每个%的百分比比循环慢 [英] foreach %dopar% slower than for loop
问题描述
foreach()
与%dopar%
慢于 $ C>。一些小小的例子:
$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ b $ library $ $ b registerDoParallel(cores = detectCores())
I < - 10 ^ 3L
for.loop < - function(I){
out< ; - 双(I)
(我在seq_len(I))
出[i] < - sqrt(i)
出
}
foreach.do< - function(I){
out< - foreach(i = seq_len(I),.combine = c)%do%
sqrt(i)
(i = seq_len(I),.combine = c)%dopar%(%)
foreach.dopar<
sqrt(i)
出
}
相同(for.loop(I),foreach.do(I),foreach.dopar(I))
## [1] TRUE
library(rbenchmark)
基准(for.loop(I),foreach.do(I),foreach.dopar(I))
## (I)100 0.696 1.000 0.690 0.000 0.0 0.000
## 2 foreach.do(I)相对于user.self的测试复制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 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版本3.0.0(2013 -04-03)
## Platform:x86_64-unknown-linux-gnu(64-bit)
##
## locale:
## [1] LC_CTYPE = LC_COLLATE = ru_RU.UTF-8 LC_MONETARY = ru_RU.UTF-8 LC_MESSAGES = ru_RU.UTF-8
#RU_RU.UTF-8 LC_NUMERIC = C LC_TIME = ru_RU.UTF-8
## [4] #[7] LC_PAPER = C LC_NAME = C LC_ADDRESS = C
## [10] LC_TELEPHONE = C LC_MEASUREMENT = ru_RU.UTF-8 LC_IDENTIFICATION = C
##
##附加的基本软件包:
## [1] parallel stats graphics grDevices utils datasets methods base
##
##其他附加软件包:
## [1] doMC_1.3.0 rbenchmark_1.0.0 doParallel_1 .0.1 iterators_1.0.6 foreach_1.4.0 plyr_1.8
##
##通过命名空间(而不是附加)加载:
## [1] codetools_0.2-8 compiler_3.0.0 tools_3.0.0
getDoParWorkers()
## [1] 4
具体提到并用示例说明,确实有时候这样做会比较慢,因为必须将doParallel包中的单独并行进程的结果合并起来。
参考: http:/ /cran.r-project.org/web/packages/doParallel/vignettes/gettingstartedParallel.pdf
第3页:
对于小任务,调度任务和返回
结果的开销可能大于执行任务本身的时间,
导致性能差。
我用这个例子发现,在某些情况下,使用这个包会导致执行所需时间的50%该代码。
Why foreach()
with %dopar%
slower than for
. Some litle exmaple:
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
Some addition info:
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
It is specifically mentioned and illustrated with examples that indeed sometimes it's slower to set this up, because of having to combine the results from the separate parallel processes in the package doParallel.
Reference: http://cran.r-project.org/web/packages/doParallel/vignettes/gettingstartedParallel.pdf
Page 3:
With small tasks, the overhead of scheduling the task and returning the result can be greater than the time to execute the task itself, resulting in poor performance.
I used the example to find out that in some case, using the package resulted in 50% the time needed to execute the code.
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