foreach %dopar% 比 for 循环慢 [英] foreach %dopar% slower than for loop

问题描述

为什么 foreach() 和 %dopar% 比 for 慢.一些小例子:

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

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

推荐答案

特别提到并举例说明，确实有时设置它会比较慢，因为必须将来自包中单独并行进程的结果组合起来做并行.

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.

参考:http://cran.r-project.org/web/packages/doParallel/vignettes/gettingstartedParallel.pdf

第 3 页:

对于小任务，调度任务和返回的开销结果可能大于执行任务本身的时间，导致性能不佳.

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.

我通过示例发现，在某些情况下，使用包会导致执行代码所需的时间缩短 50%.

I used the example to find out that in some case, using the package resulted in 50% the time needed to execute the code.

这篇关于foreach %dopar% 比 for 循环慢的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！