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Memory.html
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<!DOCTYPE html>
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<title>Memory Management</title>
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<p style="margin-top: 6px; margin-left: -2px;">Feb., 2021</p>
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</slide>
<slide class=""><hgroup><h2>Profiling Memory: Rprof</h2></hgroup><article id="profiling-memory-rprof">
<p>Rprof has tools for profiling memory:</p>
<pre class = 'prettyprint lang-r'>siml <- function(l) {
c <- rep(0,l); hits <- 0 #variables initialization
listp <- as.list(seq(10000000))
pow2 <- function(x) { x2 <- sqrt( x[1]*x[1]+x[2]*x[2] ); return(x2) }
for(i in 1:l){
x = runif(2,-1,1)
if( pow2(x) <=1 ){ hits <- hits + 1 }
dens <- hits/i; pi_partial = dens*4; c[i] = pi_partial
}; return(c)
}</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Rprof</h2></hgroup><article id="profiling-memory-rprof-1">
<pre class = 'prettyprint lang-r'>size <- 1000000
Rprof("Rprof-mem.out", memory.profiling=TRUE)
res <- siml(size)
Rprof(NULL)</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Rprof</h2></hgroup><article id="profiling-memory-rprof-2">
<pre class = 'prettyprint lang-r'>summaryRprof("Rprof-mem.out", memory="both")</pre>
<pre >## $by.self
## self.time self.pct total.time total.pct mem.total
## "runif" 2.24 41.18 2.24 41.18 1703.2
## "as.list.default" 1.46 26.84 1.46 26.84 680.0
## "siml" 1.10 20.22 5.44 100.00 3917.4
## "pow2" 0.60 11.03 0.60 11.03 576.3
## "findCenvVar" 0.02 0.37 0.04 0.74 2.4
## "%in%" 0.02 0.37 0.02 0.37 2.4
##
## $by.total
## total.time total.pct mem.total self.time self.pct
## "siml" 5.44 100.00 3917.4 1.10 20.22
## "block_exec" 5.44 100.00 3917.4 0.00 0.00
## "call_block" 5.44 100.00 3917.4 0.00 0.00
## "eval" 5.44 100.00 3917.4 0.00 0.00
## "evaluate" 5.44 100.00 3917.4 0.00 0.00
## "evaluate::evaluate" 5.44 100.00 3917.4 0.00 0.00
## "evaluate_call" 5.44 100.00 3917.4 0.00 0.00
## "FUN" 5.44 100.00 3917.4 0.00 0.00
## "generator$render" 5.44 100.00 3917.4 0.00 0.00
## "handle" 5.44 100.00 3917.4 0.00 0.00
## "in_dir" 5.44 100.00 3917.4 0.00 0.00
## "knitr::knit" 5.44 100.00 3917.4 0.00 0.00
## "lapply" 5.44 100.00 3917.4 0.00 0.00
## "process_file" 5.44 100.00 3917.4 0.00 0.00
## "process_group" 5.44 100.00 3917.4 0.00 0.00
## "process_group.block" 5.44 100.00 3917.4 0.00 0.00
## "render" 5.44 100.00 3917.4 0.00 0.00
## "render_one" 5.44 100.00 3917.4 0.00 0.00
## "rmarkdown::render" 5.44 100.00 3917.4 0.00 0.00
## "rmarkdown::render_site" 5.44 100.00 3917.4 0.00 0.00
## "sapply" 5.44 100.00 3917.4 0.00 0.00
## "suppressMessages" 5.44 100.00 3917.4 0.00 0.00
## "timing_fn" 5.44 100.00 3917.4 0.00 0.00
## "withCallingHandlers" 5.44 100.00 3917.4 0.00 0.00
## "withVisible" 5.44 100.00 3917.4 0.00 0.00
## "runif" 2.24 41.18 1703.2 2.24 41.18
## "as.list.default" 1.46 26.84 680.0 1.46 26.84
## "as.list" 1.46 26.84 680.0 0.00 0.00
## "pow2" 0.60 11.03 576.3 0.60 11.03
## "findCenvVar" 0.04 0.74 2.4 0.02 0.37
## "cmp" 0.04 0.74 2.4 0.00 0.00
## "cmpCall" 0.04 0.74 2.4 0.00 0.00
## "cmpfun" 0.04 0.74 2.4 0.00 0.00
## "compiler:::tryCmpfun" 0.04 0.74 2.4 0.00 0.00
## "doTryCatch" 0.04 0.74 2.4 0.00 0.00
## "genCode" 0.04 0.74 2.4 0.00 0.00
## "getInlineInfo" 0.04 0.74 2.4 0.00 0.00
## "h" 0.04 0.74 2.4 0.00 0.00
## "tryCatch" 0.04 0.74 2.4 0.00 0.00
## "tryCatchList" 0.04 0.74 2.4 0.00 0.00
## "tryCatchOne" 0.04 0.74 2.4 0.00 0.00
## "tryInline" 0.04 0.74 2.4 0.00 0.00
## "%in%" 0.02 0.37 2.4 0.02 0.37
## "cmpForBody" 0.02 0.37 2.4 0.00 0.00
## "cmpPrim1" 0.02 0.37 0.0 0.00 0.00
## "cmpPrim2" 0.02 0.37 0.0 0.00 0.00
## "cmpSymbolAssign" 0.02 0.37 0.0 0.00 0.00
## "constantFold" 0.02 0.37 2.4 0.00 0.00
## "constantFoldCall" 0.02 0.37 2.4 0.00 0.00
## "getFoldFun" 0.02 0.37 2.4 0.00 0.00
## "isBaseVar" 0.02 0.37 2.4 0.00 0.00
##
## $sample.interval
## [1] 0.02
##
## $sampling.time
## [1] 5.44</pre>
<p>notice that the memory usage reported is the accumulated memory.</p>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: gc</h2></hgroup><article id="profiling-memory-gc">
<p>A better approach would be by using <strong>gc()</strong> function. <strong>gc()</strong> reports the memory usage at some specific point. Information for parts of the code can be reported with the flags <strong>gcinfo()</strong>:</p>
<pre class = 'prettyprint lang-r'>size <- 1000000
gc()
# used (Mb) gc trigger (Mb) max used (Mb)
#Ncells 551156 29.5 1222600 65.3 1067006 57.0
#Vcells 1413536 10.8 8388608 64.0 1745827 13.4
gcinfo(TRUE) #checking the memory usage during function execution
res <- siml(size)
#... ommited lines
#Garbage collection 59 = 49+3+7 (level 0) ...
#563.6 Mbytes of cons cells used (79%)
#171.1 Mbytes of vectors used (76%)
gcinfo(FALSE)</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: gc</h2></hgroup><article id="profiling-memory-gc-1">
<p>A better approach would be by using <strong>gc()</strong> function. <strong>gc()</strong> reports the memory usage at some specific point. Information for parts of the code can be reported with the flags <strong>gcinfo()</strong>:</p>
<p>Finally, a call to <strong>gc()</strong> will report the memory allocated for the outputs of the function <strong>res <- siml()</strong>:</p>
<pre class = 'prettyprint lang-r'>gc()
# used (Mb) gc trigger (Mb) max used (Mb)
#Ncells 558900 29.9 10696839 571.3 11155454 595.8
#Vcells 2429818 18.6 23726023 181.1 29657289 226.3</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: gc</h2></hgroup><article id="profiling-memory-gc-2">
<p>gc() can help us to find memory usage changes upon creating objects:</p>
<pre class = 'prettyprint lang-r'>gc(reset=TRUE)
# used (Mb) gc trigger (Mb) max used (Mb)
#Ncells 562188 30.1 1154511 61.7 562188 30.1
#Vcells 1425756 10.9 8388608 64.0 1425756 10.9
listp <- as.list(seq(10000000))
gc()
# used (Mb) gc trigger (Mb) max used (Mb)
#Ncells 10564701 564.3 22068058 1178.6 10567700 564.4
#Vcells 21431560 163.6 33209716 253.4 21441849 163.6</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: gc</h2></hgroup><article id="profiling-memory-gc-3">
<pre class = 'prettyprint lang-r'>rm(listp)
gc()
# used (Mb) gc trigger (Mb) max used (Mb)
#Ncells 564859 30.2 17654447 942.9 10567700 564.4
#Vcells 1431905 11.0 26567773 202.7 21441849 163.6
gc(reset=TRUE)
# used (Mb) gc trigger (Mb) max used (Mb)
#Ncells 564869 30.2 14123558 754.3 564869 30.2
#Vcells 1431935 11.0 21254219 162.2 1431935 11.0 </pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Pryr</h2></hgroup><article id="profiling-memory-pryr">
<p>Another way to monitor the memory size of the objects is with the <em>Pryr</em> package which uses the function <strong>object_size()</strong> for this purpose.</p>
<pre class = 'prettyprint lang-r'>library(pryr)</pre>
<p>R allocates memory in a heuristic manner. To see this, let us monitor how an object request for memory as it grows with the <strong>object_size()</strong> function:</p>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Pryr</h2></hgroup><article id="profiling-memory-pryr-1">
<pre class = 'prettyprint lang-r'>sizes <- sapply(0:50, function(n) object_size(seq_len(n)))
plot(0:50, sizes, xlab = "Length", ylab = "Size (bytes)",
type = "s")</pre>
<p><img src="Memory_files/figure-html/unnamed-chunk-9-1.png" width="576" style="display: block; margin: auto;" /></p>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Pryr</h2></hgroup><article id="profiling-memory-pryr-2">
<p>another feature in R is that it tries to save memory by using pointers to existing memory allocations:</p>
<pre class = 'prettyprint lang-r'>x <- 1:1e6
object_size(x)</pre>
<pre >## 4 MB</pre>
<pre class = 'prettyprint lang-r'>y <- list(x, x, x)
object_size(y)</pre>
<pre >## 4 MB</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Pryr</h2></hgroup><article id="profiling-memory-pryr-3">
<pre class = 'prettyprint lang-r'>object_size(x, y)</pre>
<pre >## 4 MB</pre>
<p>after modifying one element of the list we get a different value:</p>
<pre class = 'prettyprint lang-r'>y[[1]] <- as.integer(x+1-1)
object_size(y)</pre>
<pre >## 8 MB</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Pryr</h2></hgroup><article id="profiling-memory-pryr-4">
<p>The <em>mem_change()</em> function helps you to figure out the change in size upon creating an object:</p>
<pre class = 'prettyprint lang-r'>myf <- function() {
mem_change( A <- matrix(1.0, 5000, 5000) )
10
}
mem_change( z <- myf() )
# 1 kB
mem_change( A <- matrix(1.0, 5000, 5000) )
# 200 MB</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Lineprof</h2></hgroup><article id="profiling-memory-lineprof">
<p>Lineprof package can be installed with:</p>
<pre class = 'prettyprint lang-r'>install.packages("devtools")
devtools::install_github("hadley/lineprof")</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Lineprof</h2></hgroup><article id="profiling-memory-lineprof-1">
<pre class = 'prettyprint lang-r'>library(lineprof)
siml <- function(l) {
c <- rep(0,l);
hits <- 0
listp <- as.list(seq(10000000))
pow2 <- function(x) { x2 <- sqrt( x[1]*x[1]+x[2]*x[2] ); return(x2) }
for(i in 1:l){
x = runif(2,-1,1)
if( pow2(x) <=1 ){ hits <- hits + 1 }
dens <- hits/i; pi_partial = dens*4; c[i] = pi_partial
}
return(c)
}</pre>
</article></slide><slide class=""><hgroup><h2>Profiling Memory: Lineprof</h2></hgroup><article id="profiling-memory-lineprof-2">
<pre class = 'prettyprint lang-r'>prof <- lineprof(siml(1000000))
prof
# time alloc release dups ref
#1 0.009 5.624 0.000 1808 c("compiler:::tryCmpfun", "tryCatch")
#2 0.002 0.100 0.000 235 character(0)
#3 0.520 556.091 5.220 2 c("as.list", "as.list.default")
#4 0.001 5.332 0.000 0 "runif"
#5 0.001 5.253 0.000 0 character(0)
#6 0.001 5.197 0.000 0 #6
#7 0.009 17.406 16.858 0 "runif"
#8 0.001 8.230 0.000 0 character(0)
#... </pre>
</article></slide><slide class=""><hgroup><h2>Dealing with big arrays</h2></hgroup><article id="dealing-with-big-arrays">
<p>For big data file we can use <strong>memory-mapped</strong> files with the <strong>bigmemory</strong> package. In case the <strong>bigmemory</strong> package is not installed execute the command:</p>
<pre class = 'prettyprint lang-r'>install.packages("bigmemory")</pre>
<pre class = 'prettyprint lang-r'>library(bigmemory)
bm <- big.matrix(1e8, 3, backingfile = "bm", backingpath = getwd())
bm</pre>
<p>the large array can be retrieved for subsequent use as follows:</p>
<pre class = 'prettyprint lang-r'>my.bm <- attach.big.matrix(file.path(getwd(), "bm.desc"))</pre>
</article></slide><slide class=""><hgroup><h2>Dealing with big arrays</h2></hgroup><article id="dealing-with-big-arrays-1">
<p>now, work with chunks of \(10^7\) rows,</p>
<pre class = 'prettyprint lang-r'>chunksize <- 1e7
start <- 1
while (start <= nrow(bm)) {
end <- min(start + chunksize -1, nrow(bm))
chunksize <- end - start + 1
bm[start:end, 1] <- rpois(chunksize, 1e3)
bm[start:end, 2] <- sample(0:1, chunksize, TRUE, c(0.7,0.3))
bm[start:end, 3] <- runif(chunksize, 0, 1e5)
start <- start + chunksize
}</pre>
</article></slide><slide class=""><hgroup><h2>Summary</h2></hgroup><article id="summary">
<ul>
<li><p>We studied some methods to monitor memory usage: <strong>Rprof</strong> and <strong>gc</strong></p></li>
<li><p>One can also use the <strong>pryr</strong> and <strong>lineprof</strong> packages to have more detailed information, for instance memory changes upon creating objects and data duplication</p></li>
<li><p>To save space one can use the <strong>bigmemory</strong> package which works with file handlers instead of the actual data</p></li>
</ul>
</article></slide><slide class=""><hgroup><h2>References</h2></hgroup><article id="references">
<ul>
<li><a href='https://swcarpentry.github.io/r-novice-inflammation/' title=''>https://swcarpentry.github.io/r-novice-inflammation/</a></li>
<li><a href='https://www.tutorialspoint.com/r/index.htm' title=''>https://www.tutorialspoint.com/r/index.htm</a></li>
<li>R High Performance Programming. Aloysius, Lim; William, Tjhi. Packt Publishing, 2015.</li>
<li><a href='http://adv-r.had.co.nz/memory.html' title=''>http://adv-r.had.co.nz/memory.html</a></li>
<li><a href='https://blogs.oracle.com/r/managing-memory-limits-and-configuring-exadata-for-embedded-r-execution' title=''>https://blogs.oracle.com/r/managing-memory-limits-and-configuring-exadata-for-embedded-r-execution</a></li>
</ul>
<p><a href='index.html' title=''>Return to Index</a></p></article></slide>
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