This question already has answers here:
Why is the parallel package slower than just using apply?
(3个答案)
7年前关闭。
为什么
一些附加信息:
(3个答案)
7年前关闭。
为什么
foreach()
的%dopar%
比for
慢。一些小事:library(parallel)
library(foreach)
library(doParallel)
registerDoParallel(cores = detectCores())
I <- 10^3L
for.loop <- function(I) {
out <- double(I)
for (i in seq_len(I))
out[i] <- sqrt(i)
out
}
foreach.do <- function(I) {
out <- foreach(i = seq_len(I), .combine=c) %do%
sqrt(i)
out
}
foreach.dopar <- function(I) {
out <- foreach(i = seq_len(I), .combine=c) %dopar%
sqrt(i)
out
}
identical(for.loop(I), foreach.do(I), foreach.dopar(I))
## [1] TRUE
library(rbenchmark)
benchmark(for.loop(I), foreach.do(I), foreach.dopar(I))
## test replications elapsed relative user.self sys.self user.child sys.child
## 1 for.loop(I) 100 0.696 1.000 0.690 0.000 0.0 0.000
## 2 foreach.do(I) 100 121.096 173.989 119.463 0.056 0.0 0.000
## 3 foreach.dopar(I) 100 120.297 172.841 111.214 6.400 3.5 6.734
一些附加信息:
sessionInfo()
## R version 3.0.0 (2013-04-03)
## Platform: x86_64-unknown-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=ru_RU.UTF-8 LC_NUMERIC=C LC_TIME=ru_RU.UTF-8
## [4] LC_COLLATE=ru_RU.UTF-8 LC_MONETARY=ru_RU.UTF-8 LC_MESSAGES=ru_RU.UTF-8
## [7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=ru_RU.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] doMC_1.3.0 rbenchmark_1.0.0 doParallel_1.0.1 iterators_1.0.6 foreach_1.4.0 plyr_1.8
##
## loaded via a namespace (and not attached):
## [1] codetools_0.2-8 compiler_3.0.0 tools_3.0.0
getDoParWorkers()
## [1] 4
最佳答案
专门通过示例进行了说明和说明,由于有时必须组合来自doParallel包中单独并行进程的结果,因此设置起来确实有时会比较慢。
引用:http://cran.r-project.org/web/packages/doParallel/vignettes/gettingstartedParallel.pdf
第3页:
我使用该示例来发现在某些情况下,使用该程序包可以节省执行代码所需时间的50%。
10-08 02:19