Type is: v100 a40 a6000 a100 l40s h100 mi100 For GPUs, the above GPU list of constraints can be used either as a specifier to --gpu=type:number or as a constraint together with an unspecified gpu request --gpu=number . For selecting GPUs with certain features Type is: nvidia_gpu (Any Nvidia GPU) amd_gpu (Any AMD GPU) GPU_SP (GPU with single precision capability) GPU_DP (GPU with double precision capability) GPU_AI (GPU with AI features, like half precisions and lower) GPU_ML (GPU with ML features, like half precisions and lower) For selecting large memory nodes Type is: largemem","title":"Requesting features"},{"location":"batch/#examples__constraints","text":"Only nodes with Zen4 #SBATCH -C zen4 Nodes with a combination of features: a Zen4 CPU and a GPU with AI features #SBATCH -C 'zen4&GPU_AI' Nodes with either a Zen3 CPU or a Zen4 CPU #SBATCH -C 'zen3|zen4'","title":"Examples, constraints"},{"location":"batch/#examples__requesting__gpus","text":"To use GPU resources one has to explicitly ask for one or more GPUs. Requests for GPUs can be done either in total for the job or per node of the job. Ask for one GPU of any kind #SBATCH --gpus=1 Another way to ask for one GPU of any kind #SBATCH --gpus-per-node=1 Asking for a specific type of GPU As mentioned before, for GPUs, constraints can be used either as a specifier to --gpu=type:number or as a constraint together with an unspecified gpu request --gpu=number . #SBATCH --gpus=Type:NUMBER where Type is, as mentioned: v100 a40 a6000 a100 l40s h100 mi100 Simple GPU Job - V100 #!/bin/bash #SBATCH -A hpc2nXXXX-YYY # Expected time for job to complete #SBATCH --time=00:10:00 # Number of GPU cards needed. Here asking for 2 V100 cards #SBATCH --gpu=v100:2 # Clear the environment from any previously loaded modules module purge > /dev/null 2 > & 1 # Load modules needed for your program - here fosscuda/2021b ml fosscuda/2021b ./my-gpu-program Important The course project has the following project ID: hpc2n2024-084 In order to use it in a batch job, add this to the batch script: #SBATCH -A hpc2n2024-084 We have a storage project linked to the compute project: intro-hpc2n . You find it in /proj/nobackup/intro-hpc2n . Remember to create your own directory under it. Keypoints To submit a job, you first need to create a batch submit script, which you then submit with sbatch SUBMIT-SCRIPT . You can get a list of your running and pending jobs with squeue --me . Kebnekaise has many different nodes, both CPU and GPU. It is possible to constrain the the job to run only on specific types of nodes. If your job is an MPI job, you need to use srun in front of your executable in the batch script (unless you use software which handles the parallelization itself).","title":"Examples, requesting GPUs"},{"location":"compilers/","text":"Compiling and Linking with Libraries \u00b6 Objectives Learn about the compilers at HPC2N How to load the compiler toolchains How to use the compilers What are the popular flags How to link with libraries. Installed compilers \u00b6 There are compilers available for Fortran 77, Fortran 90, Fortran 95, C, and C++. The compilers can produce both general-purpose code and architecture-specific optimized code to improve performance (loop-level optimizations, inter-procedural analysis and cache optimizations). Loading compilers \u00b6 Note You need to load a compiler suite (and possibly libraries, depending on what you need) before you can compile and link. Use ml av to get a list of available compiler toolchains as mentioned in the modules - compiler toolchains section. You load a compiler toolchain the same way you load any other module. They are always available directly, without the need to load prerequisites first. Hint Code-along! Example: Loading foss/2023b This compiler toolchain contains: GCC/13.2.0 , BLAS (with LAPACK ), ScaLAPACK , and FFTW . b-an01 [ ~ ] $ ml foss/2023b b-an01 [ ~ ] $ ml Currently Loaded Modules: 1 ) snicenvironment ( S ) 7 ) numactl/2.0.16 13 ) libevent/2.1.12 19 ) FlexiBLAS/3.3.1 2 ) systemdefault ( S ) 8 ) XZ/5.4.4 14 ) UCX/1.15.0 20 ) FFTW/3.3.10 3 ) GCCcore/13.2.0 9 ) libxml2/2.11.5 15 ) PMIx/4.2.6 21 ) FFTW.MPI/3.3.10 4 ) zlib/1.2.13 10 ) libpciaccess/0.17 16 ) UCC/1.2.0 22 ) ScaLAPACK/2.2.0-fb 5 ) binutils/2.40 11 ) hwloc/2.9.2 17 ) OpenMPI/4.1.6 23 ) foss/2023b 6 ) GCC/13.2.0 12 ) OpenSSL/1.1 18 ) OpenBLAS/0.3.24 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ Compiling \u00b6 Note OpenMP : All compilers has this included, so it is enough to load the module for a specific compiler toolchain and then add the appropriate flag. Note If you do not name the executable (with the flag -o SOMENAME , it will be named a.out as default. This also means that the next time you compile something, if you also do not name that executable, it will overwrite the previous a.out file. Compiling with GCC \u00b6 Language Compiler name MPI Fortran77 gfortran mpif77 Fortran90 gfortran mpif90 Fortran95 gfortran N/A C gcc mpicc C++ g++ mpiCC In order to access the MPI compilers, load a compiler toolchain which contains an MPI library . Hint Code-along! Example: compiling a C program You can find the file hello.c in the exercises directory, in the subdirectory \u201csimple\u201d. Or you can download it here: hello.c . In this example we compile the C program hello.c and name the output (the executable) hello . b-an01 [ ~ ] $ gcc hello.c -o hello You can run the executable with ./hello Example: compiling an MPI C program You can find the file mpi_hello.c in the exercises directory, in the subdirectory \u201csimple\u201d. Or you can download it here: mpi_hello.c . In this example we compile the MPI C program mpi_hello.c and name the output (the executable) mpi_hello . b-an01 [ ~ ] $ mpicc mpi_hello.c -o mpi_hello You then run with `mpirun mpi_hello Important If you later have loaded a different compiler than the one your program was compiled with, you should recompile your program before running it. Exercise Try loading foss/2023b and compiling mpi_hello.c , then unload the module and instead load the module intel/2023b and see what happens if you try to run with mpirun mpi_hello . Flags \u00b6 Note List of commonly used flags: -o file Place output in file \u2018file\u2019. -c Compile or assemble the source files, but do not link. -fopenmp Enable handling of the OpenMP directives. -g Produce debugging information in the operating systems native format. -O or -O1 Optimize. The compiler tried to reduce code size and execution time. -O2 Optimize even more. GCC performs nearly all supported optimizations that do not involve a space-speed tradeoff. -O3 Optimize even more. The compiler will also do loop unrolling and function inlining. RECOMMENDED -O0 Do not optimize. This is the default. -Os Optimize for size. -Ofast Disregard strict standards compliance. -Ofast enables all -O3 optimizations. It also enables optimizations that are not valid for all standard-compliant programs. It turns on -ffast-math and the Fortran-specific -fno-protect-parens and -fstack-arrays. -ffast-math Sets the options -fno-math-errno , -funsafe-math-optimizations , -ffinite-math-only , -fno-rounding-math , -fno-signaling-nans and -fcx-limited-range . -l library Search the library named \u2018library\u2019 when linking. Hint Code-along! Example: compiling an OpenMP C program You can find the file omp_hello.c in the exercises directory, in the subdirectory \u201csimple\u201d. Or you can download it here: omp_hello.c . In this example we compile the OpenMP C program omp_hello.c and name the output (executable) omp_hello . b-an01 [ ~ ] $ gcc -fopenmp omp_hello.c -o omp_hello Note You can change the number of threads with export OMP_NUM_THREADS=#threads Hint Code-along! Example Run the binary omp_hello that we got in the previous example. Set the number of threads to 4 and then rerun the binary. b-an01 [ ~ ] $ ./omp_hello Thread 0 says: Hello World Thread 0 reports: the number of threads are 1 b-an01 [ ~ ] $ export OMP_NUM_THREADS = 4 b-an01 [ ~ ] $ ./omp_hello Thread 1 says: Hello World Thread 0 says: Hello World Thread 0 reports: the number of threads are 4 Thread 3 says: Hello World Thread 2 says: Hello World b-an01 [ ~ ] $ Exercise Try yourself! Rerun with OMP_NUM_THREADS set to 1, 2, 4, 8. NOTE : Normally you are not supposed to run anything on the command line, but these are very short and light-weight programs. Exercise You could try with a different toolchain (or version). Remember to unload/purge, load the new toolchain, compile the program again, and then run. Compiling with Intel \u00b6 Language Compiler name MPI Fortran77 ifort mpiifort Fortran90 ifort mpiifort Fortran95 ifort N/A C icc mpiicc C++ icpc mpiicc In order to access the MPI compilers, load a compiler toolchain which contains an MPI library . Example: compiling a C program We are again compiling the hello.c program from before. This time we name the executable hello_intel to not overwrite the previously created executable. b-an01 [ ~ ] $ icc hello.c -o hello Flags \u00b6 Note List of commonly used flags: -fast This option maximizes speed across the entire program. -g Produce symbolic debug information in an object file. The -g option changes the default optimization from -O2 to -O0 . It is often a good idea to add -traceback also, so the compiler generates extra information in the object file to provide source file traceback information. -debug all Enables generation of enhanced debugging information. You need to also specify -g -O0 Disable optimizations. Use if you want to be certain of getting correct code. Otherwise use -O2 for speed. -O Same as -O2 -O1 Optimize to favor code size and code locality. Disables loop unrolling. -O1 may improve performance for applications with very large code size, many branches, and execution time not dominated by code within loops. In most cases, -O2 is recommended over -O1 . -O2 (default) Optimize for code speed. This is the generally recommended optimization level. -O3 Enable -O2 optimizations and in addition, enable more aggressive optimizations such as loop and memory access transformation, and prefetching. The -O3 option optimizes for maximum speed, but may not improve performance for some programs and may in some cases even slow down code. -Os Enable speed optimizations, but disable some optimizations that increase code size for small speed benefit. -fpe{0,1,3} Allows some control over floating-point exception (divide by zero, overflow, invalid operation, underflow, denormalized number, positive infinity, negative infinity or a NaN) handling for the main program at runtime. Fortran only. -qopenmp Enable the parallelizer to generate multi-threaded code based on the OpenMP directives. -parallel Enable the auto-parallelizer to generate multi-threaded code for loops that can be safely executed in parallel. Linking \u00b6 Build environment \u00b6 Using a compiler toolchain by itself is possible but requires a fair bit of manual work, figuring out which paths to add to -I or -L for including files and libraries, and similar. To make life as a software builder easier there is a special module available, buildenv , that can be loaded on top of any toolchain. If it is missing for some toolchain, send a mail to support@hpc2n.umu.se and let us know. This module defines a large number of environment variables with the relevant settings for the used toolchain. Among other things it sets CC, CXX, F90, FC, MPICC, MPICXX, MPIF90, CFLAGS, FFLAGS, and much more. To see all of them, after loading a toolchain do: ml show buildenv To use the environment variables, load buildenv: ml buildenv Using the environment variable (prefaced with $) for linking is highly recommended! Example Linking with LAPACK (gcc, C program). gcc -o PROGRAM PROGRAM.c -lflexiblas -lgfortran OR use the environment variable $LIBLAPACK : gcc -o PROGRAM PROGRAM.c $LIBLAPACK Note You can see a list of all the libraries on Kebnekaise (June 2024) here: https://docs.hpc2n.umu.se/documentation/compiling/#libraries . Keypoints In order to compile a program, you must first load a \u201ccompiler toolchain\u201d module Kebnekaise has both GCC and Intel compilers installed The GCC compilers are: gfortran gcc g++ The Intel compilers are: ifort icc icpc Compiling MPI programs can be done after loading a compiler toolchains which contains MPI libraries The easiest way to figure out how to link with a library is to use ml show buildenv after loading a compiler toolchain","title":"Compiling"},{"location":"compilers/#compiling__and__linking__with__libraries","text":"Objectives Learn about the compilers at HPC2N How to load the compiler toolchains How to use the compilers What are the popular flags How to link with libraries.","title":"Compiling and Linking with Libraries"},{"location":"compilers/#installed__compilers","text":"There are compilers available for Fortran 77, Fortran 90, Fortran 95, C, and C++. The compilers can produce both general-purpose code and architecture-specific optimized code to improve performance (loop-level optimizations, inter-procedural analysis and cache optimizations).","title":"Installed compilers"},{"location":"compilers/#loading__compilers","text":"Note You need to load a compiler suite (and possibly libraries, depending on what you need) before you can compile and link. Use ml av to get a list of available compiler toolchains as mentioned in the modules - compiler toolchains section. You load a compiler toolchain the same way you load any other module. They are always available directly, without the need to load prerequisites first. Hint Code-along! Example: Loading foss/2023b This compiler toolchain contains: GCC/13.2.0 , BLAS (with LAPACK ), ScaLAPACK , and FFTW . b-an01 [ ~ ] $ ml foss/2023b b-an01 [ ~ ] $ ml Currently Loaded Modules: 1 ) snicenvironment ( S ) 7 ) numactl/2.0.16 13 ) libevent/2.1.12 19 ) FlexiBLAS/3.3.1 2 ) systemdefault ( S ) 8 ) XZ/5.4.4 14 ) UCX/1.15.0 20 ) FFTW/3.3.10 3 ) GCCcore/13.2.0 9 ) libxml2/2.11.5 15 ) PMIx/4.2.6 21 ) FFTW.MPI/3.3.10 4 ) zlib/1.2.13 10 ) libpciaccess/0.17 16 ) UCC/1.2.0 22 ) ScaLAPACK/2.2.0-fb 5 ) binutils/2.40 11 ) hwloc/2.9.2 17 ) OpenMPI/4.1.6 23 ) foss/2023b 6 ) GCC/13.2.0 12 ) OpenSSL/1.1 18 ) OpenBLAS/0.3.24 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $","title":"Loading compilers"},{"location":"compilers/#compiling","text":"Note OpenMP : All compilers has this included, so it is enough to load the module for a specific compiler toolchain and then add the appropriate flag. Note If you do not name the executable (with the flag -o SOMENAME , it will be named a.out as default. This also means that the next time you compile something, if you also do not name that executable, it will overwrite the previous a.out file.","title":"Compiling"},{"location":"compilers/#compiling__with__gcc","text":"Language Compiler name MPI Fortran77 gfortran mpif77 Fortran90 gfortran mpif90 Fortran95 gfortran N/A C gcc mpicc C++ g++ mpiCC In order to access the MPI compilers, load a compiler toolchain which contains an MPI library . Hint Code-along! Example: compiling a C program You can find the file hello.c in the exercises directory, in the subdirectory \u201csimple\u201d. Or you can download it here: hello.c . In this example we compile the C program hello.c and name the output (the executable) hello . b-an01 [ ~ ] $ gcc hello.c -o hello You can run the executable with ./hello Example: compiling an MPI C program You can find the file mpi_hello.c in the exercises directory, in the subdirectory \u201csimple\u201d. Or you can download it here: mpi_hello.c . In this example we compile the MPI C program mpi_hello.c and name the output (the executable) mpi_hello . b-an01 [ ~ ] $ mpicc mpi_hello.c -o mpi_hello You then run with `mpirun mpi_hello Important If you later have loaded a different compiler than the one your program was compiled with, you should recompile your program before running it. Exercise Try loading foss/2023b and compiling mpi_hello.c , then unload the module and instead load the module intel/2023b and see what happens if you try to run with mpirun mpi_hello .","title":"Compiling with GCC"},{"location":"compilers/#flags","text":"Note List of commonly used flags: -o file Place output in file \u2018file\u2019. -c Compile or assemble the source files, but do not link. -fopenmp Enable handling of the OpenMP directives. -g Produce debugging information in the operating systems native format. -O or -O1 Optimize. The compiler tried to reduce code size and execution time. -O2 Optimize even more. GCC performs nearly all supported optimizations that do not involve a space-speed tradeoff. -O3 Optimize even more. The compiler will also do loop unrolling and function inlining. RECOMMENDED -O0 Do not optimize. This is the default. -Os Optimize for size. -Ofast Disregard strict standards compliance. -Ofast enables all -O3 optimizations. It also enables optimizations that are not valid for all standard-compliant programs. It turns on -ffast-math and the Fortran-specific -fno-protect-parens and -fstack-arrays. -ffast-math Sets the options -fno-math-errno , -funsafe-math-optimizations , -ffinite-math-only , -fno-rounding-math , -fno-signaling-nans and -fcx-limited-range . -l library Search the library named \u2018library\u2019 when linking. Hint Code-along! Example: compiling an OpenMP C program You can find the file omp_hello.c in the exercises directory, in the subdirectory \u201csimple\u201d. Or you can download it here: omp_hello.c . In this example we compile the OpenMP C program omp_hello.c and name the output (executable) omp_hello . b-an01 [ ~ ] $ gcc -fopenmp omp_hello.c -o omp_hello Note You can change the number of threads with export OMP_NUM_THREADS=#threads Hint Code-along! Example Run the binary omp_hello that we got in the previous example. Set the number of threads to 4 and then rerun the binary. b-an01 [ ~ ] $ ./omp_hello Thread 0 says: Hello World Thread 0 reports: the number of threads are 1 b-an01 [ ~ ] $ export OMP_NUM_THREADS = 4 b-an01 [ ~ ] $ ./omp_hello Thread 1 says: Hello World Thread 0 says: Hello World Thread 0 reports: the number of threads are 4 Thread 3 says: Hello World Thread 2 says: Hello World b-an01 [ ~ ] $ Exercise Try yourself! Rerun with OMP_NUM_THREADS set to 1, 2, 4, 8. NOTE : Normally you are not supposed to run anything on the command line, but these are very short and light-weight programs. Exercise You could try with a different toolchain (or version). Remember to unload/purge, load the new toolchain, compile the program again, and then run.","title":"Flags"},{"location":"compilers/#compiling__with__intel","text":"Language Compiler name MPI Fortran77 ifort mpiifort Fortran90 ifort mpiifort Fortran95 ifort N/A C icc mpiicc C++ icpc mpiicc In order to access the MPI compilers, load a compiler toolchain which contains an MPI library . Example: compiling a C program We are again compiling the hello.c program from before. This time we name the executable hello_intel to not overwrite the previously created executable. b-an01 [ ~ ] $ icc hello.c -o hello","title":"Compiling with Intel"},{"location":"compilers/#flags_1","text":"Note List of commonly used flags: -fast This option maximizes speed across the entire program. -g Produce symbolic debug information in an object file. The -g option changes the default optimization from -O2 to -O0 . It is often a good idea to add -traceback also, so the compiler generates extra information in the object file to provide source file traceback information. -debug all Enables generation of enhanced debugging information. You need to also specify -g -O0 Disable optimizations. Use if you want to be certain of getting correct code. Otherwise use -O2 for speed. -O Same as -O2 -O1 Optimize to favor code size and code locality. Disables loop unrolling. -O1 may improve performance for applications with very large code size, many branches, and execution time not dominated by code within loops. In most cases, -O2 is recommended over -O1 . -O2 (default) Optimize for code speed. This is the generally recommended optimization level. -O3 Enable -O2 optimizations and in addition, enable more aggressive optimizations such as loop and memory access transformation, and prefetching. The -O3 option optimizes for maximum speed, but may not improve performance for some programs and may in some cases even slow down code. -Os Enable speed optimizations, but disable some optimizations that increase code size for small speed benefit. -fpe{0,1,3} Allows some control over floating-point exception (divide by zero, overflow, invalid operation, underflow, denormalized number, positive infinity, negative infinity or a NaN) handling for the main program at runtime. Fortran only. -qopenmp Enable the parallelizer to generate multi-threaded code based on the OpenMP directives. -parallel Enable the auto-parallelizer to generate multi-threaded code for loops that can be safely executed in parallel.","title":"Flags"},{"location":"compilers/#linking","text":"","title":"Linking"},{"location":"compilers/#build__environment","text":"Using a compiler toolchain by itself is possible but requires a fair bit of manual work, figuring out which paths to add to -I or -L for including files and libraries, and similar. To make life as a software builder easier there is a special module available, buildenv , that can be loaded on top of any toolchain. If it is missing for some toolchain, send a mail to support@hpc2n.umu.se and let us know. This module defines a large number of environment variables with the relevant settings for the used toolchain. Among other things it sets CC, CXX, F90, FC, MPICC, MPICXX, MPIF90, CFLAGS, FFLAGS, and much more. To see all of them, after loading a toolchain do: ml show buildenv To use the environment variables, load buildenv: ml buildenv Using the environment variable (prefaced with $) for linking is highly recommended! Example Linking with LAPACK (gcc, C program). gcc -o PROGRAM PROGRAM.c -lflexiblas -lgfortran OR use the environment variable $LIBLAPACK : gcc -o PROGRAM PROGRAM.c $LIBLAPACK Note You can see a list of all the libraries on Kebnekaise (June 2024) here: https://docs.hpc2n.umu.se/documentation/compiling/#libraries . Keypoints In order to compile a program, you must first load a \u201ccompiler toolchain\u201d module Kebnekaise has both GCC and Intel compilers installed The GCC compilers are: gfortran gcc g++ The Intel compilers are: ifort icc icpc Compiling MPI programs can be done after loading a compiler toolchains which contains MPI libraries The easiest way to figure out how to link with a library is to use ml show buildenv after loading a compiler toolchain","title":"Build environment"},{"location":"filesystem/","text":"The File System \u00b6 Objectives Learn about the file system on Kebnekaise Find the project storage for this course and create your own subdirectory Overview \u00b6 Project storage $HOME /scratch Recommended for batch jobs Yes No (size) Yes Backed up No Yes No Accessible by batch system Yes Yes Yes (node only) Performance High High Medium Default readability Group only Owner Owner Permissions management chmod, chgrp, ACL chmod, chgrp, ACL N/A for batch jobs Notes Storage your group get allocated through the storage projects Your home-directory Per node $HOME \u00b6 This is your home-directory (pointed to by the $HOME variable). It has a quota limit of 25GB per default. Your home directory is backed up regularly. Note Since the home directory is quite small, it should not be used for most production jobs. These should instead be run from project storage directories. To find the path to your home directory, either run pwd just after logging in, or do the following: b-an01 [ ~/store ] $ cd b-an01 [ ~ ] $ pwd /home/u/username b-an01 [ ~ ] $ Project storage \u00b6 Project storage is where a project\u2019s members have the majority of their storage. It is applied for through SUPR, as a storage project. While storage projects needs to be applied for separately, they are usually linked to a compute project. This is where you should keep your data and run your batch jobs from. It offers high performance when accessed from the nodes making it suitable for storage that are to be accessed from parallel jobs, and your home directory (usually) has too little space. Project storage is located below /proj/nobackup/ in the directory name selected during the creation of the proposal. Note The project storage is not intended for permanent storage and there is NO BACKUP of /proj/nobackup . Using project storage \u00b6 If you have a storage project, you should use that to run your jobs. You (your PI) will either choose a directory name when you/they apply for the storage project or get the project id as default name. The location of the storage project in the file system is /proj/nobackup/NAME-YOU-PICKED Since the storage project is shared between all users of the project, you should go to that directory and create a subdirectory for your things, which you will then be using.- For this course the storage is in /proj/nobackup/intro-hpc2n Exercise Go to the course project storage and create a subdirectory for yourself. Now is a good time to prepare the course material and download the exercises. The easiest way to do so is by cloning the whole intro-course repository from GitHub. Exercise Go to the subdirectory you created under /proj/nobackup/intro-hpc2n Clone the repository for the course: git clone https://github.com/hpc2n/intro-course.git You will get a directory called intro-course . Below it you will find a directory called \u201cexercises\u201d where the majority of the exercises for the batch system section is located. Quota \u00b6 The size of the storage depends on the allocation. There are small, medium, and large storage projects, each with their own requirements. You can read about this on SUPR. The quota limits are specific for the project as such, there are no user level quotas on that space. /scratch \u00b6 Our recommendation is that you use the project storage instead of /scratch when working on Compute nodes or Login nodes. On the computers at HPC2N there is a directory called /scratch . It is a small local area split between the users using the node and it can be used for saving (temporary) files you create or need during your computations. Please do not save files in /scratch you don\u2019t need when not running jobs on the machine, and please make sure your job removes any temporary files it creates. Note When anybody need more space than available on /scratch , we will remove the oldest/largest files without any notices. More information about the file system, as well as archiving and compressing files, at the HPC2N documentation about File Systems . Keypoints When you login to Kebnekaise, you will end up in your home-directory. Your home-directory is in /home/u/username and is pointed to by the environment variable $HOME . Your project storage is located in /proj/nobackup/NAME-YOU-PICKED For this course it is /proj/nobackup/intro-hpc2n . The project storage is NOT backed up. You should run the batch jobs from your project storage.","title":"The File System"},{"location":"filesystem/#the__file__system","text":"Objectives Learn about the file system on Kebnekaise Find the project storage for this course and create your own subdirectory","title":"The File System"},{"location":"filesystem/#overview","text":"Project storage $HOME /scratch Recommended for batch jobs Yes No (size) Yes Backed up No Yes No Accessible by batch system Yes Yes Yes (node only) Performance High High Medium Default readability Group only Owner Owner Permissions management chmod, chgrp, ACL chmod, chgrp, ACL N/A for batch jobs Notes Storage your group get allocated through the storage projects Your home-directory Per node","title":"Overview"},{"location":"filesystem/#home","text":"This is your home-directory (pointed to by the $HOME variable). It has a quota limit of 25GB per default. Your home directory is backed up regularly. Note Since the home directory is quite small, it should not be used for most production jobs. These should instead be run from project storage directories. To find the path to your home directory, either run pwd just after logging in, or do the following: b-an01 [ ~/store ] $ cd b-an01 [ ~ ] $ pwd /home/u/username b-an01 [ ~ ] $","title":"$HOME"},{"location":"filesystem/#project__storage","text":"Project storage is where a project\u2019s members have the majority of their storage. It is applied for through SUPR, as a storage project. While storage projects needs to be applied for separately, they are usually linked to a compute project. This is where you should keep your data and run your batch jobs from. It offers high performance when accessed from the nodes making it suitable for storage that are to be accessed from parallel jobs, and your home directory (usually) has too little space. Project storage is located below /proj/nobackup/ in the directory name selected during the creation of the proposal. Note The project storage is not intended for permanent storage and there is NO BACKUP of /proj/nobackup .","title":"Project storage"},{"location":"filesystem/#using__project__storage","text":"If you have a storage project, you should use that to run your jobs. You (your PI) will either choose a directory name when you/they apply for the storage project or get the project id as default name. The location of the storage project in the file system is /proj/nobackup/NAME-YOU-PICKED Since the storage project is shared between all users of the project, you should go to that directory and create a subdirectory for your things, which you will then be using.- For this course the storage is in /proj/nobackup/intro-hpc2n Exercise Go to the course project storage and create a subdirectory for yourself. Now is a good time to prepare the course material and download the exercises. The easiest way to do so is by cloning the whole intro-course repository from GitHub. Exercise Go to the subdirectory you created under /proj/nobackup/intro-hpc2n Clone the repository for the course: git clone https://github.com/hpc2n/intro-course.git You will get a directory called intro-course . Below it you will find a directory called \u201cexercises\u201d where the majority of the exercises for the batch system section is located.","title":"Using project storage"},{"location":"filesystem/#quota","text":"The size of the storage depends on the allocation. There are small, medium, and large storage projects, each with their own requirements. You can read about this on SUPR. The quota limits are specific for the project as such, there are no user level quotas on that space.","title":"Quota"},{"location":"filesystem/#scratch","text":"Our recommendation is that you use the project storage instead of /scratch when working on Compute nodes or Login nodes. On the computers at HPC2N there is a directory called /scratch . It is a small local area split between the users using the node and it can be used for saving (temporary) files you create or need during your computations. Please do not save files in /scratch you don\u2019t need when not running jobs on the machine, and please make sure your job removes any temporary files it creates. Note When anybody need more space than available on /scratch , we will remove the oldest/largest files without any notices. More information about the file system, as well as archiving and compressing files, at the HPC2N documentation about File Systems . Keypoints When you login to Kebnekaise, you will end up in your home-directory. Your home-directory is in /home/u/username and is pointed to by the environment variable $HOME . Your project storage is located in /proj/nobackup/NAME-YOU-PICKED For this course it is /proj/nobackup/intro-hpc2n . The project storage is NOT backed up. You should run the batch jobs from your project storage.","title":"/scratch"},{"location":"intro/","text":"Introduction to HPC2N, Kebnekaise and HPC \u00b6 Welcome page and syllabus: https://hpc2n.github.io/intro-linux/index.html Also link at the House symbol at the top of the page. HPC2N \u00b6 Note High Performance Computing Center North (HPC2N) is a competence center for Scientific and Parallel Computing part of National Academic Infrastructure for Super\u00adcomputing in Sweden (NAISS) HPC2N provides state-of-the-art resources and expertise: Scalable and parallel HPC Large-scale storage facilities (Project storage (Lustre), SweStore, Tape) Grid and cloud computing (WLCG NT1, Swedish Science Cloud) National Data Science Node in \u201dEpidemiology and Biology of Infections\u201d (DDLS) Software for e-Science applications All levels of user support Primary, advanced, dedicated Application Experts (AEs) Primary objective To raise the national and local level of HPC competence and transfer HPC knowledge and technology to new users in academia and industry. HPC2N partners \u00b6 HPC2N is hosted by: Partners: HPC2N funding and collaborations \u00b6 Funded mainly by Ume\u00e5 University , with contributions from the other HPC2N partners . Involved in several projects and collaborations : HPC2N training and other services \u00b6 User support (primary, advanced, dedicated) Research group meetings @ UmU Also at the partner sites Online \u201cHPC2N fika\u201d User training and education program 0.5 \u2013 5 days; ready-to-run exercises intro courses: our system, Linux, R, Python, Julia, Matlab, Git intermediate courses Parallel programming and tools (OpenMP, MPI, debugging, perf. analyzers, Matlab, R, MD simulation, ML, GPU, \u2026) Courses this fall Introduction to Linux, 16 September 2024 Introduction to HPC2N and Kebnekaise, 16 September 2024 Basic Singularity, 16 October 2024 Introduction to running R, Python, Julia, and Matlab in HPC, 22-25 October 2024 Introduction to Git, 25-29 November 2024 Using Python in an HPC environment, 5-6 December 2024 Updated list: https://www.hpc2n.umu.se/events/courses Workshops and seminars NGSSC / SeSE & university courses HPC2N personnel \u00b6 Management: Paolo Bientinesi, director Bj\u00f6rn Torkelsson, deputy director Lena Hellman, administrator Application experts: Jerry Eriksson Pedro Ojeda May Birgitte Bryds\u00f6 \u00c5ke Sandgren Others: Mikael R\u00e4nnar (WLCG coord) Research Engineers under DDLS, HPC2N/SciLifeLab Paul Dulaud, System Developer, IT Abdullah Aziz, Data Engineer Nalina Hamsaiyni Venkatesh, Data Steward System and support: Erik Andersson Birgitte Bryds\u00f6 Niklas Edmundsson (Tape coord) My Karlsson Roger Oscarsson \u00c5ke Sandgren Mattias Wadenstein (NeIC, Tier1) HPC2N application experts \u00b6 HPC2N provides advanced and dedicated support in the form of Application Experts (AEs) : Jerry Eriksson: Profiling, Machine learning (DNN), MPI, OpenMP, OpenACC Pedro Ojeda May: Molecular dynamics, Profiling, QM/MM, NAMD, Amber, Gromacs, GAUSSIAN, R, Python \u00c5ke Sandgren: General high level programming assistance, VASP, Gromacs, Amber Birgitte Bryds\u00f6: General HPC, R, Python Contact through regular support HPC2N users by discipline \u00b6 Users from several scientific disciplines: Biosciences and medicine Chemistry Computing science Engineering Materials science Mathematics and statistics Physics including space physics ML, DL, and other AI HPC2N users by discipline, largest users \u00b6 Users from several scientific disciplines: Biosciences and medicine Chemistry Computing science Engineering Materials science Mathematics and statistics Physics including space physics Machine learning and artificial intelligence (several new projects) HPC2N users by software \u00b6 Kebnekaise \u00b6 The current supercomputer at HPC2N. It is a very heterogeneous system. Named after a massif (contains some of Sweden\u2019s highest mountain peaks) Kebnekaise was delivered by Lenovo and installed during the summer 2016 Opened up for general availability on November 7, 2016 In 2018, Kebnekaise was extended with 52 Intel Xeon Gold 6132 (Skylake) nodes, as well as 10 NVidian V100 (Volta) GPU nodes In 2023, Kebnekaise was extended with 2 dual NVIDIA A100 GPU nodes one many-core AMD Zen3 CPU node In 2024 Kebnekaise was extended with 2 Dual socket GPU-nodes: Lenovo ThinkSystem SR675 V3 2 x AMD EPYC 9454 48C 290W 2.75GHz Processor 768GB [24x 32GB TruDDR5 4800MHz RDIMM-A] 1 x 3.84TB Read Intensive NVMe PCIe 4.0 x4 HS SSD 1 x NVIDIA H100 SXM5 700W 80G HBM3 GPU Board 10 dual-socket GPU-nodes: ThinkSystem SR665 V3 2 x AMD EPYC 9254 24C 200W 2.9GHz Processor 384GB [24x 16GB TruDDR5 4800MHz RDIMM-A] 1 x 1.92TB Read Intensive NVMe PCIe 5.0 x4 HS SSD 2 x NVIDIA L40S 48GB PCIe Gen4 Passive GPU 8 dual-socket CPU only: ThinkSystem SR645 V3 2 x AMD EPYC 9754 128C 360W 2.25GHz Processor 768GB [24x 32GB TruDDR5 4800MHz RDIMM-A] 1 x 1 3.84TB Read Intensive NVMe PCIe 4.0 x4 HS SSD Kebnekaise will be continuosly upgraded, as old hardware gets retired. Current hardware in Kebnekaise \u00b6 Kebnekaise have CPU-only, GPU enabled and large memory nodes. The CPU-only nodes are: 2 x 14 core Intel broadwell 4460 MB memory / core 48 nodes Total of 41.6 TFlops/s 2 x 14 core Intel skylake 6785 MB memory / core 52 nodes Total of 87 TFlops/s 2 x 64 core AMD zen3 8020 MB / core 1 node Total of 11 TFlops/s 2 x 128 core AMD zen4 2516 MB / core 8 nodes Total of 216 TFlops/s The GPU enabled nodes are: 2 x 14 core Intel broadwell 9000 MB memory / core 2 x Nvidia A40 4 nodes Total of 83 TFlops/s 2 x 14 core Intel skylake 6785 MB memory / core 2 x Nvidia V100 10 nodes Total of 75 TFlops/s 2 x 24 core AMD zen3 10600 MB / core 2 x Nvidia A100 2 nodes 2 x 24 core AMD zen3 10600 MB / core 2 x AMD MI100 1 node 2 x 24 core AMD zen4 6630 MB / core 2 x Nvidia A6000 1 node 2 x 24 core AMD zen4 6630 MB / core 2 x Nvidia L40s 10 nodes 2 x 48 core AMD zen4 6630 MB / core 4 x Nvidia H100 SXM5 2 nodes The large memory nodes are: 4 x 18 core Intel broadwell 41666 MB memory / core 8 nodes Total of 13.6 TFlops/s for all these nodes GPUs can have different types of cores: CUDA cores : General-purpose cores for a variety of parallel computing tasks. Not as efficicent as specizalized cores. CUDA cores is only on NVidia. The (mostly) equivalent is called stream processors on AMD. Tensor cores : Made for matrix multiplications. Good for deep learning and AI workloads involving large matrix operations. Can be used for general-purpose as well, but less efficient for this. Tensor cores is the NVidia name. AMD has a somewhat equivalent core type called matrix cores . RT (ray tracing) cores : Cores that are optimized for tasks involving ray tracing, like rendering images or video. GPU Type CUDA cores / stream processors TENSOR cores / matrix cores RT cores A40 10752 336 V100 5120 640 A100 6912 432 MI100 7680 480 A6000 10752 386 L40S 18176 568 142 H100 16896 528 NOTE that just like you cannot really compare CPU cores directly (speed etc.) you also cannot just compare CUDA/TENSOR/RT etc. cores directly (more efficient design, faster, etc.) Kebnekaise - HPC2N storage \u00b6 Basically four types of storage are available at HPC2N: Home directory /home/X/Xyz , $HOME , ~ 25 GB, user owned Project storage /proj/nobackup/abc Shared among project members Local scratch space $SNIC_TMP SSD (170GB), per job, per node, \u201cvolatile\u201d Tape Storage Backup Long term storage Also SweStore \u2014 disk based (dCache) Research Data Storage Infrastructure, for active research data and operated by NAISS, WLCG Kebnekaise - projects \u00b6 Compute projects To use Kebnekaise, you must be a member of a compute project . A compute project has a certain number of core hours allocated for it per month A regular CPU core cost 1 core hour per hour, other resources (e.g., GPUs) cost more Not a hard limit but projects that go over the allocation get lower priority A compute project contains a certain amount of storage. If more storage is required, you must be a member of a storage project . Note As Kebnekaise is a local cluster, you need to be affiliated with UmU, IRF, SLU, Miun, or LTU to use it. Projects are applied for through SUPR ( https://supr.naiss.se ). I will cover more details in a later section, where we go more into detail about HPC2N and Kebnekaise. HPC \u00b6 What is HPC? High Performance Computing (definition) \u201cHigh Performance Computing most generally refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve large problems in science, engineering, or business.\u201d From: https://insidehpc.com/hpc-basic-training/what-is-hpc/ High Performance Computing - opening the definition \u00b6 Aggregating computing power \u00b6 147 nodes totalling 6808 CPU cores and 64 GPUs (totalling 751616 CUDA cores, 33472 TENSOR cores + 960 matrix cores, 284 RT cores) Compared to 4-8 cores in a common modern laptop Higher performance \u00b6 More than 527,000,000,000,000 arithmetical operations per second (527 trillion (billion)) in the CPU cores Compared to 200,000,000,000 Flops in a modern laptop (200 billion (milliard) Solve large problems \u00b6 When does a problem become large enough for HPC? Are there other reasons for using HPC resources? (Memory, software, support, etc.) High Performance Computing - large problems \u00b6 A problem can be large for two main reasons: Execution time : The time required to form a solution to the problem is very long Memory / storage use : The solution of the problem requires a lot of memory and/or storage The former can be remedied by increasing the performance More cores, more nodes, GPUs, \u2026 The latter by adding more memory / storage More memory per node (including large memory nodes), more nodes, \u2026 Kebnekaise: 128GB - 192GB, 384GB, 512GB, 768GB, 3TB Large storage solutions, \u2026 High Performance Computing - what counts as HPC \u00b6 High Performance Computing - other reasons \u00b6 Specialized (expensive) hardware GPUs, including those optimized for AI Kebnekaise has V100, A100, A40, MI100, A6000, L40S, H100 High-end CPUs (AVX-512 etc) and ECC memory Software HPC2N holds licenses for several softwares Software is pre-configured and ready-to-use Support and documentation High Performance Computing - memory models \u00b6 Two memory models are relevant for HPC: Shared memory: Single memory space for all data. Everyone can access the same data Straightforward to use Distributed memory: Multiple distinct memory spaces. Everyone has direct access only to the local data Requires communication High Performance Computing - programming models \u00b6 The programming model changes when we aim for extra performance and/or memory: Single-core: Matlab, Python, C, Fortran, \u2026 Single stream of operations Multi-core: Vectorized Matlab, pthreads, OpenMP Multiple streams of operations Work distribution, coordination (synchronization, etc), \u2026 Distributed memory: MPI, \u2026 Multiple streams of operations Work distribution, coordination (synchronization, etc), \u2026 Data distribution and communication GPUs: CUDA, OpenCL, OpenACC, OpenMP, \u2026 Many lightweight streams of operations Work distribution, coordination (synchronization, etc), \u2026 Data distribution across memory spaces and movement High Performance Computing - software \u00b6 Complexity grows when we aim for extra performance and/or memory/storage: Single-core: LAPACK, \u2026 Load correct toolchain etc Multi-core: LAPACK + parallel BLAS, \u2026 Load correct toolchain etc Allocate correct number of cores, configure software to use correct number of cores, \u2026 Distributed memory}: ScaLAPACK, \u2026 Load correct toolchain etc Allocate correct number of nodes and cores , configure software to use correct number of nodes and cores , \u2026 Data distribution, storage, \u2026 GPUs: MAGMA, TensorFlow, \u2026 Load correct toolchain etc Allocate correct number of cores and GPUs , configure software to use correct number of cores and GPUs , \u2026","title":"Introduction to Kebnekaise and HPC2N"},{"location":"intro/#introduction__to__hpc2n__kebnekaise__and__hpc","text":"Welcome page and syllabus: https://hpc2n.github.io/intro-linux/index.html Also link at the House symbol at the top of the page.","title":"Introduction to HPC2N, Kebnekaise and HPC"},{"location":"intro/#hpc2n","text":"Note High Performance Computing Center North (HPC2N) is a competence center for Scientific and Parallel Computing part of National Academic Infrastructure for Super\u00adcomputing in Sweden (NAISS) HPC2N provides state-of-the-art resources and expertise: Scalable and parallel HPC Large-scale storage facilities (Project storage (Lustre), SweStore, Tape) Grid and cloud computing (WLCG NT1, Swedish Science Cloud) National Data Science Node in \u201dEpidemiology and Biology of Infections\u201d (DDLS) Software for e-Science applications All levels of user support Primary, advanced, dedicated Application Experts (AEs) Primary objective To raise the national and local level of HPC competence and transfer HPC knowledge and technology to new users in academia and industry.","title":"HPC2N"},{"location":"intro/#hpc2n__partners","text":"HPC2N is hosted by: Partners:","title":"HPC2N partners"},{"location":"intro/#hpc2n__funding__and__collaborations","text":"Funded mainly by Ume\u00e5 University , with contributions from the other HPC2N partners . Involved in several projects and collaborations :","title":"HPC2N funding and collaborations"},{"location":"intro/#hpc2n__training__and__other__services","text":"User support (primary, advanced, dedicated) Research group meetings @ UmU Also at the partner sites Online \u201cHPC2N fika\u201d User training and education program 0.5 \u2013 5 days; ready-to-run exercises intro courses: our system, Linux, R, Python, Julia, Matlab, Git intermediate courses Parallel programming and tools (OpenMP, MPI, debugging, perf. analyzers, Matlab, R, MD simulation, ML, GPU, \u2026) Courses this fall Introduction to Linux, 16 September 2024 Introduction to HPC2N and Kebnekaise, 16 September 2024 Basic Singularity, 16 October 2024 Introduction to running R, Python, Julia, and Matlab in HPC, 22-25 October 2024 Introduction to Git, 25-29 November 2024 Using Python in an HPC environment, 5-6 December 2024 Updated list: https://www.hpc2n.umu.se/events/courses Workshops and seminars NGSSC / SeSE & university courses","title":"HPC2N training and other services"},{"location":"intro/#hpc2n__personnel","text":"Management: Paolo Bientinesi, director Bj\u00f6rn Torkelsson, deputy director Lena Hellman, administrator Application experts: Jerry Eriksson Pedro Ojeda May Birgitte Bryds\u00f6 \u00c5ke Sandgren Others: Mikael R\u00e4nnar (WLCG coord) Research Engineers under DDLS, HPC2N/SciLifeLab Paul Dulaud, System Developer, IT Abdullah Aziz, Data Engineer Nalina Hamsaiyni Venkatesh, Data Steward System and support: Erik Andersson Birgitte Bryds\u00f6 Niklas Edmundsson (Tape coord) My Karlsson Roger Oscarsson \u00c5ke Sandgren Mattias Wadenstein (NeIC, Tier1)","title":"HPC2N personnel"},{"location":"intro/#hpc2n__application__experts","text":"HPC2N provides advanced and dedicated support in the form of Application Experts (AEs) : Jerry Eriksson: Profiling, Machine learning (DNN), MPI, OpenMP, OpenACC Pedro Ojeda May: Molecular dynamics, Profiling, QM/MM, NAMD, Amber, Gromacs, GAUSSIAN, R, Python \u00c5ke Sandgren: General high level programming assistance, VASP, Gromacs, Amber Birgitte Bryds\u00f6: General HPC, R, Python Contact through regular support","title":"HPC2N application experts"},{"location":"intro/#hpc2n__users__by__discipline","text":"Users from several scientific disciplines: Biosciences and medicine Chemistry Computing science Engineering Materials science Mathematics and statistics Physics including space physics ML, DL, and other AI","title":"HPC2N users by discipline"},{"location":"intro/#hpc2n__users__by__discipline__largest__users","text":"Users from several scientific disciplines: Biosciences and medicine Chemistry Computing science Engineering Materials science Mathematics and statistics Physics including space physics Machine learning and artificial intelligence (several new projects)","title":"HPC2N users by discipline, largest users"},{"location":"intro/#hpc2n__users__by__software","text":"","title":"HPC2N users by software"},{"location":"intro/#kebnekaise","text":"The current supercomputer at HPC2N. It is a very heterogeneous system. Named after a massif (contains some of Sweden\u2019s highest mountain peaks) Kebnekaise was delivered by Lenovo and installed during the summer 2016 Opened up for general availability on November 7, 2016 In 2018, Kebnekaise was extended with 52 Intel Xeon Gold 6132 (Skylake) nodes, as well as 10 NVidian V100 (Volta) GPU nodes In 2023, Kebnekaise was extended with 2 dual NVIDIA A100 GPU nodes one many-core AMD Zen3 CPU node In 2024 Kebnekaise was extended with 2 Dual socket GPU-nodes: Lenovo ThinkSystem SR675 V3 2 x AMD EPYC 9454 48C 290W 2.75GHz Processor 768GB [24x 32GB TruDDR5 4800MHz RDIMM-A] 1 x 3.84TB Read Intensive NVMe PCIe 4.0 x4 HS SSD 1 x NVIDIA H100 SXM5 700W 80G HBM3 GPU Board 10 dual-socket GPU-nodes: ThinkSystem SR665 V3 2 x AMD EPYC 9254 24C 200W 2.9GHz Processor 384GB [24x 16GB TruDDR5 4800MHz RDIMM-A] 1 x 1.92TB Read Intensive NVMe PCIe 5.0 x4 HS SSD 2 x NVIDIA L40S 48GB PCIe Gen4 Passive GPU 8 dual-socket CPU only: ThinkSystem SR645 V3 2 x AMD EPYC 9754 128C 360W 2.25GHz Processor 768GB [24x 32GB TruDDR5 4800MHz RDIMM-A] 1 x 1 3.84TB Read Intensive NVMe PCIe 4.0 x4 HS SSD Kebnekaise will be continuosly upgraded, as old hardware gets retired.","title":"Kebnekaise"},{"location":"intro/#current__hardware__in__kebnekaise","text":"Kebnekaise have CPU-only, GPU enabled and large memory nodes. The CPU-only nodes are: 2 x 14 core Intel broadwell 4460 MB memory / core 48 nodes Total of 41.6 TFlops/s 2 x 14 core Intel skylake 6785 MB memory / core 52 nodes Total of 87 TFlops/s 2 x 64 core AMD zen3 8020 MB / core 1 node Total of 11 TFlops/s 2 x 128 core AMD zen4 2516 MB / core 8 nodes Total of 216 TFlops/s The GPU enabled nodes are: 2 x 14 core Intel broadwell 9000 MB memory / core 2 x Nvidia A40 4 nodes Total of 83 TFlops/s 2 x 14 core Intel skylake 6785 MB memory / core 2 x Nvidia V100 10 nodes Total of 75 TFlops/s 2 x 24 core AMD zen3 10600 MB / core 2 x Nvidia A100 2 nodes 2 x 24 core AMD zen3 10600 MB / core 2 x AMD MI100 1 node 2 x 24 core AMD zen4 6630 MB / core 2 x Nvidia A6000 1 node 2 x 24 core AMD zen4 6630 MB / core 2 x Nvidia L40s 10 nodes 2 x 48 core AMD zen4 6630 MB / core 4 x Nvidia H100 SXM5 2 nodes The large memory nodes are: 4 x 18 core Intel broadwell 41666 MB memory / core 8 nodes Total of 13.6 TFlops/s for all these nodes GPUs can have different types of cores: CUDA cores : General-purpose cores for a variety of parallel computing tasks. Not as efficicent as specizalized cores. CUDA cores is only on NVidia. The (mostly) equivalent is called stream processors on AMD. Tensor cores : Made for matrix multiplications. Good for deep learning and AI workloads involving large matrix operations. Can be used for general-purpose as well, but less efficient for this. Tensor cores is the NVidia name. AMD has a somewhat equivalent core type called matrix cores . RT (ray tracing) cores : Cores that are optimized for tasks involving ray tracing, like rendering images or video. GPU Type CUDA cores / stream processors TENSOR cores / matrix cores RT cores A40 10752 336 V100 5120 640 A100 6912 432 MI100 7680 480 A6000 10752 386 L40S 18176 568 142 H100 16896 528 NOTE that just like you cannot really compare CPU cores directly (speed etc.) you also cannot just compare CUDA/TENSOR/RT etc. cores directly (more efficient design, faster, etc.)","title":"Current hardware in Kebnekaise"},{"location":"intro/#kebnekaise__-__hpc2n__storage","text":"Basically four types of storage are available at HPC2N: Home directory /home/X/Xyz , $HOME , ~ 25 GB, user owned Project storage /proj/nobackup/abc Shared among project members Local scratch space $SNIC_TMP SSD (170GB), per job, per node, \u201cvolatile\u201d Tape Storage Backup Long term storage Also SweStore \u2014 disk based (dCache) Research Data Storage Infrastructure, for active research data and operated by NAISS, WLCG","title":"Kebnekaise - HPC2N storage"},{"location":"intro/#kebnekaise__-__projects","text":"Compute projects To use Kebnekaise, you must be a member of a compute project . A compute project has a certain number of core hours allocated for it per month A regular CPU core cost 1 core hour per hour, other resources (e.g., GPUs) cost more Not a hard limit but projects that go over the allocation get lower priority A compute project contains a certain amount of storage. If more storage is required, you must be a member of a storage project . Note As Kebnekaise is a local cluster, you need to be affiliated with UmU, IRF, SLU, Miun, or LTU to use it. Projects are applied for through SUPR ( https://supr.naiss.se ). I will cover more details in a later section, where we go more into detail about HPC2N and Kebnekaise.","title":"Kebnekaise - projects"},{"location":"intro/#hpc","text":"What is HPC? High Performance Computing (definition) \u201cHigh Performance Computing most generally refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve large problems in science, engineering, or business.\u201d From: https://insidehpc.com/hpc-basic-training/what-is-hpc/","title":"HPC"},{"location":"intro/#high__performance__computing__-__opening__the__definition","text":"","title":"High Performance Computing - opening the definition"},{"location":"intro/#aggregating__computing__power","text":"147 nodes totalling 6808 CPU cores and 64 GPUs (totalling 751616 CUDA cores, 33472 TENSOR cores + 960 matrix cores, 284 RT cores) Compared to 4-8 cores in a common modern laptop","title":"Aggregating computing power"},{"location":"intro/#higher__performance","text":"More than 527,000,000,000,000 arithmetical operations per second (527 trillion (billion)) in the CPU cores Compared to 200,000,000,000 Flops in a modern laptop (200 billion (milliard)","title":"Higher performance"},{"location":"intro/#solve__large__problems","text":"When does a problem become large enough for HPC? Are there other reasons for using HPC resources? (Memory, software, support, etc.)","title":"Solve large problems"},{"location":"intro/#high__performance__computing__-__large__problems","text":"A problem can be large for two main reasons: Execution time : The time required to form a solution to the problem is very long Memory / storage use : The solution of the problem requires a lot of memory and/or storage The former can be remedied by increasing the performance More cores, more nodes, GPUs, \u2026 The latter by adding more memory / storage More memory per node (including large memory nodes), more nodes, \u2026 Kebnekaise: 128GB - 192GB, 384GB, 512GB, 768GB, 3TB Large storage solutions, \u2026","title":"High Performance Computing - large problems"},{"location":"intro/#high__performance__computing__-__what__counts__as__hpc","text":"","title":"High Performance Computing - what counts as HPC"},{"location":"intro/#high__performance__computing__-__other__reasons","text":"Specialized (expensive) hardware GPUs, including those optimized for AI Kebnekaise has V100, A100, A40, MI100, A6000, L40S, H100 High-end CPUs (AVX-512 etc) and ECC memory Software HPC2N holds licenses for several softwares Software is pre-configured and ready-to-use Support and documentation","title":"High Performance Computing - other reasons"},{"location":"intro/#high__performance__computing__-__memory__models","text":"Two memory models are relevant for HPC: Shared memory: Single memory space for all data. Everyone can access the same data Straightforward to use Distributed memory: Multiple distinct memory spaces. Everyone has direct access only to the local data Requires communication","title":"High Performance Computing - memory models"},{"location":"intro/#high__performance__computing__-__programming__models","text":"The programming model changes when we aim for extra performance and/or memory: Single-core: Matlab, Python, C, Fortran, \u2026 Single stream of operations Multi-core: Vectorized Matlab, pthreads, OpenMP Multiple streams of operations Work distribution, coordination (synchronization, etc), \u2026 Distributed memory: MPI, \u2026 Multiple streams of operations Work distribution, coordination (synchronization, etc), \u2026 Data distribution and communication GPUs: CUDA, OpenCL, OpenACC, OpenMP, \u2026 Many lightweight streams of operations Work distribution, coordination (synchronization, etc), \u2026 Data distribution across memory spaces and movement","title":"High Performance Computing - programming models"},{"location":"intro/#high__performance__computing__-__software","text":"Complexity grows when we aim for extra performance and/or memory/storage: Single-core: LAPACK, \u2026 Load correct toolchain etc Multi-core: LAPACK + parallel BLAS, \u2026 Load correct toolchain etc Allocate correct number of cores, configure software to use correct number of cores, \u2026 Distributed memory}: ScaLAPACK, \u2026 Load correct toolchain etc Allocate correct number of nodes and cores , configure software to use correct number of nodes and cores , \u2026 Data distribution, storage, \u2026 GPUs: MAGMA, TensorFlow, \u2026 Load correct toolchain etc Allocate correct number of cores and GPUs , configure software to use correct number of cores and GPUs , \u2026","title":"High Performance Computing - software"},{"location":"login/","text":"Logging in \u00b6 When you have your account, you can login to Kebnekaise. This can be done with any number of SSH clients or with ThinLinc (the easiest option if you need a graphical interface). Objectives Login to Kebnekaise, either with ThinLinc or your SSH client of choice. Kebnekaise login servers \u00b6 Note The main login node of Kebnekaise: kebnekaise.hpc2n.umu.se ThinLinc login node: kebnekaise-tl.hpc2n.umu.se ThinLinc through a browser (less features): https://kebnekaise-tl.hpc2n.umu.se:300/ In addition, there is a login node for the AMD-based nodes. We will talk more about this later: kebnekaise-amd.hpc2n.umu.se . For ThinLinc access: kebnekaise-amd-tl.hpc2n.umu.se ThinLinc is recommended for this course ThinLinc: a cross-platform remote desktop server from Cendio AB. Especially useful when you need software with a graphical interface. This is what we recommend you use for this course, unless you have a preferred SSH client. Using ThinLinc \u00b6 Download the client from https://www.cendio.com/thinlinc/download . Install it. Windows: Run the downloaded .exe file to install. macOS: Information on the ThinLinc macOS info page . Linux Ubuntu: Download the .deb file. Run sudo dpkg -i PATH-TO-FILE/FILE-YOU-DOWNLOADED.deb Start the client. Enter the name of the server: kebnekaise-tl.hpc2n.umu.se . Enter your username. Go to \u201cOptions\u201d \\(->\\) \u201cSecurity\u201d. Check that authentication method is set to password. Go to \u201cOptions\u201d \\(->\\) \u201cScreen\u201d. Uncheck \u201cFull screen mode\u201d. Enter your HPC2N password. Click \u201cConnect\u201d Click \u201cContinue\u201d when you are being told that the server\u2019s host key is not in the registry. Wait for the ThinLinc desktop to open. Password \u00b6 You get your first, temporary HPC2N password from this page: HPC2N passwords . That page can also be used to reset your HPC2N password if you have forgotten it. Note that you are authenticating through SUPR, using that service\u2019s login credentials! Warning The HPC2N password and the SUPR password are separate! The HPC2N password and your university/department password are also separate! Exercise Login to Kebnekaise. If you are using ThinLinc, first install the ThinLinc client. If you are using another SSH client, install it first if you have not already done so. Change password \u00b6 Exercise: Change your password after first login ONLY do this if you have logged in for the first time/is still using the termporary password you got from the HPC2N password reset service! Changing password is done using the passwd command: passwd Use a good password that combines letters of different case. Do not use dictionary words. Avoid using the same password that you also use in other places. It will first ask for your current password. Type in that and press enter. Then type in the new password, enter, and repeat. You have changed the password. File transfers \u00b6 We are not going to transfer any files as part of this course, but you may have to do so as part of your workflow when using Kebnekaise (or another HPC centre) for your research. This section will only talk briefly about file transfers. You can find more information and examples on HPC2N\u2019s File transfer documentation . Linux, OS X \u00b6 scp \u00b6 SCP (Secure CoPy) is a simple way of transferring files between two machines that use the SSH (Secure SHell) protocol. You may use SCP to connect to any system where you have SSH (log-in) access. These examples show how to use scp from the command-line. Graphical programs exists for doing scp transfer. The command-lone scp program should already be installed. Remote to local Transfer a file from Kebnekaise to your local system, while on your local system scp username@kebnekaise.hpc2n.umu.se:file . Local to remote Transfer a local file to Kebnekaise, while on your local system scp file username@kebnekaise.hpc2n.umu.se:file Recursive directory copy from a local system to a remote system The directory sourcedirectory is here copied as a subdirectory to somedir scp -r sourcedirectory/ username@kebnekaise.hpc2n.umu.se:somedir/ sftp \u00b6 SFTP (SSH File Transfer Protocol or sometimes called Secure File Transfer Protocol) is a network protocol that provides file transfer over a reliable data stream. SFTP is a command -line program on most Unix, Linux, and Mac OS X systems. It is also available as a protocol choice in some graphical file transfer programs. Example: From a local system to a remote system enterprise-d [ ~ ] $ sftp user@kebnekaise.hpc2n.umu.se Connecting to kebnekaise.hpc2n.umu.se... user@kebnekaise.hpc2n.umu.se ' s password: sftp> put file.c C/file.c Uploading file.c to /home/u/user/C/file.c file.c 100 % 1 0 .0KB/s 00 :00 sftp> put -P irf.png pic/ Uploading irf.png to /home/u/user/pic/irf.png irf.png 100 % 2100 2 .1KB/s 00 :00 sftp> Windows \u00b6 Here you need to download a client: WinSCP, FileZilla (sftp), PSCP/PSFTP, \u2026 You can transfer with sftp or scp. There is documentation in HPC2N\u2019s documentation pages for Windows file transfers . Editors \u00b6 Since the editors on a Linux system are different to those you may be familiar with from Windows or macOS, here follows a short overview. There are command-line editors and graphical editors. If you are connecting with a regular SSH client, it will be simplest to use a command-line editor. If you are using ThinLinc, you can use command-line editors or graphical editors as you want. Command-line \u00b6 These are all good editors for using on the command line: nano vi , vim emacs They are all installed on Kebnekaise. Of these, vi/vim as well as emacs are probably the most powerful, though the latter is better in a GUI environment. The easiest editor to use if you are not familiar with any of them is nano . Nano Starting \u201cnano\u201d: Type nano FILENAME on the command line and press Enter . FILENAME is whatever you want to call your file. If FILENAME is a file that already exists, nano will open the file. If it dows not exist, it will be created. You now get an editor that looks like this: First thing to notice is that many of the commands are listed at the bottom. The ^ before the letter-commands means you should press CTRL and then the letter (while keeping CTRL down). Your prompt is in the editor window itself, and you can just type (or copy and paste) the content you want in your file. When you want to exit (and possibly save), you press CTRL and then x while holding CTRL down (this is written CTRL-x or ^x ). nano will ask you if you want to save the content of the buffer to the file. After that it will exit. There is a manual for nano here . GUI \u00b6 If you are connecting with ThinLinc , you will be presented with a graphical user interface (GUI). From there you can either open a terminal window/shell ( Applications -> System Tools -> MATE Terminal ) or you can choose editors from the menu by going to Applications -> Accessories . This gives several editor options, of which these have a graphical interface: Text Editor (gedit) Pluma - the default editor on the MATE desktop environments (that Thinlinc runs) Atom - not just an editor, but an IDE Emacs (GUI) NEdit \u201cNirvana Text Editor\u201d If you are not familiar with any of these, a good recommendation would be to use Text Editor/gedit . Text Editor/gedit Starting \u201c gedit \u201d: From the menu, choose Applications -> Accessories -> Text Editor . You then get a window that looks like this: You can open files by clicking \u201c Open \u201d in the top menu. Clicking the small file icon with a green plus will create a new document. Save by clicking \u201c Save \u201d in the menu. The menu on the top right (the three horizontal lines) gives you several other options, including \u201c Find \u201d and \u201c Find and Replace \u201d. Keypoints You can login with ThinLinc or another SSH client ThinLinc is easiest if you need a GUI There are several command-line editors: vi/vim, nano, emacs, \u2026 And several GUI editors, which works best when using ThinLinc: gedit, pluma, atom, emacs (gui), nedit, \u2026","title":"Logging in"},{"location":"login/#logging__in","text":"When you have your account, you can login to Kebnekaise. This can be done with any number of SSH clients or with ThinLinc (the easiest option if you need a graphical interface). Objectives Login to Kebnekaise, either with ThinLinc or your SSH client of choice.","title":"Logging in"},{"location":"login/#kebnekaise__login__servers","text":"Note The main login node of Kebnekaise: kebnekaise.hpc2n.umu.se ThinLinc login node: kebnekaise-tl.hpc2n.umu.se ThinLinc through a browser (less features): https://kebnekaise-tl.hpc2n.umu.se:300/ In addition, there is a login node for the AMD-based nodes. We will talk more about this later: kebnekaise-amd.hpc2n.umu.se . For ThinLinc access: kebnekaise-amd-tl.hpc2n.umu.se ThinLinc is recommended for this course ThinLinc: a cross-platform remote desktop server from Cendio AB. Especially useful when you need software with a graphical interface. This is what we recommend you use for this course, unless you have a preferred SSH client.","title":"Kebnekaise login servers"},{"location":"login/#using__thinlinc","text":"Download the client from https://www.cendio.com/thinlinc/download . Install it. Windows: Run the downloaded .exe file to install. macOS: Information on the ThinLinc macOS info page . Linux Ubuntu: Download the .deb file. Run sudo dpkg -i PATH-TO-FILE/FILE-YOU-DOWNLOADED.deb Start the client. Enter the name of the server: kebnekaise-tl.hpc2n.umu.se . Enter your username. Go to \u201cOptions\u201d \\(->\\) \u201cSecurity\u201d. Check that authentication method is set to password. Go to \u201cOptions\u201d \\(->\\) \u201cScreen\u201d. Uncheck \u201cFull screen mode\u201d. Enter your HPC2N password. Click \u201cConnect\u201d Click \u201cContinue\u201d when you are being told that the server\u2019s host key is not in the registry. Wait for the ThinLinc desktop to open.","title":"Using ThinLinc"},{"location":"login/#password","text":"You get your first, temporary HPC2N password from this page: HPC2N passwords . That page can also be used to reset your HPC2N password if you have forgotten it. Note that you are authenticating through SUPR, using that service\u2019s login credentials! Warning The HPC2N password and the SUPR password are separate! The HPC2N password and your university/department password are also separate! Exercise Login to Kebnekaise. If you are using ThinLinc, first install the ThinLinc client. If you are using another SSH client, install it first if you have not already done so.","title":"Password"},{"location":"login/#change__password","text":"Exercise: Change your password after first login ONLY do this if you have logged in for the first time/is still using the termporary password you got from the HPC2N password reset service! Changing password is done using the passwd command: passwd Use a good password that combines letters of different case. Do not use dictionary words. Avoid using the same password that you also use in other places. It will first ask for your current password. Type in that and press enter. Then type in the new password, enter, and repeat. You have changed the password.","title":"Change password"},{"location":"login/#file__transfers","text":"We are not going to transfer any files as part of this course, but you may have to do so as part of your workflow when using Kebnekaise (or another HPC centre) for your research. This section will only talk briefly about file transfers. You can find more information and examples on HPC2N\u2019s File transfer documentation .","title":"File transfers"},{"location":"login/#linux__os__x","text":"","title":"Linux, OS X"},{"location":"login/#scp","text":"SCP (Secure CoPy) is a simple way of transferring files between two machines that use the SSH (Secure SHell) protocol. You may use SCP to connect to any system where you have SSH (log-in) access. These examples show how to use scp from the command-line. Graphical programs exists for doing scp transfer. The command-lone scp program should already be installed. Remote to local Transfer a file from Kebnekaise to your local system, while on your local system scp username@kebnekaise.hpc2n.umu.se:file . Local to remote Transfer a local file to Kebnekaise, while on your local system scp file username@kebnekaise.hpc2n.umu.se:file Recursive directory copy from a local system to a remote system The directory sourcedirectory is here copied as a subdirectory to somedir scp -r sourcedirectory/ username@kebnekaise.hpc2n.umu.se:somedir/","title":"scp"},{"location":"login/#sftp","text":"SFTP (SSH File Transfer Protocol or sometimes called Secure File Transfer Protocol) is a network protocol that provides file transfer over a reliable data stream. SFTP is a command -line program on most Unix, Linux, and Mac OS X systems. It is also available as a protocol choice in some graphical file transfer programs. Example: From a local system to a remote system enterprise-d [ ~ ] $ sftp user@kebnekaise.hpc2n.umu.se Connecting to kebnekaise.hpc2n.umu.se... user@kebnekaise.hpc2n.umu.se ' s password: sftp> put file.c C/file.c Uploading file.c to /home/u/user/C/file.c file.c 100 % 1 0 .0KB/s 00 :00 sftp> put -P irf.png pic/ Uploading irf.png to /home/u/user/pic/irf.png irf.png 100 % 2100 2 .1KB/s 00 :00 sftp>","title":"sftp"},{"location":"login/#windows","text":"Here you need to download a client: WinSCP, FileZilla (sftp), PSCP/PSFTP, \u2026 You can transfer with sftp or scp. There is documentation in HPC2N\u2019s documentation pages for Windows file transfers .","title":"Windows"},{"location":"login/#editors","text":"Since the editors on a Linux system are different to those you may be familiar with from Windows or macOS, here follows a short overview. There are command-line editors and graphical editors. If you are connecting with a regular SSH client, it will be simplest to use a command-line editor. If you are using ThinLinc, you can use command-line editors or graphical editors as you want.","title":"Editors"},{"location":"login/#command-line","text":"These are all good editors for using on the command line: nano vi , vim emacs They are all installed on Kebnekaise. Of these, vi/vim as well as emacs are probably the most powerful, though the latter is better in a GUI environment. The easiest editor to use if you are not familiar with any of them is nano . Nano Starting \u201cnano\u201d: Type nano FILENAME on the command line and press Enter . FILENAME is whatever you want to call your file. If FILENAME is a file that already exists, nano will open the file. If it dows not exist, it will be created. You now get an editor that looks like this: First thing to notice is that many of the commands are listed at the bottom. The ^ before the letter-commands means you should press CTRL and then the letter (while keeping CTRL down). Your prompt is in the editor window itself, and you can just type (or copy and paste) the content you want in your file. When you want to exit (and possibly save), you press CTRL and then x while holding CTRL down (this is written CTRL-x or ^x ). nano will ask you if you want to save the content of the buffer to the file. After that it will exit. There is a manual for nano here .","title":"Command-line"},{"location":"login/#gui","text":"If you are connecting with ThinLinc , you will be presented with a graphical user interface (GUI). From there you can either open a terminal window/shell ( Applications -> System Tools -> MATE Terminal ) or you can choose editors from the menu by going to Applications -> Accessories . This gives several editor options, of which these have a graphical interface: Text Editor (gedit) Pluma - the default editor on the MATE desktop environments (that Thinlinc runs) Atom - not just an editor, but an IDE Emacs (GUI) NEdit \u201cNirvana Text Editor\u201d If you are not familiar with any of these, a good recommendation would be to use Text Editor/gedit . Text Editor/gedit Starting \u201c gedit \u201d: From the menu, choose Applications -> Accessories -> Text Editor . You then get a window that looks like this: You can open files by clicking \u201c Open \u201d in the top menu. Clicking the small file icon with a green plus will create a new document. Save by clicking \u201c Save \u201d in the menu. The menu on the top right (the three horizontal lines) gives you several other options, including \u201c Find \u201d and \u201c Find and Replace \u201d. Keypoints You can login with ThinLinc or another SSH client ThinLinc is easiest if you need a GUI There are several command-line editors: vi/vim, nano, emacs, \u2026 And several GUI editors, which works best when using ThinLinc: gedit, pluma, atom, emacs (gui), nedit, \u2026","title":"GUI"},{"location":"modules/","text":"The Module System (Lmod) \u00b6 Objectives Learn the basics of the module system which is used to access most of the software on Kebnekaise Try some of the most used commands for the module system: find/list software modules load/unload software modules Learn about compiler toolchains Most programs are accessed by first loading them as a \u2018module\u2019. Modules are: used to set up your environment (paths to executables, libraries, etc.) for using a particular (set of) software package(s) a tool to help users manage their Unix/Linux shell environment, allowing groups of related environment-variable settings to be made or removed dynamically allows having multiple versions of a program or package available by just loading the proper module are installed in a hierarchial layout. This means that some modules are only available after loading a specific compiler and/or MPI version. Useful commands (Lmod) \u00b6 See which modules exists: module spider or ml spider See which versions exist of a specific module: module spider MODULE or ml spider MODULE See prerequisites and how to load a specfic version of a module: module spider MODULE/VERSION or ml spider MODULE/VERSION List modules depending only on what is currently loaded: module avail or ml av See which modules are currently loaded: module list or ml Loading a module: module load MODULE or ml MODULE Loading a specific version of a module: module load MODULE/VERSION or ml MODULE/VERSION Unload a module: module unload MODULE or ml -MODULE Get more information about a module: ml show MODULE or module show MODULE Unload all modules except the \u2018sticky\u2019 modules: module purge or ml purge Important! Not all the modules (and versions) are the same on the skylake/broadwell nodes and the zen3/zen4 nodes. The regular login node kebnekaise.hpc2n.umu.se has the modules available on skylake/broadwell nodes. (ThinLinc: kebnekaise-tl.hpc2n.umu.se ) In order to check if a module is available on the zen3/zen4 nodes, login to kebnekaise-amd.hpc2n.umu.se . (ThinLinc: kebnekaise-amd-tl.hpc2n.umu.se ). Hint Code-along! Example: checking which versions exist of the module \u2018Python\u2019 on the regular login node b-an01 [ ~ ] $ ml spider Python --------------------------------------------------------------------------------------------------------- Python: --------------------------------------------------------------------------------------------------------- Description: Python is a programming language that lets you work more quickly and integrate your systems more effectively. Versions: Python/2.7.15 Python/2.7.16 Python/2.7.18-bare Python/2.7.18 Python/3.7.2 Python/3.7.4 Python/3.8.2 Python/3.8.6 Python/3.9.5-bare Python/3.9.5 Python/3.9.6-bare Python/3.9.6 Python/3.10.4-bare Python/3.10.4 Python/3.10.8-bare Python/3.10.8 Python/3.11.3 Python/3.11.5 Other possible modules matches: Biopython Boost.Python Brotli-python GitPython IPython Python-bundle-PyPI flatbuffers-python ... --------------------------------------------------------------------------------------------------------- To find other possible module matches execute: $ module -r spider '.*Python.*' --------------------------------------------------------------------------------------------------------- For detailed information about a specific \"Python\" package ( including how to load the modules ) use the module ' s full name. Note that names that have a trailing ( E ) are extensions provided by other modules. For example: $ module spider Python/3.11.5 --------------------------------------------------------------------------------------------------------- b-an01 [ ~ ] $ Example: Check how to load a specific Python version (3.11.5 in this example) on the regular login node b-an01 [ ~ ] $ ml spider Python/3.11.5 --------------------------------------------------------------------------------------------------------- Python: Python/3.11.5 --------------------------------------------------------------------------------------------------------- Description: Python is a programming language that lets you work more quickly and integrate your systems more effectively. You will need to load all module ( s ) on any one of the lines below before the \"Python/3.11.5\" module is available to load. GCCcore/13.2.0 This module provides the following extensions: flit_core/3.9.0 ( E ) , packaging/23.2 ( E ) , pip/23.2.1 ( E ) , setuptools-scm/8.0.4 ( E ) , setuptools/68.2.2 ( E ) , tomli/2.0.1 ( E ) , typing_extensions/4.8.0 ( E ) , wheel/0.41.2 ( E ) Help: Description =========== Python is a programming language that lets you work more quickly and integrate your systems more effectively. More information ================ - Homepage: https://python.org/ Included extensions =================== flit_core-3.9.0, packaging-23.2, pip-23.2.1, setuptools-68.2.2, setuptools- scm-8.0.4, tomli-2.0.1, typing_extensions-4.8.0, wheel-0.41.2 b-an01 [ ~ ] $ Example: Load Python/3.11.5 and its prerequisite(s) (on the regular login node) Here we also show the loaded module before and after the load. For illustration, we use first ml and then module list : b-an01 [ ~ ] $ ml Currently Loaded Modules: 1 ) snicenvironment ( S ) 2 ) systemdefault ( S ) Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ module load GCCcore/13.2.0 Python/3.11.5 b-an01 [ ~ ] $ module list Currently Loaded Modules: 1 ) snicenvironment ( S ) 4 ) zlib/1.2.13 7 ) ncurses/6.4 10 ) SQLite/3.43.1 13 ) OpenSSL/1.1 2 ) systemdefault ( S ) 5 ) binutils/2.40 8 ) libreadline/8.2 11 ) XZ/5.4.4 14 ) Python/3.11.5 3 ) GCCcore/13.2.0 6 ) bzip2/1.0.8 9 ) Tcl/8.6.13 12 ) libffi/3.4.4 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ Example: Unloading the module Python/3.11.5 (on the regular login node) In this example we unload the module Python/3.11.5 , but not the prerequisite GCCcore/13.2.0 . We also look at the output of module list before and after. b-an01 [ ~ ] $ module list Currently Loaded Modules: 1 ) snicenvironment ( S ) 4 ) zlib/1.2.13 7 ) ncurses/6.4 10 ) SQLite/3.43.1 13 ) OpenSSL/1.1 2 ) systemdefault ( S ) 5 ) binutils/2.40 8 ) libreadline/8.2 11 ) XZ/5.4.4 14 ) Python/3.11.5 3 ) GCCcore/13.2.0 6 ) bzip2/1.0.8 9 ) Tcl/8.6.13 12 ) libffi/3.4.4 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ ml unload Python/3.11.5 b-an01 [ ~ ] $ module list Currently Loaded Modules: 1 ) snicenvironment ( S ) 2 ) systemdefault ( S ) 3 ) GCCcore/13.2.0 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ As you can see, the prerequisite did not get unloaded. This is on purpose, because you may have other things loaded which uses the prerequisite. Example: unloading every module you have loaded, with module purge except the \u2018sticky\u2019 modules (some needed things for the environment) (on the regular login node) First we load some modules. Here Python 3.11.5, SciPy-bundle, and prerequisites for them. We also do module list after loading the modules and after using module purge . b-an01 [ ~ ] $ ml GCC/13.2.0 b-an01 [ ~ ] $ ml Python/3.11.5 ml SciPy-bundle/2023.11 b-an01 [ ~ ] $ ml list Currently Loaded Modules: 1 ) snicenvironment ( S ) 7 ) bzip2/1.0.8 13 ) libffi/3.4.4 19 ) cffi/1.15.1 2 ) systemdefault ( S ) 8 ) ncurses/6.4 14 ) OpenSSL/1.1 20 ) cryptography/41.0.5 3 ) GCCcore/13.2.0 9 ) libreadline/8.2 15 ) Python/3.11.5 21 ) virtualenv/20.24.6 4 ) zlib/1.2.13 10 ) Tcl/8.6.13 16 ) OpenBLAS/0.3.24 22 ) Python-bundle-PyPI/2023.10 5 ) binutils/2.40 11 ) SQLite/3.43.1 17 ) FlexiBLAS/3.3.1 23 ) pybind11/2.11.1 6 ) GCC/13.2.0 12 ) XZ/5.4.4 18 ) FFTW/3.3.10 24 ) SciPy-bundle/2023.11 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ ml purge The following modules were not unloaded: ( Use \"module --force purge\" to unload all ) : 1 ) snicenvironment 2 ) systemdefault b-an01 [ ~ ] $ ml list Currently Loaded Modules: 1 ) snicenvironment ( S ) 2 ) systemdefault ( S ) Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ Note You can do several module load on the same line. Or you can do them one at a time, as you want. The modules have to be loaded in order! You cannot list the prerequisite after the module that needs it! One advantage to loading modules one at a time is that you can then find compatible modules that depend on that version easily. Example: you have loaded GCC/13.2.0 and Python/3.11.5 . You can now do ml av to see which versions of other modules you want to load, say SciPy-bundle, are compatible. If you know the name of the module you want, you can even start writing module load SciPy-bundle/ and press TAB - the system will then autocomplete to the compatible one(s). Exercise Login to kebnekaise-amd (can be easily done with ssh kebnekaise-amd from a terminal window on the regular login node). Check if the versions of Python available differs from on the regular login node. Compiler Toolchains \u00b6 Compiler toolchains load bundles of software making up a complete environment for compiling/using a specific prebuilt software. Includes some/all of: compiler suite, MPI, BLAS, LAPACK, ScaLapack, FFTW, CUDA. Some currently available toolchains (check ml av for versions and full, updated list): GCC : GCC only gcccuda : GCC and CUDA foss : GCC, OpenMPI, OpenBLAS/LAPACK, FFTW, ScaLAPACK gompi : GCC, OpenMPI gompic : GCC, OpenMPI, CUDA gomkl : GCC, OpenMPI, MKL iccifort : icc, ifort iccifortcuda : icc, ifort, CUDA iimpi : icc, ifort, IntelMPI iimpic : iccifort, CUDA, impi intel : icc, ifort, IntelMPI, IntelMKL intel-compilers : icc, ifort (classic and oneAPI) intelcuda : intel and CUDA iompi : iccifort and OpenMPI Exercise Check which versions of the foss toolchain exist. Load one of them. Check which modules you now have loaded. Remove all the (non-sticky) modules. Keypoints The software on Kebnekaise is mostly accessed through the module system. The modules are arranged in a hierarchial layout; many modules have prerequisites that needs to be loaded first. Important commands to the module system: Loading: module load MODULE Unloading: module unload MODULE Unload all modules: module purge List all modules in the system: module spider List versions available of a specific module: module spider MODULE Show how to load a specific module and version: module spider MODULE/VERSION List the modules you have currently loaded: module list Compiler toolchains are modules containing compiler suites and various libraries More information There is more information about the module system and how to work with it in HPC2N\u2019s documentation for the modules system .","title":"The Module System"},{"location":"modules/#the__module__system__lmod","text":"Objectives Learn the basics of the module system which is used to access most of the software on Kebnekaise Try some of the most used commands for the module system: find/list software modules load/unload software modules Learn about compiler toolchains Most programs are accessed by first loading them as a \u2018module\u2019. Modules are: used to set up your environment (paths to executables, libraries, etc.) for using a particular (set of) software package(s) a tool to help users manage their Unix/Linux shell environment, allowing groups of related environment-variable settings to be made or removed dynamically allows having multiple versions of a program or package available by just loading the proper module are installed in a hierarchial layout. This means that some modules are only available after loading a specific compiler and/or MPI version.","title":"The Module System (Lmod)"},{"location":"modules/#useful__commands__lmod","text":"See which modules exists: module spider or ml spider See which versions exist of a specific module: module spider MODULE or ml spider MODULE See prerequisites and how to load a specfic version of a module: module spider MODULE/VERSION or ml spider MODULE/VERSION List modules depending only on what is currently loaded: module avail or ml av See which modules are currently loaded: module list or ml Loading a module: module load MODULE or ml MODULE Loading a specific version of a module: module load MODULE/VERSION or ml MODULE/VERSION Unload a module: module unload MODULE or ml -MODULE Get more information about a module: ml show MODULE or module show MODULE Unload all modules except the \u2018sticky\u2019 modules: module purge or ml purge Important! Not all the modules (and versions) are the same on the skylake/broadwell nodes and the zen3/zen4 nodes. The regular login node kebnekaise.hpc2n.umu.se has the modules available on skylake/broadwell nodes. (ThinLinc: kebnekaise-tl.hpc2n.umu.se ) In order to check if a module is available on the zen3/zen4 nodes, login to kebnekaise-amd.hpc2n.umu.se . (ThinLinc: kebnekaise-amd-tl.hpc2n.umu.se ). Hint Code-along! Example: checking which versions exist of the module \u2018Python\u2019 on the regular login node b-an01 [ ~ ] $ ml spider Python --------------------------------------------------------------------------------------------------------- Python: --------------------------------------------------------------------------------------------------------- Description: Python is a programming language that lets you work more quickly and integrate your systems more effectively. Versions: Python/2.7.15 Python/2.7.16 Python/2.7.18-bare Python/2.7.18 Python/3.7.2 Python/3.7.4 Python/3.8.2 Python/3.8.6 Python/3.9.5-bare Python/3.9.5 Python/3.9.6-bare Python/3.9.6 Python/3.10.4-bare Python/3.10.4 Python/3.10.8-bare Python/3.10.8 Python/3.11.3 Python/3.11.5 Other possible modules matches: Biopython Boost.Python Brotli-python GitPython IPython Python-bundle-PyPI flatbuffers-python ... --------------------------------------------------------------------------------------------------------- To find other possible module matches execute: $ module -r spider '.*Python.*' --------------------------------------------------------------------------------------------------------- For detailed information about a specific \"Python\" package ( including how to load the modules ) use the module ' s full name. Note that names that have a trailing ( E ) are extensions provided by other modules. For example: $ module spider Python/3.11.5 --------------------------------------------------------------------------------------------------------- b-an01 [ ~ ] $ Example: Check how to load a specific Python version (3.11.5 in this example) on the regular login node b-an01 [ ~ ] $ ml spider Python/3.11.5 --------------------------------------------------------------------------------------------------------- Python: Python/3.11.5 --------------------------------------------------------------------------------------------------------- Description: Python is a programming language that lets you work more quickly and integrate your systems more effectively. You will need to load all module ( s ) on any one of the lines below before the \"Python/3.11.5\" module is available to load. GCCcore/13.2.0 This module provides the following extensions: flit_core/3.9.0 ( E ) , packaging/23.2 ( E ) , pip/23.2.1 ( E ) , setuptools-scm/8.0.4 ( E ) , setuptools/68.2.2 ( E ) , tomli/2.0.1 ( E ) , typing_extensions/4.8.0 ( E ) , wheel/0.41.2 ( E ) Help: Description =========== Python is a programming language that lets you work more quickly and integrate your systems more effectively. More information ================ - Homepage: https://python.org/ Included extensions =================== flit_core-3.9.0, packaging-23.2, pip-23.2.1, setuptools-68.2.2, setuptools- scm-8.0.4, tomli-2.0.1, typing_extensions-4.8.0, wheel-0.41.2 b-an01 [ ~ ] $ Example: Load Python/3.11.5 and its prerequisite(s) (on the regular login node) Here we also show the loaded module before and after the load. For illustration, we use first ml and then module list : b-an01 [ ~ ] $ ml Currently Loaded Modules: 1 ) snicenvironment ( S ) 2 ) systemdefault ( S ) Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ module load GCCcore/13.2.0 Python/3.11.5 b-an01 [ ~ ] $ module list Currently Loaded Modules: 1 ) snicenvironment ( S ) 4 ) zlib/1.2.13 7 ) ncurses/6.4 10 ) SQLite/3.43.1 13 ) OpenSSL/1.1 2 ) systemdefault ( S ) 5 ) binutils/2.40 8 ) libreadline/8.2 11 ) XZ/5.4.4 14 ) Python/3.11.5 3 ) GCCcore/13.2.0 6 ) bzip2/1.0.8 9 ) Tcl/8.6.13 12 ) libffi/3.4.4 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ Example: Unloading the module Python/3.11.5 (on the regular login node) In this example we unload the module Python/3.11.5 , but not the prerequisite GCCcore/13.2.0 . We also look at the output of module list before and after. b-an01 [ ~ ] $ module list Currently Loaded Modules: 1 ) snicenvironment ( S ) 4 ) zlib/1.2.13 7 ) ncurses/6.4 10 ) SQLite/3.43.1 13 ) OpenSSL/1.1 2 ) systemdefault ( S ) 5 ) binutils/2.40 8 ) libreadline/8.2 11 ) XZ/5.4.4 14 ) Python/3.11.5 3 ) GCCcore/13.2.0 6 ) bzip2/1.0.8 9 ) Tcl/8.6.13 12 ) libffi/3.4.4 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ ml unload Python/3.11.5 b-an01 [ ~ ] $ module list Currently Loaded Modules: 1 ) snicenvironment ( S ) 2 ) systemdefault ( S ) 3 ) GCCcore/13.2.0 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ As you can see, the prerequisite did not get unloaded. This is on purpose, because you may have other things loaded which uses the prerequisite. Example: unloading every module you have loaded, with module purge except the \u2018sticky\u2019 modules (some needed things for the environment) (on the regular login node) First we load some modules. Here Python 3.11.5, SciPy-bundle, and prerequisites for them. We also do module list after loading the modules and after using module purge . b-an01 [ ~ ] $ ml GCC/13.2.0 b-an01 [ ~ ] $ ml Python/3.11.5 ml SciPy-bundle/2023.11 b-an01 [ ~ ] $ ml list Currently Loaded Modules: 1 ) snicenvironment ( S ) 7 ) bzip2/1.0.8 13 ) libffi/3.4.4 19 ) cffi/1.15.1 2 ) systemdefault ( S ) 8 ) ncurses/6.4 14 ) OpenSSL/1.1 20 ) cryptography/41.0.5 3 ) GCCcore/13.2.0 9 ) libreadline/8.2 15 ) Python/3.11.5 21 ) virtualenv/20.24.6 4 ) zlib/1.2.13 10 ) Tcl/8.6.13 16 ) OpenBLAS/0.3.24 22 ) Python-bundle-PyPI/2023.10 5 ) binutils/2.40 11 ) SQLite/3.43.1 17 ) FlexiBLAS/3.3.1 23 ) pybind11/2.11.1 6 ) GCC/13.2.0 12 ) XZ/5.4.4 18 ) FFTW/3.3.10 24 ) SciPy-bundle/2023.11 Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ ml purge The following modules were not unloaded: ( Use \"module --force purge\" to unload all ) : 1 ) snicenvironment 2 ) systemdefault b-an01 [ ~ ] $ ml list Currently Loaded Modules: 1 ) snicenvironment ( S ) 2 ) systemdefault ( S ) Where: S: Module is Sticky, requires --force to unload or purge b-an01 [ ~ ] $ Note You can do several module load on the same line. Or you can do them one at a time, as you want. The modules have to be loaded in order! You cannot list the prerequisite after the module that needs it! One advantage to loading modules one at a time is that you can then find compatible modules that depend on that version easily. Example: you have loaded GCC/13.2.0 and Python/3.11.5 . You can now do ml av to see which versions of other modules you want to load, say SciPy-bundle, are compatible. If you know the name of the module you want, you can even start writing module load SciPy-bundle/ and press TAB - the system will then autocomplete to the compatible one(s). Exercise Login to kebnekaise-amd (can be easily done with ssh kebnekaise-amd from a terminal window on the regular login node). Check if the versions of Python available differs from on the regular login node.","title":"Useful commands (Lmod)"},{"location":"modules/#compiler__toolchains","text":"Compiler toolchains load bundles of software making up a complete environment for compiling/using a specific prebuilt software. Includes some/all of: compiler suite, MPI, BLAS, LAPACK, ScaLapack, FFTW, CUDA. Some currently available toolchains (check ml av for versions and full, updated list): GCC : GCC only gcccuda : GCC and CUDA foss : GCC, OpenMPI, OpenBLAS/LAPACK, FFTW, ScaLAPACK gompi : GCC, OpenMPI gompic : GCC, OpenMPI, CUDA gomkl : GCC, OpenMPI, MKL iccifort : icc, ifort iccifortcuda : icc, ifort, CUDA iimpi : icc, ifort, IntelMPI iimpic : iccifort, CUDA, impi intel : icc, ifort, IntelMPI, IntelMKL intel-compilers : icc, ifort (classic and oneAPI) intelcuda : intel and CUDA iompi : iccifort and OpenMPI Exercise Check which versions of the foss toolchain exist. Load one of them. Check which modules you now have loaded. Remove all the (non-sticky) modules. Keypoints The software on Kebnekaise is mostly accessed through the module system. The modules are arranged in a hierarchial layout; many modules have prerequisites that needs to be loaded first. Important commands to the module system: Loading: module load MODULE Unloading: module unload MODULE Unload all modules: module purge List all modules in the system: module spider List versions available of a specific module: module spider MODULE Show how to load a specific module and version: module spider MODULE/VERSION List the modules you have currently loaded: module list Compiler toolchains are modules containing compiler suites and various libraries More information There is more information about the module system and how to work with it in HPC2N\u2019s documentation for the modules system .","title":"Compiler Toolchains"},{"location":"projectsaccounts/","text":"Projects - compute and storage \u00b6 Note In order to have an account at HPC2N, you need to be a member of a compute project. You can either join a project or apply for one yourself (if you fulfill the requirements). There are both storage projects and compute projects. The storage projects are for when the amount of storage included with the compute project is not enough. Important You cannot have a storage project without a compute project! Kebnekaise is only open for local project requests! The PI must be affiliated with UmU, LTU, IRF, MiUN, or SLU. You can still add members (join) from anywhere. Application process \u00b6 Apply for compute projects in SUPR . Login to SUPR (create SUPR account if you do not have one). Click \u201cRounds\u201d in the left menu. Pick \u201cCompute Rounds\u201d. Pick \u201cCentre Local Compute\u201d. Pick \u201cHPC2N Local Compute YYYY\u201d. Choose \u201cCreate New Proposal for HPC2N Local Compute YYYY\u201d. Create from scratch or use earlier proposal as starting point. Agree to the default storage if 500GB is enough. More information: https://supr.naiss.se/round/open_or_pending_type/?type=Centre+Local+Compute If the above mentioned default storage is not enough, you will need to apply for a Local storage project : https://supr.naiss.se/round/open_or_pending_type/?type=Centre+Local+Storage Info As default, you have 25GB in your home directory. If you need more, you/your PI can accept the \u201cdefault storage\u201d you will be offered after applying for compute resources. The default storage is 500GB. If you need more than that, you/your PI will have to apply for a storage project. When you have both, link them together. It is done from the storage project. This way all members of the compute project also becomes members of the storage project. After applying on SUPR, the project(s) will be reviewed. Linking a compute project to a storage project \u00b6 Before linking (SUPR): 2. Pick a compute project to link: 3. Showing linked projects: 4. Members of the storage project after linking: Accounts \u00b6 When you have a project / have become member of a project, you can apply for an account at HPC2N. This is done in SUPR, under \u201cAccounts\u201d: https://supr.naiss.se/account/ . Your account request will be processed within a week. You will then get an email with information about logging in and links to getting started information. More information on the account process can be found on HPC2N\u2019s documentation pages: https://www.hpc2n.umu.se/documentation/access-and-accounts/users","title":"Projects and Accounts"},{"location":"projectsaccounts/#projects__-__compute__and__storage","text":"Note In order to have an account at HPC2N, you need to be a member of a compute project. You can either join a project or apply for one yourself (if you fulfill the requirements). There are both storage projects and compute projects. The storage projects are for when the amount of storage included with the compute project is not enough. Important You cannot have a storage project without a compute project! Kebnekaise is only open for local project requests! The PI must be affiliated with UmU, LTU, IRF, MiUN, or SLU. You can still add members (join) from anywhere.","title":"Projects - compute and storage"},{"location":"projectsaccounts/#application__process","text":"Apply for compute projects in SUPR . Login to SUPR (create SUPR account if you do not have one). Click \u201cRounds\u201d in the left menu. Pick \u201cCompute Rounds\u201d. Pick \u201cCentre Local Compute\u201d. Pick \u201cHPC2N Local Compute YYYY\u201d. Choose \u201cCreate New Proposal for HPC2N Local Compute YYYY\u201d. Create from scratch or use earlier proposal as starting point. Agree to the default storage if 500GB is enough. More information: https://supr.naiss.se/round/open_or_pending_type/?type=Centre+Local+Compute If the above mentioned default storage is not enough, you will need to apply for a Local storage project : https://supr.naiss.se/round/open_or_pending_type/?type=Centre+Local+Storage Info As default, you have 25GB in your home directory. If you need more, you/your PI can accept the \u201cdefault storage\u201d you will be offered after applying for compute resources. The default storage is 500GB. If you need more than that, you/your PI will have to apply for a storage project. When you have both, link them together. It is done from the storage project. This way all members of the compute project also becomes members of the storage project. After applying on SUPR, the project(s) will be reviewed.","title":"Application process"},{"location":"projectsaccounts/#linking__a__compute__project__to__a__storage__project","text":"Before linking (SUPR): 2. Pick a compute project to link: 3. Showing linked projects: 4. Members of the storage project after linking:","title":"Linking a compute project to a storage project"},{"location":"projectsaccounts/#accounts","text":"When you have a project / have become member of a project, you can apply for an account at HPC2N. This is done in SUPR, under \u201cAccounts\u201d: https://supr.naiss.se/account/ . Your account request will be processed within a week. You will then get an email with information about logging in and links to getting started information. More information on the account process can be found on HPC2N\u2019s documentation pages: https://www.hpc2n.umu.se/documentation/access-and-accounts/users","title":"Accounts"},{"location":"simple/","text":"Simple batch script examples \u00b6 Objectives See and try out different types of simple batch script examples. Try using constraints: how to allocate specific CPUs. Try using constraints: how to allocate specific GPUs. For consistency, I have given all the example batch scripts the suffix .sh even though it is not required. Another commonly used suffix is .batch , but any or none will work. You need to compile any programs mentioned in a batch script in order to run the examples, except for compile-run.sh and the CUDA examples, which includes compilation. Important The course project has the following project ID: hpc2n2024-084 In order to use it in a batch job, add this to the batch script: #SBATCH -A hpc2n2024-084 We have a storage project linked to the compute project: intro-hpc2n . You find it in /proj/nobackup/intro-hpc2n . Remember to create your own directory under it. Hint Try to change the C programs, add different programs, and in general play around with the examples! Note For these test examples I would suggest using the foss compiler toolchain, version 2022b, unless otherwise specified. If you decide to use a different one, you will have to make changes to some of the batch scripts. To submit a job script, do sbatch JOBSCRIPT In most of the examples, I name the executable when I compile. The flag -o tells the compiler you want to name the executable. If you don\u2019t include that and a name, you will get an executable named a.out . Of course, you do not have to name the executable hello . This is just an example. In general, I have named all the executables the same as the program (without the suffix). Serial batch job \u00b6 To compile a serial program, like hello.c with gcc do: gcc hello.c -o hello Sample batch script (hello.sh) #!/bin/bash # Project id - change to your own after the course! #SBATCH -A hpc2n2024-084 # Asking for 1 core #SBATCH -n 1 # Asking for a walltime of 1 min #SBATCH --time=00:01:00 # Purge modules before loading new ones in a script. ml purge > /dev/null 2 > & 1 ml foss/2022b ./hello Exercise: serial job Submit the job with sbatch . Check on it with squeue --me . Take a look at the output ( slurm-JOBID.out ) with nano or your favourite editor. MPI batch job \u00b6 To compile an MPI program, like mpi_hello.c (and create an executable named mpi_hello ) with gcc, do: mpicc mpi_hello.c -o mpi_hello Sample batch script (mpi_hello.sh) #!/bin/bash # Remember to change this to your own Project ID after the course! #SBATCH -A hpc2n2024-084 # Number of tasks - default is 1 core per task #SBATCH -n 14 #SBATCH --time=00:05:00 # It is always a good idea to do ml purge before loading other modules ml purge > /dev/null 2 > & 1 ml add foss/2022b # Use srun since this is an MPI program srun ./mpi_hello Exercise: MPI job Submit the job with sbatch . Check on it with squeue --me . Take a look at the output ( slurm-JOBID.out ) with nano or your favourite editor. Try running it more than once to see that the order of the tasks are random. OpenMP batch job \u00b6 To compile an OpenMP program, like omp_hello.c (and create an executable named omp_hello ) with gcc, do: gcc -fopenmp omp_hello.c -o omp_hello Sample batch script (omp_hello.sh) #!/bin/bash #SBATCH -A hpc2n2024-084 # Number of cores per task #SBATCH -c 28 #SBATCH --time=00:05:00 # It is always a good idea to do ml purge before loading other modules ml purge > /dev/null 2 > & 1 ml add foss/2022b # Set OMP_NUM_THREADS to the same value as -c with a fallback in case it isn't set. # SLURM_CPUS_PER_TASK is set to the value of -c, but only if -c is explicitly set if [ -n \" $SLURM_CPUS_PER_TASK \" ] ; then omp_threads = $SLURM_CPUS_PER_TASK else omp_threads = 1 fi export OMP_NUM_THREADS = $omp_threads ./omp_hello Exercise: OpenMP job Set OMP_NUM_THREADS to some value between 1 and 28 ( export OMP_NUM_THREADS=value ). Submit the job with sbatch . Take a look at the output ( slurm-JOBID.out ) with nano or your favourite editor. Change the value of OMP_NUM_THREADS ). Submit it again and check on the output to see the change. Multiple serial jobs from same submit file \u00b6 This submit file shows one way of running several programs from inside the same submit file. To run this example, you need to compile the following serial C programs: hello.c Greeting.c Adding2.c Mult2.c When the C programs have been compiled, submit the multiple-serial.sh program: multiple-serial.sh All jobs run at the same time, so you need as many cores as they need combined. You also need to ask for long enough time that even the longest of the jobs will finish. Note that here you submit with srun even if it is serial jobs. You use & to send the job to the background. Also note the wait at the end. If you do not add that, the whole batch job will finish when the first of the jobs inside ends. #!/bin/bash #SBATCH -A hpc2n2024-084 # Add enough cores that all jobs can run at the same time #SBATCH -n 5 # Make sure that the time is long enough that the longest job will have time to finish #SBATCH --time=00:05:00 module purge > /dev/null 2 > & 1 ml foss/2022b srun -n 1 --exclusive ./hello & srun -n 1 --exclusive ./Greeting & srun -n 1 --exclusive ./Adding2 10 20 & srun -n 1 --exclusive /bin/hostname & srun -n 1 --exclusive ./Mult2 10 2 wait Exercise: multiple serial jobs Compile the above mentioned programs. Submit the batch script with sbatch multiple-serial.sh If you run it several times you will notice that the order is random. Job arrays \u00b6 Job arrays offer a mechanism for submitting and managing collections of similar jobs. All jobs must have the same initial options (e.g. size, time limit, etc.), however it is possible to change some of these options after the job has begun execution using the scontrol command specifying the JobID of the array or individual ArrayJobID. More information here on the official Slurm documentation pages . To try an example, we have included a small Python script hello-world-array.py and a batch script hello-world-array.sh . Both can also be found in the exercises/simple directory you have cloned. hello-world-array.py # import sys library (we need this for the command line args) import sys # print task number print ( 'Hello world! from task number: ' , sys.argv [ 1 ]) hello-world-array.sh #!/bin/bash # This is a very simple example of how to run a Python script with a job array #SBATCH -A hpc2n2024-084 # Change to your own after the course! #SBATCH --time=00:05:00 # Asking for 5 minutes #SBATCH --array=1-10 # how many tasks in the array #SBATCH -c 1 # Asking for 1 core # one core per task #SBATCH -o hello-world-%j-%a.out # Load any modules you need, here for Python 3.11.3 ml GCC/12.3.0 Python/3.11.3 # Run your Python script srun python hello-world-array.py $SLURM_ARRAY_TASK_ID Exercise: job arrays Submit the batch script. Look at the output files. Change the number of tasks in the array. Rerun. See the change. Multiple parallel jobs sequentially \u00b6 To run this example, you need to compile the following parallel C programs: mpi_hello.c mpi_greeting.c mpi_hi.c When the MPI C programs have been compiled, submit the multiple-parallel-sequential.sh program: #!/bin/bash #SBATCH -A hpc2n2024-084 # Since the files are run sequentially I only need enough cores for the largest of them to run #SBATCH -c 28 # Remember to ask for enough time for all jobs to complete #SBATCH --time=00:10:00 module purge > /dev/null 2 > & 1 ml foss/2022b # Here 14 tasks with 2 cores per task. Output to file - not needed if your job creates output in a file directly # In this example I also copy the output somewhere else and then run another executable. srun -n 14 -c 2 ./mpi_hello > myoutput1 2 > & 1 cp myoutput1 mydatadir srun -n 14 -c 2 ./mpi_greeting > myoutput2 2 > & 1 cp myoutput2 mydatadir srun -n 14 -c 2 ./mpi_hi > myoutput3 2 > & 1 cp myoutput3 mydatadir sbatch multiple-parallel-sequential.sh Exercise: multiple parallel jobs sequentially Submit the job: sbatch multiple-parallel-sequential.sh See that output data are thrown to files and copied to the directory mydatadir . Multiple parallel jobs simultaneously \u00b6 To run this example, you need to compile the following parallel C programs: mpi_hello.c mpi_greeting.c mpi_hi.c As before, we recommend using the foss/2022b module for this. If you use a different one you need to change it in the multiple-parallel-simultaneous.sh batch script. When the MPI C programs have been compiled, submit the multiple-parallel-simultaneous.sh program: #!/bin/bash #SBATCH -A hpc2n2024-084 # Since the files run simultaneously I need enough cores for all of them to run #SBATCH -n 56 # Remember to ask for enough time for all jobs to complete #SBATCH --time=00:10:00 module purge > /dev/null 2 > & 1 ml foss/2022b srun -n 14 --exclusive ./mpi_hello & srun -n 14 --exclusive ./mpi_greeting & srun -n 14 --exclusive ./mpi_hi & wait Just like for the multiple serial jobs simultaneously example, you need to add wait to make sure the batch job will not finish when the first of the jobs in it finishes. Exercise: multiple parallel jobs simultaneously When you have compiled the needed programs, as mentioned above, submit with sbatch multiple-parallel-simultaneous.sh Compiling and running in the batch job \u00b6 Sometimes you have a program that takes a long time to compile, or that you need to recompile before each run. To see a simple example of compiling and running from the batch job, look at the batch script compile-run.sh . In this case it compiles and runs the mpi_hello.c program. compile-run.sh #!/bin/bash # CHANGE THE PROJECT ID TO YOUR OWN PROJECT ID AFTER THE COURSE! #SBATCH -A hpc2n2024-084 #Name the job, for easier finding in the list #SBATCH -J compiler-run #SBATCH -t 00:10:00 #SBATCH -n 12 ml purge > /dev/null 2 > & 1 ml foss/2022b mpicc mpi_hello.c -o mpi_hello mpirun ./mpi_hello Exercise: compile and run in a batch job This batch script can be submitted directly, without compiling anything first, as that happens in the batch script. Try submitting it with sbatch and see what happens. Which files are created? You could try changing the program it compiles and runs to a different one. Remember to change the compiler if you are not using an MPI program. Getting errors and outputs in separate files \u00b6 As a default, Slurm throws both errors and other output to the same file, named slurm-JOBID.out . If you want the errors and other output to separate files, you can do as in the example separate-err-out.sh : #!/bin/bash # Remember to change this to your own Project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH -n 8 #SBATCH --time=00:05:00 # Putting the output in a separate output file and the errors in an # error file instead of putting it all in slurm-JOBID.out # Note the environment variable %J, which contains the job ID. It is handy to # avoid naming the files the same for different runs, and thus overwriting them. #SBATCH --error=job.%J.err #SBATCH --output=job.%J.out ml purge > /dev/null 2 > & 1 ml foss/2022b mpirun ./mpi_hello You need the mpi_hello.c file compiled (and the executable named mpi_hello ) for this to run without changes. Of course, you can also just add your own programs. Exercise: errors and outputs in separate files Compile the file mpi_hello.s after loading the module foss/2022b . Submit the job script with sbatch . See that separate output and error files are created. CUDA/GPU programs \u00b6 To run programs/software that uses GPUs, you need to allocated GPUs in the job script. They will not be allocated by your program. To compile a cuda program, like hello-world.cu you need to load a toolchain containing CUDA compilers/load CUDA compilers. To run a piece of software that uses GPUs, you need to load a module version which is GPU aware. In many cases there are several versions of a module, only some of which are for running on GPUs. Important Remember to check the modules, versions, and prerequisites! Also make sure you check for the correct node type. Some of the GPUs are on Intel nodes (check modules on kebnekaise.hpc2n.umu.se ), some on AMD nodes (check modules on kebnekaise-amd.hpc2n.umu.se ). V100 - Intel Skylake \u00b6 This example runs a small CUDA code. We recommend fosscuda/2020b (contains GCC , OpenMPI , OpenBLAS / LAPACK , FFTW , ScaLAPACK , and CUDA ) or intelcuda/2019a (contains icc , ifort , IntelMPI , IntelMKL , and CUDA ) Sample batch script gpu-skylake.sh #!/bin/bash # This job script is for running on 1 V100 GPU. # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=v100:1 ml purge > /dev/null 2 > & 1 ml fosscuda/2020b nvcc hello-world.cu -o hello ./hello The batch script gpu.sh compiles and runs a small cuda program called hello-world.cu . Exercise: V100 GPU job To submit it, just do: sbatch gpu.sh Use squeue --me or scontrol show job JOBID to see that the job runs in the correct partition/node types. A100 - AMD Zen3 \u00b6 Remember, in order to find the correct modules, as well as compile a program if you need that, you must login to one of the AMD login nodes with either SSH ( kebnekaise-amd.hpc2n.umu.se ) or ThinLinc ( kebnekaise-amd-tl.kebnekaise.hpc2n.umu.se ). The job can be submitted from the regular login node, though. Exercise: login to the AMD login node and find a suitable module If you are logged in to the regular Kebnekaise login node, then you can easiest login to the AMD login node by typing this in a terminal window: ssh kebnekaise-amd.hpc2n.umu.se After that, you check for a suitable CUDA toolchain: ml spider CUDA . You can then load it (here CUDA/11.7.0 ) and use nvcc to compile the program hello-world.cu : ml CUDA/11.7.0 nvcc hello-world.cu -o hello Now logout from the AMD login node again. The batch script gpu-a100.sh compiles and runs a small cuda program called hello-world.cu . Sample A100 GPU job script: gpu-a100.sh #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=a100:1 ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 nvcc hello-world.cu -o hello ./hello Exercise: A100 GPU batch jobs The above script is found in the same directory as the other exercises ( intro-course/exercises/simple ). You can submit it directory: sbatch gpu-a100.sh Like for the A100, you are encouraged to use squeue --me and/or scontrol show job JOBID to see that the job gets the correct partition/node type allocated. A40 - Intel broadwell \u00b6 Kebnekaise also has a few of the A40 GPUs. These are placed on Intel broadwell nodes. In order to run on these, you add this to your batch script: #SBATCH --gpus=a40:number where number is 1 or 2 (the number of GPU cards). You can find the available modules on the regular login node, kebnekaise.hpc2n.umu.se . L40s - AMD Zen4 \u00b6 Since these GPUs are located on AMD Zen4 nodes, you need to login to kebnekaise-amd.hpc2n.umu.se to check available modules. Then, to ask for these nodes in your batch script, you add: #SBATCH --gpus=l40s:number where number is 1 or 2 (the number of GPU cards). H100 - AMD Zen4 \u00b6 The H100 GPUs are located on AMD Zen4 nodes. You can find the available modules by logging in to kebnekaise-amd.hpc2n.umu.se . You ask for these GPUs in your batch script by adding: #SBATCH --gpus=h100:number where number is 1, 2, 3, or 4 (the number of GPU cards you want to allocate). A6000 - AMD Zen4 \u00b6 The A6000 GPUs are placed on AMD Zen4 nodes. That means you can find the available modules by logging in to kebnekaise-amd.hpc2n.umu.se . To run on these GPUs, add this to your batch script: #SBATCH --gpus=a6000:number where number is 1 or 2 (the number of GPU cards you want to allocated). MI100 - AMD Zen3 \u00b6 The MI100 GPUs are located on AMD Zen3 nodes. You can find the available modules by logging in to kebnekaise-amd.hpc2n.umu.se . To allocate MI100 GPUs, add this to your batch script: #SBATCH --gpus=mi100:number where number is 1 or 2 (the number of GPU cards). GPU features \u00b6 Sample batch script for allocating any AMD GPU #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=1 #SBATCH -C amd_gpu ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 ./myGPUcode Sample batch script for allocating any Nvidia GPU #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=1 #SBATCH -C nvidia_gpu ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 ./myGPUcode Sample batch script for allocating any Nvidia GPU on Intel node #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=1 #SBATCH -C 'nvidia_gpu&intel_cpu' ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 ./myGPUcode Sample batch script for allocating any GPU with AI features and on a Zen node #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=1 #SBATCH -C ''zen3|zen4'&GPU_AI' ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 ./myGPUcode Exercise: GPU features In order to run these examples, you can change ./myGPUcode to nvcc hello-world.cu -o hello ./hello or any other GPU program of your choice. The gpu-features.sh example script in the exercises/simple directory is prepared for the \u201cany GPU with AI features and on a Zen node\u201d. You can either run it as is, or make changes to it and try any of the other combinations here (or try new combinations yourself). Check with squeue --me which partition/node type the job ends up in, and that it fits. More information can be found with scontrol show job JOBID . Starting JupyterLab \u00b6 On Kebnekaise, it is possible to run JupyterLab. This is done through a batch job, and is described in detail on our \u201cJupyter on Kebnekaise\u201d documentation . Keypoints \u00b6 Keypoints How to run serial, MPI, OpenMP, and GPU jobs How to use GPU features How to run several jobs from inside one batch job","title":"Simple examples"},{"location":"simple/#simple__batch__script__examples","text":"Objectives See and try out different types of simple batch script examples. Try using constraints: how to allocate specific CPUs. Try using constraints: how to allocate specific GPUs. For consistency, I have given all the example batch scripts the suffix .sh even though it is not required. Another commonly used suffix is .batch , but any or none will work. You need to compile any programs mentioned in a batch script in order to run the examples, except for compile-run.sh and the CUDA examples, which includes compilation. Important The course project has the following project ID: hpc2n2024-084 In order to use it in a batch job, add this to the batch script: #SBATCH -A hpc2n2024-084 We have a storage project linked to the compute project: intro-hpc2n . You find it in /proj/nobackup/intro-hpc2n . Remember to create your own directory under it. Hint Try to change the C programs, add different programs, and in general play around with the examples! Note For these test examples I would suggest using the foss compiler toolchain, version 2022b, unless otherwise specified. If you decide to use a different one, you will have to make changes to some of the batch scripts. To submit a job script, do sbatch JOBSCRIPT In most of the examples, I name the executable when I compile. The flag -o tells the compiler you want to name the executable. If you don\u2019t include that and a name, you will get an executable named a.out . Of course, you do not have to name the executable hello . This is just an example. In general, I have named all the executables the same as the program (without the suffix).","title":"Simple batch script examples"},{"location":"simple/#serial__batch__job","text":"To compile a serial program, like hello.c with gcc do: gcc hello.c -o hello Sample batch script (hello.sh) #!/bin/bash # Project id - change to your own after the course! #SBATCH -A hpc2n2024-084 # Asking for 1 core #SBATCH -n 1 # Asking for a walltime of 1 min #SBATCH --time=00:01:00 # Purge modules before loading new ones in a script. ml purge > /dev/null 2 > & 1 ml foss/2022b ./hello Exercise: serial job Submit the job with sbatch . Check on it with squeue --me . Take a look at the output ( slurm-JOBID.out ) with nano or your favourite editor.","title":"Serial batch job"},{"location":"simple/#mpi__batch__job","text":"To compile an MPI program, like mpi_hello.c (and create an executable named mpi_hello ) with gcc, do: mpicc mpi_hello.c -o mpi_hello Sample batch script (mpi_hello.sh) #!/bin/bash # Remember to change this to your own Project ID after the course! #SBATCH -A hpc2n2024-084 # Number of tasks - default is 1 core per task #SBATCH -n 14 #SBATCH --time=00:05:00 # It is always a good idea to do ml purge before loading other modules ml purge > /dev/null 2 > & 1 ml add foss/2022b # Use srun since this is an MPI program srun ./mpi_hello Exercise: MPI job Submit the job with sbatch . Check on it with squeue --me . Take a look at the output ( slurm-JOBID.out ) with nano or your favourite editor. Try running it more than once to see that the order of the tasks are random.","title":"MPI batch job"},{"location":"simple/#openmp__batch__job","text":"To compile an OpenMP program, like omp_hello.c (and create an executable named omp_hello ) with gcc, do: gcc -fopenmp omp_hello.c -o omp_hello Sample batch script (omp_hello.sh) #!/bin/bash #SBATCH -A hpc2n2024-084 # Number of cores per task #SBATCH -c 28 #SBATCH --time=00:05:00 # It is always a good idea to do ml purge before loading other modules ml purge > /dev/null 2 > & 1 ml add foss/2022b # Set OMP_NUM_THREADS to the same value as -c with a fallback in case it isn't set. # SLURM_CPUS_PER_TASK is set to the value of -c, but only if -c is explicitly set if [ -n \" $SLURM_CPUS_PER_TASK \" ] ; then omp_threads = $SLURM_CPUS_PER_TASK else omp_threads = 1 fi export OMP_NUM_THREADS = $omp_threads ./omp_hello Exercise: OpenMP job Set OMP_NUM_THREADS to some value between 1 and 28 ( export OMP_NUM_THREADS=value ). Submit the job with sbatch . Take a look at the output ( slurm-JOBID.out ) with nano or your favourite editor. Change the value of OMP_NUM_THREADS ). Submit it again and check on the output to see the change.","title":"OpenMP batch job"},{"location":"simple/#multiple__serial__jobs__from__same__submit__file","text":"This submit file shows one way of running several programs from inside the same submit file. To run this example, you need to compile the following serial C programs: hello.c Greeting.c Adding2.c Mult2.c When the C programs have been compiled, submit the multiple-serial.sh program: multiple-serial.sh All jobs run at the same time, so you need as many cores as they need combined. You also need to ask for long enough time that even the longest of the jobs will finish. Note that here you submit with srun even if it is serial jobs. You use & to send the job to the background. Also note the wait at the end. If you do not add that, the whole batch job will finish when the first of the jobs inside ends. #!/bin/bash #SBATCH -A hpc2n2024-084 # Add enough cores that all jobs can run at the same time #SBATCH -n 5 # Make sure that the time is long enough that the longest job will have time to finish #SBATCH --time=00:05:00 module purge > /dev/null 2 > & 1 ml foss/2022b srun -n 1 --exclusive ./hello & srun -n 1 --exclusive ./Greeting & srun -n 1 --exclusive ./Adding2 10 20 & srun -n 1 --exclusive /bin/hostname & srun -n 1 --exclusive ./Mult2 10 2 wait Exercise: multiple serial jobs Compile the above mentioned programs. Submit the batch script with sbatch multiple-serial.sh If you run it several times you will notice that the order is random.","title":"Multiple serial jobs from same submit file"},{"location":"simple/#job__arrays","text":"Job arrays offer a mechanism for submitting and managing collections of similar jobs. All jobs must have the same initial options (e.g. size, time limit, etc.), however it is possible to change some of these options after the job has begun execution using the scontrol command specifying the JobID of the array or individual ArrayJobID. More information here on the official Slurm documentation pages . To try an example, we have included a small Python script hello-world-array.py and a batch script hello-world-array.sh . Both can also be found in the exercises/simple directory you have cloned. hello-world-array.py # import sys library (we need this for the command line args) import sys # print task number print ( 'Hello world! from task number: ' , sys.argv [ 1 ]) hello-world-array.sh #!/bin/bash # This is a very simple example of how to run a Python script with a job array #SBATCH -A hpc2n2024-084 # Change to your own after the course! #SBATCH --time=00:05:00 # Asking for 5 minutes #SBATCH --array=1-10 # how many tasks in the array #SBATCH -c 1 # Asking for 1 core # one core per task #SBATCH -o hello-world-%j-%a.out # Load any modules you need, here for Python 3.11.3 ml GCC/12.3.0 Python/3.11.3 # Run your Python script srun python hello-world-array.py $SLURM_ARRAY_TASK_ID Exercise: job arrays Submit the batch script. Look at the output files. Change the number of tasks in the array. Rerun. See the change.","title":"Job arrays"},{"location":"simple/#multiple__parallel__jobs__sequentially","text":"To run this example, you need to compile the following parallel C programs: mpi_hello.c mpi_greeting.c mpi_hi.c When the MPI C programs have been compiled, submit the multiple-parallel-sequential.sh program: #!/bin/bash #SBATCH -A hpc2n2024-084 # Since the files are run sequentially I only need enough cores for the largest of them to run #SBATCH -c 28 # Remember to ask for enough time for all jobs to complete #SBATCH --time=00:10:00 module purge > /dev/null 2 > & 1 ml foss/2022b # Here 14 tasks with 2 cores per task. Output to file - not needed if your job creates output in a file directly # In this example I also copy the output somewhere else and then run another executable. srun -n 14 -c 2 ./mpi_hello > myoutput1 2 > & 1 cp myoutput1 mydatadir srun -n 14 -c 2 ./mpi_greeting > myoutput2 2 > & 1 cp myoutput2 mydatadir srun -n 14 -c 2 ./mpi_hi > myoutput3 2 > & 1 cp myoutput3 mydatadir sbatch multiple-parallel-sequential.sh Exercise: multiple parallel jobs sequentially Submit the job: sbatch multiple-parallel-sequential.sh See that output data are thrown to files and copied to the directory mydatadir .","title":"Multiple parallel jobs sequentially"},{"location":"simple/#multiple__parallel__jobs__simultaneously","text":"To run this example, you need to compile the following parallel C programs: mpi_hello.c mpi_greeting.c mpi_hi.c As before, we recommend using the foss/2022b module for this. If you use a different one you need to change it in the multiple-parallel-simultaneous.sh batch script. When the MPI C programs have been compiled, submit the multiple-parallel-simultaneous.sh program: #!/bin/bash #SBATCH -A hpc2n2024-084 # Since the files run simultaneously I need enough cores for all of them to run #SBATCH -n 56 # Remember to ask for enough time for all jobs to complete #SBATCH --time=00:10:00 module purge > /dev/null 2 > & 1 ml foss/2022b srun -n 14 --exclusive ./mpi_hello & srun -n 14 --exclusive ./mpi_greeting & srun -n 14 --exclusive ./mpi_hi & wait Just like for the multiple serial jobs simultaneously example, you need to add wait to make sure the batch job will not finish when the first of the jobs in it finishes. Exercise: multiple parallel jobs simultaneously When you have compiled the needed programs, as mentioned above, submit with sbatch multiple-parallel-simultaneous.sh","title":"Multiple parallel jobs simultaneously"},{"location":"simple/#compiling__and__running__in__the__batch__job","text":"Sometimes you have a program that takes a long time to compile, or that you need to recompile before each run. To see a simple example of compiling and running from the batch job, look at the batch script compile-run.sh . In this case it compiles and runs the mpi_hello.c program. compile-run.sh #!/bin/bash # CHANGE THE PROJECT ID TO YOUR OWN PROJECT ID AFTER THE COURSE! #SBATCH -A hpc2n2024-084 #Name the job, for easier finding in the list #SBATCH -J compiler-run #SBATCH -t 00:10:00 #SBATCH -n 12 ml purge > /dev/null 2 > & 1 ml foss/2022b mpicc mpi_hello.c -o mpi_hello mpirun ./mpi_hello Exercise: compile and run in a batch job This batch script can be submitted directly, without compiling anything first, as that happens in the batch script. Try submitting it with sbatch and see what happens. Which files are created? You could try changing the program it compiles and runs to a different one. Remember to change the compiler if you are not using an MPI program.","title":"Compiling and running in the batch job"},{"location":"simple/#getting__errors__and__outputs__in__separate__files","text":"As a default, Slurm throws both errors and other output to the same file, named slurm-JOBID.out . If you want the errors and other output to separate files, you can do as in the example separate-err-out.sh : #!/bin/bash # Remember to change this to your own Project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH -n 8 #SBATCH --time=00:05:00 # Putting the output in a separate output file and the errors in an # error file instead of putting it all in slurm-JOBID.out # Note the environment variable %J, which contains the job ID. It is handy to # avoid naming the files the same for different runs, and thus overwriting them. #SBATCH --error=job.%J.err #SBATCH --output=job.%J.out ml purge > /dev/null 2 > & 1 ml foss/2022b mpirun ./mpi_hello You need the mpi_hello.c file compiled (and the executable named mpi_hello ) for this to run without changes. Of course, you can also just add your own programs. Exercise: errors and outputs in separate files Compile the file mpi_hello.s after loading the module foss/2022b . Submit the job script with sbatch . See that separate output and error files are created.","title":"Getting errors and outputs in separate files"},{"location":"simple/#cudagpu__programs","text":"To run programs/software that uses GPUs, you need to allocated GPUs in the job script. They will not be allocated by your program. To compile a cuda program, like hello-world.cu you need to load a toolchain containing CUDA compilers/load CUDA compilers. To run a piece of software that uses GPUs, you need to load a module version which is GPU aware. In many cases there are several versions of a module, only some of which are for running on GPUs. Important Remember to check the modules, versions, and prerequisites! Also make sure you check for the correct node type. Some of the GPUs are on Intel nodes (check modules on kebnekaise.hpc2n.umu.se ), some on AMD nodes (check modules on kebnekaise-amd.hpc2n.umu.se ).","title":"CUDA/GPU programs"},{"location":"simple/#v100__-__intel__skylake","text":"This example runs a small CUDA code. We recommend fosscuda/2020b (contains GCC , OpenMPI , OpenBLAS / LAPACK , FFTW , ScaLAPACK , and CUDA ) or intelcuda/2019a (contains icc , ifort , IntelMPI , IntelMKL , and CUDA ) Sample batch script gpu-skylake.sh #!/bin/bash # This job script is for running on 1 V100 GPU. # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=v100:1 ml purge > /dev/null 2 > & 1 ml fosscuda/2020b nvcc hello-world.cu -o hello ./hello The batch script gpu.sh compiles and runs a small cuda program called hello-world.cu . Exercise: V100 GPU job To submit it, just do: sbatch gpu.sh Use squeue --me or scontrol show job JOBID to see that the job runs in the correct partition/node types.","title":"V100 - Intel Skylake"},{"location":"simple/#a100__-__amd__zen3","text":"Remember, in order to find the correct modules, as well as compile a program if you need that, you must login to one of the AMD login nodes with either SSH ( kebnekaise-amd.hpc2n.umu.se ) or ThinLinc ( kebnekaise-amd-tl.kebnekaise.hpc2n.umu.se ). The job can be submitted from the regular login node, though. Exercise: login to the AMD login node and find a suitable module If you are logged in to the regular Kebnekaise login node, then you can easiest login to the AMD login node by typing this in a terminal window: ssh kebnekaise-amd.hpc2n.umu.se After that, you check for a suitable CUDA toolchain: ml spider CUDA . You can then load it (here CUDA/11.7.0 ) and use nvcc to compile the program hello-world.cu : ml CUDA/11.7.0 nvcc hello-world.cu -o hello Now logout from the AMD login node again. The batch script gpu-a100.sh compiles and runs a small cuda program called hello-world.cu . Sample A100 GPU job script: gpu-a100.sh #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=a100:1 ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 nvcc hello-world.cu -o hello ./hello Exercise: A100 GPU batch jobs The above script is found in the same directory as the other exercises ( intro-course/exercises/simple ). You can submit it directory: sbatch gpu-a100.sh Like for the A100, you are encouraged to use squeue --me and/or scontrol show job JOBID to see that the job gets the correct partition/node type allocated.","title":"A100 - AMD Zen3"},{"location":"simple/#a40__-__intel__broadwell","text":"Kebnekaise also has a few of the A40 GPUs. These are placed on Intel broadwell nodes. In order to run on these, you add this to your batch script: #SBATCH --gpus=a40:number where number is 1 or 2 (the number of GPU cards). You can find the available modules on the regular login node, kebnekaise.hpc2n.umu.se .","title":"A40 - Intel broadwell"},{"location":"simple/#l40s__-__amd__zen4","text":"Since these GPUs are located on AMD Zen4 nodes, you need to login to kebnekaise-amd.hpc2n.umu.se to check available modules. Then, to ask for these nodes in your batch script, you add: #SBATCH --gpus=l40s:number where number is 1 or 2 (the number of GPU cards).","title":"L40s - AMD Zen4"},{"location":"simple/#h100__-__amd__zen4","text":"The H100 GPUs are located on AMD Zen4 nodes. You can find the available modules by logging in to kebnekaise-amd.hpc2n.umu.se . You ask for these GPUs in your batch script by adding: #SBATCH --gpus=h100:number where number is 1, 2, 3, or 4 (the number of GPU cards you want to allocate).","title":"H100 - AMD Zen4"},{"location":"simple/#a6000__-__amd__zen4","text":"The A6000 GPUs are placed on AMD Zen4 nodes. That means you can find the available modules by logging in to kebnekaise-amd.hpc2n.umu.se . To run on these GPUs, add this to your batch script: #SBATCH --gpus=a6000:number where number is 1 or 2 (the number of GPU cards you want to allocated).","title":"A6000 - AMD Zen4"},{"location":"simple/#mi100__-__amd__zen3","text":"The MI100 GPUs are located on AMD Zen3 nodes. You can find the available modules by logging in to kebnekaise-amd.hpc2n.umu.se . To allocate MI100 GPUs, add this to your batch script: #SBATCH --gpus=mi100:number where number is 1 or 2 (the number of GPU cards).","title":"MI100 - AMD Zen3"},{"location":"simple/#gpu__features","text":"Sample batch script for allocating any AMD GPU #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=1 #SBATCH -C amd_gpu ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 ./myGPUcode Sample batch script for allocating any Nvidia GPU #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=1 #SBATCH -C nvidia_gpu ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 ./myGPUcode Sample batch script for allocating any Nvidia GPU on Intel node #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=1 #SBATCH -C 'nvidia_gpu&intel_cpu' ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 ./myGPUcode Sample batch script for allocating any GPU with AI features and on a Zen node #!/bin/bash # Remember to change this to your own project ID after the course! #SBATCH -A hpc2n2024-084 #SBATCH --time=00:05:00 #SBATCH --gpus=1 #SBATCH -C ''zen3|zen4'&GPU_AI' ml purge > /dev/null 2 > & 1 ml CUDA/11.7.0 ./myGPUcode Exercise: GPU features In order to run these examples, you can change ./myGPUcode to nvcc hello-world.cu -o hello ./hello or any other GPU program of your choice. The gpu-features.sh example script in the exercises/simple directory is prepared for the \u201cany GPU with AI features and on a Zen node\u201d. You can either run it as is, or make changes to it and try any of the other combinations here (or try new combinations yourself). Check with squeue --me which partition/node type the job ends up in, and that it fits. More information can be found with scontrol show job JOBID .","title":"GPU features"},{"location":"simple/#starting__jupyterlab","text":"On Kebnekaise, it is possible to run JupyterLab. This is done through a batch job, and is described in detail on our \u201cJupyter on Kebnekaise\u201d documentation .","title":"Starting JupyterLab"},{"location":"simple/#keypoints","text":"Keypoints How to run serial, MPI, OpenMP, and GPU jobs How to use GPU features How to run several jobs from inside one batch job","title":"Keypoints"},{"location":"software/","text":"Application examples \u00b6 Create a soft-link to your storage project It will be very convinient to create a soft-link to your storage project in your home directory for a faster navigation: cd $HOME ln -s /proj/nobackup/hpc2n202X-XYZ choose-a-name Monitoring the use of resources Most likely you will allocate many cores and many GPUs for your simulations. You can monitor the use of these resources with the job-usage job_ID command, where job_ID is the output number of the sbatch command. You can also see this number if you type squeue -u my-username . job-usage outputs a url that you can copy/paste in your local browser where you can see how resources are being used: Matlab \u00b6 How to find Matlab \u00b6 Matlab is available through the Menu bar if you are using ThinLinc client (recommended). Additionally, you can load a Matlab module on a Linux terminal on Kebnekaise. Details for these two options can be found here . First time configuration \u00b6 The first time you access Matlab on Kebnekaise, you need to configure it by following these guidelines Configuring Matlab . After configuring the cluster, it is a good practice to validate the cluster (HOME -> Parallel -> Create and Manage Clusters): Notice that it is recommended to use a small number of workers for the validation, in this case 4. Tools for efficient simulations \u00b6 Chart flow for a more efficient Matlab code using existing tools (adapted from 1 ) MATLAB on GPUs Notice that MATLAB currently supports only NVIDIA GPUs (v100,a40,a6000,a100,l40s,h100), with v100 and l40s being the most abundant (10 nodes each). Use MATLAB for lightweight tasks on the login nodes Remember that login nodes are used by many users and if you run heavy jobs there, you will interfere with the workflow of them. Exercises \u00b6 Exercise 1: Matlab serial job The folder SERIAL contains a function funct.m which performs a FFT on a matrix. The execution time is obtained with tic/toc and written down in the output file called log.out . Run the function by using the MATLAB GUI with the help of the script submit.m . As an alternative, you can submit the job via a batch script job.sh . Here, you will need to fix the Project_ID with the one provided for the present course and the Matlab version. Exercise 2: Matlab parallel job PARFOR folder contains an example of a parallelized loop with the \u201cparfor\u201d directive. A pause() function is included in the loop to make it heavy. This function can be submitted to the queue by running the script submit.m in the MATLAB GUI. The number of workers can be set by replacing the string FIXME (in the \u201csubmit.m\u201d file) with the number you desire. Try different values for the number of workers from 1 to 10 and take a note of the simulation time output at the end of the simulation. Where does the code achieve its peak performance? SPMD folder presents an example of a parallelized code using SPMD paradigm. Submit this job to the queue through the MATLAB GUI. This example illustrates the use of parpool to run parallel code in a more interactive manner. Exercise 3: Matlab GPU job GPU folder contains a test case that computes a Mandelbrot set both on CPU mandelcpu.m and on GPU mandelgpu.m . You can submit the jobs through the MATLAB GUI using the submitcpu.m and submitgpu.m files. The final output if everything ran well are two .png figures which display the timings for both architectures. Use the \u201ceom\u201d command on the terminal to visualize the images (eom out-X.png) R \u00b6 How to find R \u00b6 Similar to Matlab, R is available through the Menu bar if you are using ThinLinc client (recommended). Additionally, you can load a Matlab module on a Linux terminal on Kebnekaise. Details for these two options can be found here . First time configuration \u00b6 The first time you access R on Kebnekaise, you need to configure it by following the Preparations step. Recommendations \u00b6 Be aware of data duplication in R Some parallel functions mcapply in this example, tend to replicate the data for the workers (cores) if the dataframe is modified by them. This can be crucial if you are working with a large data frame and you are employing several parallel functions, for instance during the training of machine learning models because your simulation could easily exceed the available memory per node. library ( parallel ) library ( pryr ) prev <- mem_used () print ( paste ( \"Memory initially allocated by R:\" , prev/1e6, \"MB\" )) # Define a relatively large dataframe data_df <- data.frame ( ID = seq ( 1 , 1e7 ) , Value = runif ( 1e7 ) ) # Create a function to be applied to each row (or a subset of rows) process_function <- function ( i, df ) { # do some modification the i-th row return ( df $Value [ i ] * 2 ) } prev <- mem_used () - prev print ( paste ( \"Memory after the serial code execution:\" , prev/1e6, \"MB\" )) # Use mclapply to process the dataframe in parallel num_cores <- 4 results <- mclapply ( 1 :nrow ( data_df ) , function ( i ) process_function ( i, data_df ) , mc.cores = num_cores ) prev <- mem_used () - prev print ( paste ( \"Memory after parallel code execution:\" , prev/1e6, \"MB\" )) In this example mem-dup.R , I used the function mem_used() provided by the pryr package to monitor the memory usage. The batch script for this example is job.sh . One possible solution for data duplication could be to use use a data frame for each worker that includes only the relevant data for that particular computation. Use R for lightweight tasks on the login nodes Remember that login nodes are used by many users and if you run heavy jobs there, you will interfere with the workflow of them. Exercises \u00b6 Requirements Prior to running the examples, you will need to install several packages. Follow these instructions : The packages needed are: For this R version (check if they are not already installed) ml GCC/10.2.0 OpenMPI/4.0.5 R/4.0.4 Rmpi doParallel caret MASS klaR nnet e1071 rpart mlbench parallel Exercise 1: R serial job In the SERIAL folder, a serial is provided. Submit the script job.sh with the command R CMD and also with Rscript . Where could it be more suitable to use Rscript over R CMD ? Why do we need the flag #SBATCH -C \u2018skylake\u2019 in the batch script? Exercise 2: Job Arrays JOB-ARRAYS folder shows an example for job arrays, the batch file is job.sh . Submit the script and notice what is written in the output files. Could you use job arrays in your simulations if you need to run many simulations where some parameters are changed? As an example, imagine that you need to run 28 simulations where a single parameter, such as the temperature, is changed from 2 to 56 C. Could you use the variable task_id in the previous script to get that range of temperatures so that each simulation prints out a different temperature? Exercise 3: Parallel jobs with Rmpi In the folder RMPI , you can find the R script Rmpi.R which uses 5 MPI slaves to apply the runif() function on an array \u201cc\u201d. The submit file is job_Rmpi.sh . As a result, you will see the random numbers generated by the slaves in the slurm output file Exercise 4: Parallel jobs with doParallel The folder DOPARALLEL contains two examples: doParallel.R shows how to use the foreach function in sequential mode (1 core) and the parallel mode using 4 cores. What is the difference in the usage of foreach for these two modes? Submit the job_doParallel.sh script and compare the timings of the sequential and parallel codes. How many workers are allocated for this simulation? If you want to allocate more or less, what changes must be made to these files? doParallel_ML.R presents the evaluation of several ML models in both sequential and parallel modes using the standard \u201ciris\u201d database. The difference is basically in the use of %dopar% instead of %do% function. Submit the batch script job_doParallel_ML.sh to the queue. In the output file observe the resulting elapsed times for the sequential and the 4 cores parallel simulation. Upon submitting the job to the queue you will get a number called job ID. Use the command: job-usage job_ID to obtain a URL which you can copy/paste in your local browser. Tip: refresh your browser several times to get the statistics. Can you see how the CPU is used? What about the memory? Note 1: In order to run this exercise, you need to have all the packages listed at the beginning of this document installed. Note 2: If you want to try a different number of cores for running the scripts, you should change that number in both the .R and .sh scripts Exercise 5: Machine Learning jobs In the folder ML we show a ML model using a sonar database and Random Forest as the training method ( Rscript.R ). The simulations are done both in serial and parallel modes. You may change the values for the number of cores (1 in the present case) to other values. Notice that the number of cores needs to be the same in the files job.sh and Rscript.R . Try a different number of cores and monitor the timings which are reported at the end of the output file. Alphafold \u00b6 How to find Alphafold \u00b6 Alphafold is installed as a module. Notice that on the Intel nodes there are more versions of Alphafold installed than on the AMD nodes. Thus, if you are targeting one version that is only installed on the Intel nodes, you will need to add the instruction #SBATCH -C skylake to your batch script, otherwise the job could arrive to an AMD node that lacks that installation. Exercises \u00b6 Exercise 1: Running a monomer protein simulation In the folder ALPHAFOLD you will find a fasta secuence for a monomer and the corresponding batch file job.sh for running the simulation on GPUs. Try running the simulation with CPUs only and then with l40s, v100 and a100 GPUs. Notice that the simulation will take ~1hrs. so the purpose of this exercise is to know if the simulation starts running well only. CryoSPARC \u00b6 How to find CryoSPARC \u00b6 The version 4.5.3 of CryoSPARC is installed as a module. First time configuration \u00b6 One needs a license for using this software. For academic purposes a free of charge license can be requested at the website cryosparc.com (one working day for the processing). Once you obtain your license ID copy it, create a file called /home/u/username/.cryosparc-license and paste it in the first line of this file. In the second line of the file write your email address. Using CryoSPARC on Kebnekaise \u00b6 Create a suitable folder in your project directory, for instance /proj/nobackup/hpc2n202X-XYZ/cryosparc and move into this folder. Download/copy the lane*tar files that are located here to the cryosparc folder and untar them here ( tar -xvf lane_CPU.tar as an example). Fix your Project_ID and time Change the string Project_ID in the file lane*/cluster_script.sh to reflect your current project. Also, the time was set to 20 min. in these files but for your realistic simulations you can change it to longer times ( -t 00:20:00 ). The lanes should be recognized by CryoSPARC when it starts running. Load the CryoSPARC modules. Start CryoSPARC and accept the request which asks about continuing using cryostart and that the folder was not used before. List the users on the server (which should be only yourself for this type of license), check the email address that is displayed for this user (it should be the one you added in the license file) and reset the password to. These steps are summarized here: $cryosparc start ... Do you wish to continue starting cryosparc? [ yN ] : y ... CryoSPARC master started. From this machine, access CryoSPARC and CryoSPARC Live at http://localhost:39007 ... $cryosparc listusers cryosparc resetpassword --email \"myemail@mail.com\" --password \"choose-a-password\" Copy and paste the line which has the localhost port (notice that port number can change) to a browser on Kebnekaise: After loging in, you will be able to see the CryoSPARC\u2019s dashboard: There are several tutorials at the CryoSPARC website, in the previous picture I followed the Introductory Tutorial (v4.0+) . Use cryosparc instead of cryosparcm On Kebnekaise the command cryosparc should be used and not the one cited in the tutorial cryosparcm Depending on the job type, CryoSPARC would suggest the hardware resources. For instance, in the tutorial above Step 4: Import Movies suggests using 1 CPU upon queueing it, but Step 5: Motion Correction suggests using 1 GPU. For CPU-only jobs you can choose the CPU lane, and if your job uses GPUs you can choose among L40s, V100, A100, and H100. Notice that the V100 and L40s are the most abundant at the moment: Keypoints The software on Kebnekaise is mostly accessed through the module system. References \u00b6 MathWorks documentation on Parallel Computing \u21a9","title":"Application examples"},{"location":"software/#application__examples","text":"Create a soft-link to your storage project It will be very convinient to create a soft-link to your storage project in your home directory for a faster navigation: cd $HOME ln -s /proj/nobackup/hpc2n202X-XYZ choose-a-name Monitoring the use of resources Most likely you will allocate many cores and many GPUs for your simulations. You can monitor the use of these resources with the job-usage job_ID command, where job_ID is the output number of the sbatch command. You can also see this number if you type squeue -u my-username . job-usage outputs a url that you can copy/paste in your local browser where you can see how resources are being used:","title":"Application examples"},{"location":"software/#matlab","text":"","title":"Matlab"},{"location":"software/#how__to__find__matlab","text":"Matlab is available through the Menu bar if you are using ThinLinc client (recommended). Additionally, you can load a Matlab module on a Linux terminal on Kebnekaise. Details for these two options can be found here .","title":"How to find Matlab"},{"location":"software/#first__time__configuration","text":"The first time you access Matlab on Kebnekaise, you need to configure it by following these guidelines Configuring Matlab . After configuring the cluster, it is a good practice to validate the cluster (HOME -> Parallel -> Create and Manage Clusters): Notice that it is recommended to use a small number of workers for the validation, in this case 4.","title":"First time configuration"},{"location":"software/#tools__for__efficient__simulations","text":"Chart flow for a more efficient Matlab code using existing tools (adapted from 1 ) MATLAB on GPUs Notice that MATLAB currently supports only NVIDIA GPUs (v100,a40,a6000,a100,l40s,h100), with v100 and l40s being the most abundant (10 nodes each). Use MATLAB for lightweight tasks on the login nodes Remember that login nodes are used by many users and if you run heavy jobs there, you will interfere with the workflow of them.","title":"Tools for efficient simulations"},{"location":"software/#exercises","text":"Exercise 1: Matlab serial job The folder SERIAL contains a function funct.m which performs a FFT on a matrix. The execution time is obtained with tic/toc and written down in the output file called log.out . Run the function by using the MATLAB GUI with the help of the script submit.m . As an alternative, you can submit the job via a batch script job.sh . Here, you will need to fix the Project_ID with the one provided for the present course and the Matlab version. Exercise 2: Matlab parallel job PARFOR folder contains an example of a parallelized loop with the \u201cparfor\u201d directive. A pause() function is included in the loop to make it heavy. This function can be submitted to the queue by running the script submit.m in the MATLAB GUI. The number of workers can be set by replacing the string FIXME (in the \u201csubmit.m\u201d file) with the number you desire. Try different values for the number of workers from 1 to 10 and take a note of the simulation time output at the end of the simulation. Where does the code achieve its peak performance? SPMD folder presents an example of a parallelized code using SPMD paradigm. Submit this job to the queue through the MATLAB GUI. This example illustrates the use of parpool to run parallel code in a more interactive manner. Exercise 3: Matlab GPU job GPU folder contains a test case that computes a Mandelbrot set both on CPU mandelcpu.m and on GPU mandelgpu.m . You can submit the jobs through the MATLAB GUI using the submitcpu.m and submitgpu.m files. The final output if everything ran well are two .png figures which display the timings for both architectures. Use the \u201ceom\u201d command on the terminal to visualize the images (eom out-X.png)","title":"Exercises"},{"location":"software/#r","text":"","title":"R"},{"location":"software/#how__to__find__r","text":"Similar to Matlab, R is available through the Menu bar if you are using ThinLinc client (recommended). Additionally, you can load a Matlab module on a Linux terminal on Kebnekaise. Details for these two options can be found here .","title":"How to find R"},{"location":"software/#first__time__configuration_1","text":"The first time you access R on Kebnekaise, you need to configure it by following the Preparations step.","title":"First time configuration"},{"location":"software/#recommendations","text":"Be aware of data duplication in R Some parallel functions mcapply in this example, tend to replicate the data for the workers (cores) if the dataframe is modified by them. This can be crucial if you are working with a large data frame and you are employing several parallel functions, for instance during the training of machine learning models because your simulation could easily exceed the available memory per node. library ( parallel ) library ( pryr ) prev <- mem_used () print ( paste ( \"Memory initially allocated by R:\" , prev/1e6, \"MB\" )) # Define a relatively large dataframe data_df <- data.frame ( ID = seq ( 1 , 1e7 ) , Value = runif ( 1e7 ) ) # Create a function to be applied to each row (or a subset of rows) process_function <- function ( i, df ) { # do some modification the i-th row return ( df $Value [ i ] * 2 ) } prev <- mem_used () - prev print ( paste ( \"Memory after the serial code execution:\" , prev/1e6, \"MB\" )) # Use mclapply to process the dataframe in parallel num_cores <- 4 results <- mclapply ( 1 :nrow ( data_df ) , function ( i ) process_function ( i, data_df ) , mc.cores = num_cores ) prev <- mem_used () - prev print ( paste ( \"Memory after parallel code execution:\" , prev/1e6, \"MB\" )) In this example mem-dup.R , I used the function mem_used() provided by the pryr package to monitor the memory usage. The batch script for this example is job.sh . One possible solution for data duplication could be to use use a data frame for each worker that includes only the relevant data for that particular computation. Use R for lightweight tasks on the login nodes Remember that login nodes are used by many users and if you run heavy jobs there, you will interfere with the workflow of them.","title":"Recommendations"},{"location":"software/#exercises_1","text":"Requirements Prior to running the examples, you will need to install several packages. Follow these instructions : The packages needed are: For this R version (check if they are not already installed) ml GCC/10.2.0 OpenMPI/4.0.5 R/4.0.4 Rmpi doParallel caret MASS klaR nnet e1071 rpart mlbench parallel Exercise 1: R serial job In the SERIAL folder, a serial is provided. Submit the script job.sh with the command R CMD and also with Rscript . Where could it be more suitable to use Rscript over R CMD ? Why do we need the flag #SBATCH -C \u2018skylake\u2019 in the batch script? Exercise 2: Job Arrays JOB-ARRAYS folder shows an example for job arrays, the batch file is job.sh . Submit the script and notice what is written in the output files. Could you use job arrays in your simulations if you need to run many simulations where some parameters are changed? As an example, imagine that you need to run 28 simulations where a single parameter, such as the temperature, is changed from 2 to 56 C. Could you use the variable task_id in the previous script to get that range of temperatures so that each simulation prints out a different temperature? Exercise 3: Parallel jobs with Rmpi In the folder RMPI , you can find the R script Rmpi.R which uses 5 MPI slaves to apply the runif() function on an array \u201cc\u201d. The submit file is job_Rmpi.sh . As a result, you will see the random numbers generated by the slaves in the slurm output file Exercise 4: Parallel jobs with doParallel The folder DOPARALLEL contains two examples: doParallel.R shows how to use the foreach function in sequential mode (1 core) and the parallel mode using 4 cores. What is the difference in the usage of foreach for these two modes? Submit the job_doParallel.sh script and compare the timings of the sequential and parallel codes. How many workers are allocated for this simulation? If you want to allocate more or less, what changes must be made to these files? doParallel_ML.R presents the evaluation of several ML models in both sequential and parallel modes using the standard \u201ciris\u201d database. The difference is basically in the use of %dopar% instead of %do% function. Submit the batch script job_doParallel_ML.sh to the queue. In the output file observe the resulting elapsed times for the sequential and the 4 cores parallel simulation. Upon submitting the job to the queue you will get a number called job ID. Use the command: job-usage job_ID to obtain a URL which you can copy/paste in your local browser. Tip: refresh your browser several times to get the statistics. Can you see how the CPU is used? What about the memory? Note 1: In order to run this exercise, you need to have all the packages listed at the beginning of this document installed. Note 2: If you want to try a different number of cores for running the scripts, you should change that number in both the .R and .sh scripts Exercise 5: Machine Learning jobs In the folder ML we show a ML model using a sonar database and Random Forest as the training method ( Rscript.R ). The simulations are done both in serial and parallel modes. You may change the values for the number of cores (1 in the present case) to other values. Notice that the number of cores needs to be the same in the files job.sh and Rscript.R . Try a different number of cores and monitor the timings which are reported at the end of the output file.","title":"Exercises"},{"location":"software/#alphafold","text":"","title":"Alphafold"},{"location":"software/#how__to__find__alphafold","text":"Alphafold is installed as a module. Notice that on the Intel nodes there are more versions of Alphafold installed than on the AMD nodes. Thus, if you are targeting one version that is only installed on the Intel nodes, you will need to add the instruction #SBATCH -C skylake to your batch script, otherwise the job could arrive to an AMD node that lacks that installation.","title":"How to find Alphafold"},{"location":"software/#exercises_2","text":"Exercise 1: Running a monomer protein simulation In the folder ALPHAFOLD you will find a fasta secuence for a monomer and the corresponding batch file job.sh for running the simulation on GPUs. Try running the simulation with CPUs only and then with l40s, v100 and a100 GPUs. Notice that the simulation will take ~1hrs. so the purpose of this exercise is to know if the simulation starts running well only.","title":"Exercises"},{"location":"software/#cryosparc","text":"","title":"CryoSPARC"},{"location":"software/#how__to__find__cryosparc","text":"The version 4.5.3 of CryoSPARC is installed as a module.","title":"How to find CryoSPARC"},{"location":"software/#first__time__configuration_2","text":"One needs a license for using this software. For academic purposes a free of charge license can be requested at the website cryosparc.com (one working day for the processing). Once you obtain your license ID copy it, create a file called /home/u/username/.cryosparc-license and paste it in the first line of this file. In the second line of the file write your email address.","title":"First time configuration"},{"location":"software/#using__cryosparc__on__kebnekaise","text":"Create a suitable folder in your project directory, for instance /proj/nobackup/hpc2n202X-XYZ/cryosparc and move into this folder. Download/copy the lane*tar files that are located here to the cryosparc folder and untar them here ( tar -xvf lane_CPU.tar as an example). Fix your Project_ID and time Change the string Project_ID in the file lane*/cluster_script.sh to reflect your current project. Also, the time was set to 20 min. in these files but for your realistic simulations you can change it to longer times ( -t 00:20:00 ). The lanes should be recognized by CryoSPARC when it starts running. Load the CryoSPARC modules. Start CryoSPARC and accept the request which asks about continuing using cryostart and that the folder was not used before. List the users on the server (which should be only yourself for this type of license), check the email address that is displayed for this user (it should be the one you added in the license file) and reset the password to. These steps are summarized here: $cryosparc start ... Do you wish to continue starting cryosparc? [ yN ] : y ... CryoSPARC master started. From this machine, access CryoSPARC and CryoSPARC Live at http://localhost:39007 ... $cryosparc listusers cryosparc resetpassword --email \"myemail@mail.com\" --password \"choose-a-password\" Copy and paste the line which has the localhost port (notice that port number can change) to a browser on Kebnekaise: After loging in, you will be able to see the CryoSPARC\u2019s dashboard: There are several tutorials at the CryoSPARC website, in the previous picture I followed the Introductory Tutorial (v4.0+) . Use cryosparc instead of cryosparcm On Kebnekaise the command cryosparc should be used and not the one cited in the tutorial cryosparcm Depending on the job type, CryoSPARC would suggest the hardware resources. For instance, in the tutorial above Step 4: Import Movies suggests using 1 CPU upon queueing it, but Step 5: Motion Correction suggests using 1 GPU. For CPU-only jobs you can choose the CPU lane, and if your job uses GPUs you can choose among L40s, V100, A100, and H100. Notice that the V100 and L40s are the most abundant at the moment: Keypoints The software on Kebnekaise is mostly accessed through the module system.","title":"Using CryoSPARC on Kebnekaise"},{"location":"software/#references","text":"MathWorks documentation on Parallel Computing \u21a9","title":"References"}]} \ No newline at end of file diff --git a/search/worker.js b/search/worker.js new file mode 100644 index 00000000..8628dbce --- /dev/null +++ b/search/worker.js @@ -0,0 +1,133 @@ +var base_path = 'function' === typeof importScripts ? '.' : '/search/'; +var allowSearch = false; +var index; +var documents = {}; +var lang = ['en']; +var data; + +function getScript(script, callback) { + console.log('Loading script: ' + script); + $.getScript(base_path + script).done(function () { + callback(); + }).fail(function (jqxhr, settings, exception) { + console.log('Error: ' + exception); + }); +} + +function getScriptsInOrder(scripts, callback) { + if (scripts.length === 0) { + callback(); + return; + } + getScript(scripts[0], function() { + getScriptsInOrder(scripts.slice(1), callback); + }); +} + +function loadScripts(urls, callback) { + if( 'function' === typeof importScripts ) { + importScripts.apply(null, urls); + callback(); + } else { + getScriptsInOrder(urls, callback); + } +} + +function onJSONLoaded () { + data = JSON.parse(this.responseText); + var scriptsToLoad = ['lunr.js']; + if (data.config && data.config.lang && data.config.lang.length) { + lang = data.config.lang; + } + if (lang.length > 1 || lang[0] !== "en") { + scriptsToLoad.push('lunr.stemmer.support.js'); + if (lang.length > 1) { + scriptsToLoad.push('lunr.multi.js'); + } + if (lang.includes("ja") || lang.includes("jp")) { + scriptsToLoad.push('tinyseg.js'); + } + for (var i=0; i < lang.length; i++) { + if (lang[i] != 'en') { + scriptsToLoad.push(['lunr', lang[i], 'js'].join('.')); + } + } + } + loadScripts(scriptsToLoad, onScriptsLoaded); +} + +function onScriptsLoaded () { + console.log('All search scripts loaded, building Lunr index...'); + if (data.config && data.config.separator && data.config.separator.length) { + lunr.tokenizer.separator = new RegExp(data.config.separator); + } + + if (data.index) { + index = lunr.Index.load(data.index); + data.docs.forEach(function (doc) { + documents[doc.location] = doc; + }); + console.log('Lunr pre-built index loaded, search ready'); + } else { + index = lunr(function () { + if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) { + this.use(lunr[lang[0]]); + } else if (lang.length > 1) { + this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility + } + this.field('title'); + this.field('text'); + this.ref('location'); + + for (var i=0; i < data.docs.length; i++) { + var doc = data.docs[i]; + this.add(doc); + documents[doc.location] = doc; + } + }); + console.log('Lunr index built, search ready'); + } + allowSearch = true; + postMessage({config: data.config}); + postMessage({allowSearch: allowSearch}); +} + +function init () { + var oReq = new XMLHttpRequest(); + oReq.addEventListener("load", onJSONLoaded); + var index_path = base_path + '/search_index.json'; + if( 'function' === typeof importScripts ){ + index_path = 'search_index.json'; + } + oReq.open("GET", index_path); + oReq.send(); +} + +function search (query) { + if (!allowSearch) { + console.error('Assets for search still loading'); + return; + } + + var resultDocuments = []; + var results = index.search(query); + for (var i=0; i < results.length; i++){ + var result = results[i]; + doc = documents[result.ref]; + doc.summary = doc.text.substring(0, 200); + resultDocuments.push(doc); + } + return resultDocuments; +} + +if( 'function' === typeof importScripts ) { + onmessage = function (e) { + if (e.data.init) { + init(); + } else if (e.data.query) { + postMessage({ results: search(e.data.query) }); + } else { + console.error("Worker - Unrecognized message: " + e); + } + }; +} diff --git a/simple/index.html b/simple/index.html new file mode 100644 index 00000000..16f55cd7 --- /dev/null +++ b/simple/index.html @@ -0,0 +1,678 @@ + + + + + + + + Simple examples - Introduction to Kebnekaise + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
    +
    • +
  • Simple examples
    • +
  • +
    • +
+
+
+
+
+ +

Simple batch script examples

+
+

Objectives

+
    +
  • See and try out different types of simple batch script examples.
    • +
  • Try using constraints: how to allocate specific CPUs.
    • +
  • Try using constraints: how to allocate specific GPUs.
    • +
+
+

For consistency, I have given all the example batch scripts the suffix .sh even though it is not required. Another commonly used suffix is .batch, but any or none will work.

+

You need to compile any programs mentioned in a batch script in order to run the examples, except for compile-run.sh and the CUDA examples, which includes compilation.

+
+

Important

+
    +
  • The course project has the following project ID: hpc2n2024-084
    • +
  • In order to use it in a batch job, add this to the batch script: #SBATCH -A hpc2n2024-084
    • +
  • We have a storage project linked to the compute project: intro-hpc2n.
      +
    • You find it in /proj/nobackup/intro-hpc2n.
      • +
    • Remember to create your own directory under it.
      • +
    +
    • +
+
+
+

Hint

+

Try to change the C programs, add different programs, and in general play around with the examples!

+
+
+

Note

+
    +
  1. For these test examples I would suggest using the foss compiler toolchain, version 2022b, unless otherwise specified. If you decide to use a different one, you will have to make changes to some of the batch scripts.
    2. +
  2. To submit a job script, do sbatch JOBSCRIPT
    3. +
  3. In most of the examples, I name the executable when I compile. The flag -o tells the compiler you want to name the executable. If you don’t include that and a name, you will get an executable named a.out. Of course, you do not have to name the executable hello. This is just an example. In general, I have named all the executables the same as the program (without the suffix).
    4. +
+
+

Serial batch job

+

To compile a serial program, like hello.c with gcc do:

+
gcc hello.c -o hello
+
+

Sample batch script (hello.sh)

+
#!/bin/bash
+# Project id - change to your own after the course!
+#SBATCH -A hpc2n2024-084
+# Asking for 1 core
+#SBATCH -n 1
+# Asking for a walltime of 1 min
+#SBATCH --time=00:01:00
+
+# Purge modules before loading new ones in a script.
+ml purge  > /dev/null 2>&1
+ml foss/2022b
+
+./hello
+
+
+

Exercise: serial job

+

Submit the job with sbatch. Check on it with squeue --me. Take a look at the output (slurm-JOBID.out) with nano or your favourite editor.

+
+

MPI batch job

+

To compile an MPI program, like mpi_hello.c (and create an executable named mpi_hello) with gcc, do:

+
mpicc mpi_hello.c -o mpi_hello
+
+

Sample batch script (mpi_hello.sh)

+
#!/bin/bash
+# Remember to change this to your own Project ID after the course! 
+#SBATCH -A hpc2n2024-084
+# Number of tasks - default is 1 core per task 
+#SBATCH -n 14
+#SBATCH --time=00:05:00
+
+# It is always a good idea to do ml purge before loading other modules 
+ml purge > /dev/null 2>&1
+
+ml add foss/2022b
+
+# Use srun since this is an MPI program 
+srun ./mpi_hello
+
+
+

Exercise: MPI job

+

Submit the job with sbatch. Check on it with squeue --me. Take a look at the output (slurm-JOBID.out) with nano or your favourite editor. Try running it more than once to see that the order of the tasks are random.

+
+

OpenMP batch job

+

To compile an OpenMP program, like omp_hello.c (and create an executable named omp_hello) with gcc, do:

+
gcc -fopenmp omp_hello.c -o omp_hello
+
+

Sample batch script (omp_hello.sh)

+
#!/bin/bash
+#SBATCH -A hpc2n2024-084 
+# Number of cores per task 
+#SBATCH -c 28
+#SBATCH --time=00:05:00
+
+# It is always a good idea to do ml purge before loading other modules 
+ml purge > /dev/null 2>&1
+
+ml add foss/2022b
+
+# Set OMP_NUM_THREADS to the same value as -c with a fallback in case it isn't set.
+# SLURM_CPUS_PER_TASK is set to the value of -c, but only if -c is explicitly set
+if [ -n "$SLURM_CPUS_PER_TASK" ]; then
+  omp_threads=$SLURM_CPUS_PER_TASK
+else
+  omp_threads=1
+fi
+export OMP_NUM_THREADS=$omp_threads
+
+./omp_hello
+
+
+

Exercise: OpenMP job

+

Set OMP_NUM_THREADS to some value between 1 and 28 (export OMP_NUM_THREADS=value). Submit the job with sbatch. Take a look at the output (slurm-JOBID.out) with nano or your favourite editor. Change the value of OMP_NUM_THREADS). Submit it again and check on the output to see the change.

+
+

Multiple serial jobs from same submit file

+

This submit file shows one way of running several programs from inside the same submit file.

+

To run this example, you need to compile the following serial C programs:

+
hello.c
+Greeting.c
+Adding2.c
+Mult2.c
+
+

When the C programs have been compiled, submit the multiple-serial.sh program:

+
+multiple-serial.sh +

All jobs run at the same time, so you need as many cores as they need combined. You also need to ask for long enough time that even the longest of the jobs will finish.

+

Note that here you submit with srun even if it is serial jobs. You use & to send the job to the background. Also note the wait at the end. If you do not add that, the whole batch job will finish when the first of the jobs inside ends.

+
#!/bin/bash
+#SBATCH -A hpc2n2024-084
+# Add enough cores that all jobs can run at the same time 
+#SBATCH -n 5
+# Make sure that the time is long enough that the longest job will have time to finish 
+#SBATCH --time=00:05:00
+
+module purge > /dev/null 2>&1
+ml foss/2022b 
+
+srun -n 1 --exclusive ./hello &
+srun -n 1 --exclusive ./Greeting & 
+srun -n 1 --exclusive ./Adding2 10 20 &
+srun -n 1 --exclusive /bin/hostname & 
+srun -n 1 --exclusive ./Mult2 10 2 
+wait
+
+
+
+

Exercise: multiple serial jobs

+

Compile the above mentioned programs. Submit the batch script with

+
sbatch multiple-serial.sh
+
+

If you run it several times you will notice that the order is random.

+
+

Job arrays

+

Job arrays offer a mechanism for submitting and managing collections of similar jobs. All jobs must have the same initial options (e.g. size, time limit, etc.), however it is possible to change some of these options after the job has begun execution using the scontrol command specifying the JobID of the array or individual ArrayJobID.

+

More information here on the official Slurm documentation pages.

+

To try an example, we have included a small Python script hello-world-array.py and a batch script hello-world-array.sh. Both can also be found in the exercises/simple directory you have cloned.

+
+

hello-world-array.py

+
# import sys library (we need this for the command line args)
+import sys
+
+# print task number
+print('Hello world! from task number: ', sys.argv[1])
+
+
+
+

hello-world-array.sh

+
#!/bin/bash
+# This is a very simple example of how to run a Python script with a job array
+#SBATCH -A hpc2n2024-084 # Change to your own after the course!
+#SBATCH --time=00:05:00 # Asking for 5 minutes
+#SBATCH --array=1-10   # how many tasks in the array
+#SBATCH -c 1 # Asking for 1 core    # one core per task
+#SBATCH -o hello-world-%j-%a.out
+
+# Load any modules you need, here for Python 3.11.3
+ml GCC/12.3.0 Python/3.11.3
+
+# Run your Python script
+srun python hello-world-array.py $SLURM_ARRAY_TASK_ID
+
+
+
+

Exercise: job arrays

+

Submit the batch script. Look at the output files. Change the number of tasks in the array. Rerun. See the change.

+
+

Multiple parallel jobs sequentially

+

To run this example, you need to compile the following parallel C programs:

+
mpi_hello.c
+mpi_greeting.c
+mpi_hi.c
+
+

When the MPI C programs have been compiled, submit the multiple-parallel-sequential.sh program:

+
#!/bin/bash
+#SBATCH -A hpc2n2024-084 
+# Since the files are run sequentially I only need enough cores for the largest of them to run 
+#SBATCH -c 28
+# Remember to ask for enough time for all jobs to complete
+#SBATCH --time=00:10:00
+
+module purge > /dev/null 2>&1 
+ml foss/2022b
+
+# Here 14 tasks with 2 cores per task. Output to file - not needed if your job creates output in a file directly 
+# In this example I also copy the output somewhere else and then run another executable.
+
+srun -n 14 -c 2 ./mpi_hello > myoutput1 2>&1
+cp myoutput1 mydatadir
+srun -n 14 -c 2 ./mpi_greeting > myoutput2 2>&1
+cp myoutput2 mydatadir
+srun -n 14 -c 2 ./mpi_hi > myoutput3 2>&1
+cp myoutput3 mydatadir
+
+
sbatch multiple-parallel-sequential.sh
+
+
+

Exercise: multiple parallel jobs sequentially

+

Submit the job:

+
sbatch multiple-parallel-sequential.sh
+
+

See that output data are thrown to files and copied to the directory mydatadir.

+
+

Multiple parallel jobs simultaneously

+

To run this example, you need to compile the following parallel C programs:

+
mpi_hello.c
+mpi_greeting.c
+mpi_hi.c
+
+

As before, we recommend using the foss/2022b module for this. If you use a different one you need to change it in the multiple-parallel-simultaneous.sh batch script.

+

When the MPI C programs have been compiled, submit the multiple-parallel-simultaneous.sh program:

+
#!/bin/bash
+#SBATCH -A hpc2n2024-084
+# Since the files run simultaneously I need enough cores for all of them to run 
+#SBATCH -n 56
+# Remember to ask for enough time for all jobs to complete
+#SBATCH --time=00:10:00
+
+module purge > /dev/null 2>&1 
+ml foss/2022b
+
+srun -n 14 --exclusive ./mpi_hello &
+srun -n 14 --exclusive ./mpi_greeting &
+srun -n 14 --exclusive ./mpi_hi &
+wait
+
+

Just like for the multiple serial jobs simultaneously example, you need to add wait to make sure the batch job will not finish when the first of the jobs in it finishes.

+
+

Exercise: multiple parallel jobs simultaneously

+

When you have compiled the needed programs, as mentioned above, submit with

+
sbatch multiple-parallel-simultaneous.sh
+
+
+

Compiling and running in the batch job

+

Sometimes you have a program that takes a long time to compile, or that you need to recompile before each run. To see a simple example of compiling and running from the batch job, look at the batch script compile-run.sh.

+

In this case it compiles and runs the mpi_hello.c program.

+
+

compile-run.sh

+
#!/bin/bash
+# CHANGE THE PROJECT ID TO YOUR OWN PROJECT ID AFTER THE COURSE!
+#SBATCH -A hpc2n2024-084
+#Name the job, for easier finding in the list
+#SBATCH -J compiler-run
+#SBATCH -t 00:10:00
+#SBATCH -n 12
+
+ml purge > /dev/null 2>&1
+
+ml foss/2022b 
+
+mpicc mpi_hello.c -o mpi_hello 
+mpirun ./mpi_hello
+
+
+
+

Exercise: compile and run in a batch job

+

This batch script can be submitted directly, without compiling anything first, as that happens in the batch script. Try submitting it with sbatch and see what happens. Which files are created? You could try changing the program it compiles and runs to a different one. Remember to change the compiler if you are not using an MPI program.

+
+

Getting errors and outputs in separate files

+

As a default, Slurm throws both errors and other output to the same file, named slurm-JOBID.out. If you want the errors and other output to separate files, you can do as in the example separate-err-out.sh:

+
#!/bin/bash 
+# Remember to change this to your own Project ID after the course! 
+#SBATCH -A hpc2n2024-084
+#SBATCH -n 8 
+#SBATCH --time=00:05:00
+
+# Putting the output in a separate output file and the errors in an 
+# error file instead of putting it all in slurm-JOBID.out 
+# Note the environment variable %J, which contains the job ID. It is handy to 
+# avoid naming the files the same for different runs, and thus overwriting them.  
+#SBATCH --error=job.%J.err
+#SBATCH --output=job.%J.out
+
+ml purge > /dev/null 2>&1
+ml foss/2022b
+
+mpirun ./mpi_hello 
+
+

You need the mpi_hello.c file compiled (and the executable named mpi_hello) for this to run without changes. Of course, you can also just add your own programs.

+
+

Exercise: errors and outputs in separate files

+

Compile the file mpi_hello.s after loading the module foss/2022b. Submit the job script with sbatch. See that separate output and error files are created.

+
+

CUDA/GPU programs

+

To run programs/software that uses GPUs, you need to allocated GPUs in the job script. They will not be allocated by your program.

+

To compile a cuda program, like hello-world.cu you need to load a toolchain containing CUDA compilers/load CUDA compilers.

+

To run a piece of software that uses GPUs, you need to load a module version which is GPU aware. In many cases there are several versions of a module, only some of which are for running on GPUs.

+
+

Important

+

Remember to check the modules, versions, and prerequisites! Also make sure you check for the correct node type. Some of the GPUs are on Intel nodes (check modules on kebnekaise.hpc2n.umu.se), some on AMD nodes (check modules on kebnekaise-amd.hpc2n.umu.se).

+
+

V100 - Intel Skylake

+

This example runs a small CUDA code.

+

We recommend fosscuda/2020b (contains GCC, OpenMPI, OpenBLAS/LAPACK, FFTW, ScaLAPACK, and CUDA) or intelcuda/2019a (contains icc, ifort, IntelMPI, IntelMKL, and CUDA)

+

Sample batch script gpu-skylake.sh

+
#!/bin/bash 
+# This job script is for running on 1 V100 GPU. 
+# Remember to change this to your own project ID after the course! 
+#SBATCH -A hpc2n2024-084 
+#SBATCH --time=00:05:00
+#SBATCH --gpus=v100:1
+
+ml purge > /dev/null 2>&1
+ml fosscuda/2020b
+
+nvcc hello-world.cu -o hello
+./hello
+
+

The batch script gpu.sh compiles and runs a small cuda program called hello-world.cu.

+
+

Exercise: V100 GPU job

+

To submit it, just do:

+
sbatch gpu.sh
+
+

Use squeue --me or scontrol show job JOBID to see that the job runs in the correct partition/node types.

+
+

A100 - AMD Zen3

+

Remember, in order to find the correct modules, as well as compile a program if you need that, you must login to one of the AMD login nodes with either SSH (kebnekaise-amd.hpc2n.umu.se) or ThinLinc (kebnekaise-amd-tl.kebnekaise.hpc2n.umu.se). +The job can be submitted from the regular login node, though.

+
+

Exercise: login to the AMD login node and find a suitable module

+

If you are logged in to the regular Kebnekaise login node, then you can easiest login to the AMD login node by typing this in a terminal window:

+
ssh kebnekaise-amd.hpc2n.umu.se
+
+

After that, you check for a suitable CUDA toolchain: ml spider CUDA.

+

You can then load it (here CUDA/11.7.0) and use nvcc to compile the program hello-world.cu:

+
ml CUDA/11.7.0
+nvcc hello-world.cu -o hello
+
+

Now logout from the AMD login node again.

+
+

The batch script gpu-a100.sh compiles and runs a small cuda program called hello-world.cu.

+

Sample A100 GPU job script: gpu-a100.sh

+
#!/bin/bash 
+# Remember to change this to your own project ID after the course! 
+#SBATCH -A hpc2n2024-084
+#SBATCH --time=00:05:00
+#SBATCH --gpus=a100:1
+
+ml purge > /dev/null 2>&1
+ml CUDA/11.7.0
+
+nvcc hello-world.cu -o hello
+./hello
+
+
+

Exercise: A100 GPU batch jobs

+

The above script is found in the same directory as the other exercises (intro-course/exercises/simple). You can submit it directory:

+
sbatch gpu-a100.sh
+
+

Like for the A100, you are encouraged to use squeue --me and/or scontrol show job JOBID to see that the job gets the correct partition/node type allocated.

+
+

A40 - Intel broadwell

+

Kebnekaise also has a few of the A40 GPUs. These are placed on Intel broadwell nodes.

+

In order to run on these, you add this to your batch script:

+
#SBATCH --gpus=a40:number
+
+

where number is 1 or 2 (the number of GPU cards).

+

You can find the available modules on the regular login node, kebnekaise.hpc2n.umu.se.

+

L40s - AMD Zen4

+

Since these GPUs are located on AMD Zen4 nodes, you need to login to kebnekaise-amd.hpc2n.umu.se to check available modules.

+

Then, to ask for these nodes in your batch script, you add:

+
#SBATCH --gpus=l40s:number
+
+

where number is 1 or 2 (the number of GPU cards).

+

H100 - AMD Zen4

+

The H100 GPUs are located on AMD Zen4 nodes. You can find the available modules by logging in to kebnekaise-amd.hpc2n.umu.se.

+

You ask for these GPUs in your batch script by adding:

+
#SBATCH --gpus=h100:number
+
+

where number is 1, 2, 3, or 4 (the number of GPU cards you want to allocate).

+

A6000 - AMD Zen4

+

The A6000 GPUs are placed on AMD Zen4 nodes. That means you can find the available modules by logging in to kebnekaise-amd.hpc2n.umu.se.

+

To run on these GPUs, add this to your batch script:

+
#SBATCH --gpus=a6000:number 
+
+

where number is 1 or 2 (the number of GPU cards you want to allocated).

+

MI100 - AMD Zen3

+

The MI100 GPUs are located on AMD Zen3 nodes. You can find the available modules by logging in to kebnekaise-amd.hpc2n.umu.se.

+

To allocate MI100 GPUs, add this to your batch script:

+
#SBATCH --gpus=mi100:number
+
+

where number is 1 or 2 (the number of GPU cards).

+

GPU features

+

Sample batch script for allocating any AMD GPU

+
#!/bin/bash 
+# Remember to change this to your own project ID after the course! 
+#SBATCH -A hpc2n2024-084 
+#SBATCH --time=00:05:00
+#SBATCH --gpus=1
+#SBATCH -C amd_gpu 
+
+ml purge > /dev/null 2>&1
+ml CUDA/11.7.0
+
+./myGPUcode
+
+

Sample batch script for allocating any Nvidia GPU

+
#!/bin/bash
+# Remember to change this to your own project ID after the course! 
+#SBATCH -A hpc2n2024-084 
+#SBATCH --time=00:05:00
+#SBATCH --gpus=1
+#SBATCH -C nvidia_gpu 
+
+ml purge > /dev/null 2>&1
+ml CUDA/11.7.0
+
+./myGPUcode
+
+

Sample batch script for allocating any Nvidia GPU on Intel node

+
#!/bin/bash
+# Remember to change this to your own project ID after the course!
+#SBATCH -A hpc2n2024-084
+#SBATCH --time=00:05:00
+#SBATCH --gpus=1
+#SBATCH -C 'nvidia_gpu&intel_cpu'
+
+ml purge > /dev/null 2>&1
+ml CUDA/11.7.0
+
+./myGPUcode
+
+

Sample batch script for allocating any GPU with AI features and on a Zen node

+
#!/bin/bash
+# Remember to change this to your own project ID after the course!
+#SBATCH -A hpc2n2024-084
+#SBATCH --time=00:05:00
+#SBATCH --gpus=1
+#SBATCH -C ''zen3|zen4'&GPU_AI'
+
+ml purge > /dev/null 2>&1
+ml CUDA/11.7.0
+
+./myGPUcode
+
+
+

Exercise: GPU features

+

In order to run these examples, you can change ./myGPUcode to

+
nvcc hello-world.cu -o hello
+./hello
+
+

or any other GPU program of your choice.

+

The gpu-features.sh example script in the exercises/simple directory is prepared for the “any GPU with AI features and on a Zen node”. You can either run it as is, or make changes to it and try any of the other combinations here (or try new combinations yourself).

+

Check with squeue --me which partition/node type the job ends up in, and that it fits. More information can be found with scontrol show job JOBID.

+
+

Starting JupyterLab

+

On Kebnekaise, it is possible to run JupyterLab. This is done through a batch job, and is described in detail on our “Jupyter on Kebnekaise” documentation.

+

Keypoints

+
+

Keypoints

+
    +
  • How to run serial, MPI, OpenMP, and GPU jobs
    • +
  • How to use GPU features
    • +
  • How to run several jobs from inside one batch job
    • +
+
+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + Next » + + +
+ + + + + + + + + + + diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 00000000..af5c321e --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,43 @@ + + + + https://hpc2n.github.io/intro-course/ + 2024-09-02 + + + https://hpc2n.github.io/intro-course/batch/ + 2024-09-02 + + + https://hpc2n.github.io/intro-course/compilers/ + 2024-09-02 + + + https://hpc2n.github.io/intro-course/filesystem/ + 2024-09-02 + + + https://hpc2n.github.io/intro-course/intro/ + 2024-09-02 + + + https://hpc2n.github.io/intro-course/login/ + 2024-09-02 + + + https://hpc2n.github.io/intro-course/modules/ + 2024-09-02 + + + https://hpc2n.github.io/intro-course/projectsaccounts/ + 2024-09-02 + + + https://hpc2n.github.io/intro-course/simple/ + 2024-09-02 + + + https://hpc2n.github.io/intro-course/software/ + 2024-09-02 + + \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz new file mode 100644 index 00000000..97d828bf Binary files /dev/null and b/sitemap.xml.gz differ diff --git a/software/index.html b/software/index.html new file mode 100644 index 00000000..ee171fbf --- /dev/null +++ b/software/index.html @@ -0,0 +1,507 @@ + + + + + + + + Application examples - Introduction to Kebnekaise + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
    +
    • +
  • Application examples
    • +
  • +
    • +
+
+
+
+
+ +

Application examples

+
+Create a soft-link to your storage project +

It will be very convinient to create a soft-link to your storage project in your +home directory for a faster navigation:

+
     cd $HOME
+     ln -s /proj/nobackup/hpc2n202X-XYZ choose-a-name
+
+
+
+Monitoring the use of resources +

Most likely you will allocate many cores and many GPUs for your simulations. You can +monitor the use of these resources with the job-usage job_ID command, where job_ID +is the output number of the sbatch command. You can also see this number if you type +squeue -u my-username. job-usage outputs a url that you can copy/paste in your +local browser where you can see how resources are being used:

+

job-usage

+
+

Matlab

+

How to find Matlab

+

Matlab is available through the Menu bar if you are using ThinLinc client (recommended). Additionally, you can load +a Matlab module on a Linux terminal on Kebnekaise. Details for these two options can be found +here.

+

First time configuration

+

The first time you access Matlab on Kebnekaise, you need to configure it by following these guidelines +Configuring Matlab. After configuring the cluster, it is a good practice to validate the +cluster (HOME -> Parallel -> Create and Manage Clusters):

+

clustervalidation

+

Notice that it is recommended to use a small number of workers for the validation, in this case 4.

+

Tools for efficient simulations

+

Chart flow for a more efficient Matlab code using existing tools (adapted from1)

+

pctworkflow

+
+

MATLAB on GPUs

+

Notice that MATLAB currently supports only NVIDIA GPUs (v100,a40,a6000,a100,l40s,h100), +with v100 and l40s being the most abundant (10 nodes each).

+
+
+

Use MATLAB for lightweight tasks on the login nodes

+

Remember that login nodes are used by many users and if you run heavy jobs there, +you will interfere with the workflow of them.

+
+

Exercises

+
+Exercise 1: Matlab serial job +

The folder SERIAL contains a function funct.m +which performs a FFT on a matrix. +The execution time is obtained with tic/toc and written down in the output file called +log.out. Run the function by using the MATLAB GUI with the help of the script submit.m.

+

As an alternative, you can submit the job via a batch script +job.sh. +Here, you will need to fix the Project_ID with the one provided for the present course and the Matlab version.

+
+
+Exercise 2: Matlab parallel job +
    +
  • +

    PARFOR folder contains an example of a parallelized loop with the “parfor” directive. A pause() +function is included in the loop to make it heavy. This function can be +submitted to the queue by running the script submit.m in the MATLAB GUI. +The number of workers can be set by replacing the string FIXME (in the “submit.m” +file) with the number you desire. + Try different values for the number of workers from 1 to 10 and take a note + of the simulation time output at the end of the simulation. Where does the + code achieve its peak performance?

    +
    • +
  • +

    SPMD folder presents an example of a parallelized code using SPMD paradigm. Submit this job to the queue through the MATLAB GUI. This +example illustrates the use of parpool to run parallel code in a more interactive manner.

    +
    • +
+
+
+Exercise 3: Matlab GPU job +

GPU folder contains a test case that computes a Mandelbrot set both +on CPU mandelcpu.m +and on GPU mandelgpu.m. You can submit the jobs through +the MATLAB GUI using the submitcpu.m and submitgpu.m files.

+

The final output if everything ran well are two .png figures +which display the timings for both architectures. Use the “eom” command on the +terminal to visualize the images (eom out-X.png)

+
+

R

+

How to find R

+

Similar to Matlab, R is available through the Menu bar if you are using ThinLinc client (recommended). Additionally, you can load +a Matlab module on a Linux terminal on Kebnekaise. Details for these two options can be found +here.

+

First time configuration

+

The first time you access R on Kebnekaise, you need to configure it by following the +Preparations step.

+

Recommendations

+
+Be aware of data duplication in R +

Some parallel functions mcapply in this example, tend to replicate the data for +the workers (cores) if the dataframe is modified by them. This can be crucial if you +are working with a large data frame and you are employing several parallel functions, +for instance during the training of machine learning models because your simulation could +easily exceed the available memory per node.

+
   library(parallel)
+   library(pryr)
+
+   prev <- mem_used()                                                         
+   print(paste("Memory initially allocated by R:", prev/1e6, "MB"))
+
+   # Define a relatively large dataframe
+   data_df <- data.frame(
+   ID = seq(1, 1e7),
+   Value = runif(1e7)
+   )
+
+   # Create a function to be applied to each row (or a subset of rows)
+   process_function <- function(i, df) {
+   # do some modification the i-th row 
+   return(df$Value[i] * 2)
+   }
+   prev <- mem_used() - prev
+   print(paste("Memory after the serial code execution:", prev/1e6, "MB"))
+
+   # Use mclapply to process the dataframe in parallel
+   num_cores <- 4
+   results <- mclapply(1:nrow(data_df), function(i) process_function(i, data_df), mc.cores = num_cores)
+   prev <- mem_used() - prev
+   print(paste("Memory after parallel code execution:", prev/1e6, "MB"))
+
+

In this example mem-dup.R, I used the function mem_used() provided by the pryr package +to monitor the memory usage. The batch script for this example is job.sh.

+

One possible solution for data duplication could be to use use a data frame for each worker that includes +only the relevant data for that particular computation.

+
+
+

Use R for lightweight tasks on the login nodes

+

Remember that login nodes are used by many users and if you run heavy jobs there, +you will interfere with the workflow of them.

+
+

Exercises

+
+Requirements +

Prior to running the examples, you will need to install several packages. +Follow these instructions:

+
    +
  • +

    The packages needed are:

    +

    For this R version (check if they are not already installed)

    +

    ml GCC/10.2.0 OpenMPI/4.0.5 R/4.0.4

    +

    Rmpi

    +

    doParallel

    +

    caret

    +

    MASS

    +

    klaR

    +

    nnet

    +

    e1071

    +

    rpart

    +

    mlbench

    +

    parallel

    +
    • +
+
+
+Exercise 1: R serial job +

In the SERIAL folder, a serial is provided. Submit the script +job.sh with the command R CMD and also with Rscript. Where could +it be more suitable to use Rscript over R CMD?

+

Why do we need the flag #SBATCH -C ‘skylake’ in the batch script?

+
+
+Exercise 2: Job Arrays +

JOB-ARRAYS folder shows an example for job arrays, the batch file is job.sh. Submit the +script and notice what is written in the output files.

+

Could you use job arrays in your simulations if you need to run many simulations where some parameters are changed? As an example, imagine that you need to run 28 simulations +where a single parameter, such as the temperature, is changed from 2 to 56 C. Could you +use the variable task_id in the previous script to get that range of temperatures so +that each simulation prints out a different temperature?

+
+
+Exercise 3: Parallel jobs with Rmpi +

In the folder RMPI, you can find the R script Rmpi.R which uses 5 +MPI slaves to apply the runif() function on an array “c”. The submit file is +job_Rmpi.sh. As a result, you will see the random numbers +generated by the slaves in the slurm output file

+
+
+Exercise 4: Parallel jobs with doParallel +

The folder DOPARALLEL contains two examples:

+
    +
  1. +

    doParallel.R + shows how to use the foreach function in sequential mode + (1 core) and the parallel mode using 4 cores. What is the difference in the usage + of foreach for these two modes?

    +

    Submit the job_doParallel.sh script and compare the timings of the + sequential and parallel codes.

    +

    How many workers are allocated for this simulation? If you want to allocate + more or less, what changes must be made to these files?

    +
    2. +
  2. +

    doParallel_ML.R presents the evaluation of several ML models in both + sequential and parallel modes using the standard “iris” database. The + difference is basically in the use of %dopar% instead of %do% function.

    +

    Submit the batch script job_doParallel_ML.sh to the queue.

    +

    In the output file observe the resulting elapsed times for the sequential + and the 4 cores parallel simulation.

    +

    Upon submitting the job to the queue you will get a number called job ID. + Use the command:

    +

    job-usage job_ID

    +

    to obtain a URL which you can copy/paste in your local browser. Tip: refresh + your browser several times to get the statistics.

    +

    Can you see how the CPU is used? What about the memory?

    +

    Note 1: In order to run this exercise, you need to have all the packages + listed at the beginning of this document installed.

    +

    Note 2: If you want to try a different number of cores for running the + scripts, you should change that number in both the .R and .sh scripts

    +
    3. +
+
+
+Exercise 5: Machine Learning jobs +

In the folder ML we show a ML model using a sonar database +and Random Forest as the training method (Rscript.R). The simulations are done both in serial +and parallel modes. You may change the values for the number of cores (1 in the present case) +to other values. Notice that the number of cores needs to be the same in the +files job.sh and Rscript.R.

+

Try a different number of cores and monitor the timings which are reported at +the end of the output file.

+
+

Alphafold

+

How to find Alphafold

+

Alphafold is installed as a module. Notice that on the Intel nodes there are more +versions of Alphafold installed than on the AMD nodes. Thus, if you are targeting one +version that is only installed on the Intel nodes, you will need to add the instruction +#SBATCH -C skylake to your batch script, otherwise the job could arrive to an +AMD node that lacks that installation.

+

Exercises

+
+Exercise 1: Running a monomer protein simulation +

In the folder ALPHAFOLD you will find a fasta secuence for a monomer and the +corresponding batch file job.sh for running the simulation on +GPUs. Try running the simulation with CPUs only and then with l40s, v100 and a100 GPUs.

+

Notice that the simulation will take ~1hrs. so the purpose of this exercise is to know +if the simulation starts running well only.

+
+

CryoSPARC

+

How to find CryoSPARC

+

The version 4.5.3 of CryoSPARC is installed as a module.

+

First time configuration

+

One needs a license for using this software. For +academic purposes a free of charge license can be requested at the website +cryosparc.com (one working day for the processing). +Once you obtain your license ID copy it, create a file called /home/u/username/.cryosparc-license and paste +it in the first line of this file. In the second line of the file write your email address.

+

Using CryoSPARC on Kebnekaise

+

Create a suitable folder in your project directory, for instance /proj/nobackup/hpc2n202X-XYZ/cryosparc +and move into this folder. Download/copy the lane*tar files that are located here to the cryosparc folder and untar them here (tar -xvf lane_CPU.tar as an example).

+
+Fix your Project_ID and time +

Change the string Project_ID in the file lane*/cluster_script.sh to reflect your current project. +Also, the time was set to 20 min. in these files but for your realistic simulations you can change it to +longer times (-t 00:20:00).

+
+

The lanes should be recognized by CryoSPARC when it starts running.

+

Load the CryoSPARC modules. Start CryoSPARC and accept the request which asks about continuing using +cryostart and that the folder was not used before. List the users on the server (which should be only yourself +for this type of license), check the email address that is displayed for this user (it should be the one you +added in the license file) and reset the password to. These steps are summarized here:

+
$cryosparc start 
+...
+Do you wish to continue starting cryosparc? [yN]: y
+...
+CryoSPARC master started. 
+ From this machine, access CryoSPARC and CryoSPARC Live at
+    http://localhost:39007
+...
+
+$cryosparc listusers
+cryosparc resetpassword --email "myemail@mail.com" --password "choose-a-password"
+
+

Copy and paste the line which has the localhost port (notice that port number can change) to a browser on Kebnekaise:

+

cryosparc-ini

+

After loging in, you will be able to see the CryoSPARC’s dashboard:

+

cryosparc-dash

+

There are several tutorials at the CryoSPARC website, in the previous picture I followed the +Introductory Tutorial (v4.0+).

+
+

Use cryosparc instead of cryosparcm

+

On Kebnekaise the command cryosparc should be used and not the one cited in the tutorial cryosparcm

+
+

Depending on the job type, CryoSPARC would suggest the hardware resources. For instance, in the tutorial +above Step 4: Import Movies suggests using 1 CPU upon queueing it, but Step 5: Motion Correction suggests +using 1 GPU. For CPU-only jobs you can choose the CPU lane, and if your job uses GPUs you can choose +among L40s, V100, A100, and H100. Notice that the V100 and L40s are the most abundant at the moment:

+

cryosparc-dash

+
+

Keypoints

+
    +
  • The software on Kebnekaise is mostly accessed through the module system.
    • +
+
+

References

+
+
+
    +
  1. +

    MathWorks documentation on Parallel Computing ↩

    +
    2. +
+
+ +
+
+ +
+
+ +
+ +
+ +
+ + + + « Previous + + + +
+ + + + + + + + + + +