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| Rmd | b2043f3 | Dave Tang | 2022-11-17 | Parallel computation in R |
As stated in the foreach vignette:
Much of parallel computing comes to doing three things: splitting the problem into pieces, executing the pieces in parallel, and combining the results back together.
There are several packages that make it easy to run tasks in parallel:
foreach package and
acts as an interface between foreach and the
parallel package.system.timeFrom ?proc.time:
The “user time” is the CPU time charged for the execution of user instructions of the calling process.
The “system time” is the CPU time charged for execution by the system on behalf of the calling process.
Elapsed time is the amount of time that has elapsed/passed. The
user and system time while sleeping is close
to zero because the CPU is idly waiting and not executing anything.
system.time(
Sys.sleep(5)
) user system elapsed
0.000 0.000 5.005 More information is provided on Stack Overflow:
“User CPU time” gives the CPU time spent by the current process (i.e., the current R session and outside the kernel)
“System CPU time” gives the CPU time spent by the kernel (the operating system) on behalf of the current process. The operating system is used for things like opening files, doing input or output, starting other processes, and looking at the system clock: operations that involve resources that many processes must share.
Create a list of 100 data frames each with 5,000 observations across 100 variables.
create_df <- function(n, m, seed = 1984){
set.seed(seed)
as.data.frame(
matrix(
data = rnorm(n = n * m),
nrow = n,
ncol = m
)
)
}
my_list <- lapply(1:100, function(x) create_df(5000, 100, x))
length(my_list)[1] 100parallelLoad the parallel package.
library(parallel)Create a summary of each variable in each data frame without parallelisation.
system.time(
my_sum <- lapply(my_list, summary)
) user system elapsed
4.237 0.011 4.258 The mclapply function can be used to process a list in
parallel. Note that this function uses forking, which is not available
on Windows.
system.time(
my_sum_mc <- mclapply(my_list, summary, mc.cores = params$threads)
) user system elapsed
2.612 0.806 0.764 Compare the two summaries.
identical(my_sum, my_sum_mc)[1] TRUEAnother way to run the jobs in parallel is via sockets. For Windows
users, you will need to use this method for parallelisation. In
addition, you need to use the parLapply function instead of
mclapply.
cl <- makeCluster(params$threads)
system.time(
my_sum_sock <- parLapply(cl, my_list, summary)
) user system elapsed
0.745 0.375 2.112 stopCluster(cl)
identical(my_sum_mc, my_sum_sock)[1] TRUEpbapplyParallelisation with a progress bar!
library(pbapply)
cl <- makeCluster(params$threads)
system.time(
my_sum_pb <- pblapply(my_list, summary, cl = cl)
) user system elapsed
0.614 0.322 2.097 stopCluster(cl)
identical(my_sum_mc, my_sum_pb)[1] TRUEdoParallelLoad the doParallel package.
library(doParallel)Loading required package: foreachLoading required package: iteratorsUsing foreach.
cl <- makeCluster(params$threads)
registerDoParallel(cl)
system.time(
my_sum_dopar <- foreach(l = my_list) %dopar% {
summary(l)
}
) user system elapsed
0.897 0.406 2.478 stopCluster(cl)
identical(my_sum_mc, my_sum_dopar)[1] TRUEBiocParallelLoad BiocParallel.
library(BiocParallel)Using bplapply.
param <- SnowParam(workers = params$threads, type = "SOCK")
system.time(
my_sum_bp <- bplapply(my_list, summary, BPPARAM = param)
) user system elapsed
0.833 0.273 7.934 identical(my_sum_mc, my_sum_bp)[1] TRUEfurrrLoad required libraries.
library(furrr)Loading required package: futurelibrary(purrr)
Attaching package: 'purrr'The following objects are masked from 'package:foreach':
accumulate, whenMap without parallelisation.
system.time(
my_sum_pur <- map(my_list, summary)
) user system elapsed
4.170 0.427 4.608 identical(my_sum_mc, my_sum_pur)[1] TRUEMap with parallelisation.
plan(multisession, workers = params$threads)
system.time(
my_sum_fur <- future_map(my_list, summary)
) user system elapsed
0.744 0.674 3.381 identical(my_sum_pur, my_sum_fur)[1] TRUESo, which package should you use? BiocParallel and
furrr are tailored for use with Bioconductor and
purrr, so use those packages accordingly.
For parallelisation over a list, use parallel. The foreach
function provides more flexibility when parallelising, so use the
doParallel package if you have a more complicated task.
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] purrr_1.0.1 furrr_0.3.1 future_1.32.0
[4] BiocParallel_1.34.2 doParallel_1.0.17 iterators_1.0.14
[7] foreach_1.5.2 pbapply_1.7-0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] sass_0.4.6 utf8_1.2.3 stringi_1.7.12 listenv_0.9.0
[5] digest_0.6.31 magrittr_2.0.3 evaluate_0.21 fastmap_1.1.1
[9] rprojroot_2.0.3 jsonlite_1.8.5 processx_3.8.1 whisker_0.4.1
[13] ps_1.7.5 promises_1.2.0.1 httr_1.4.6 fansi_1.0.4
[17] codetools_0.2-19 jquerylib_0.1.4 cli_3.6.1 rlang_1.1.1
[21] parallelly_1.36.0 cachem_1.0.8 yaml_2.3.7 tools_4.3.0
[25] httpuv_1.6.11 globals_0.16.2 vctrs_0.6.2 R6_2.5.1
[29] lifecycle_1.0.3 git2r_0.32.0 stringr_1.5.0 fs_1.6.2
[33] pkgconfig_2.0.3 callr_3.7.3 pillar_1.9.0 bslib_0.5.0
[37] later_1.3.1 glue_1.6.2 Rcpp_1.0.10 xfun_0.39
[41] tibble_3.2.1 rstudioapi_0.14 knitr_1.43 htmltools_0.5.5
[45] snow_0.4-4 rmarkdown_2.22 compiler_4.3.0 getPass_0.2-2