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我在 Linux 上运行并且mclapply很容易使用。parlapply即使在使用后，我也遇到了一些错误clusterEvalQ。

在我进一步解决问题之前，是否有任何意义，即两者之间是否存在显着的速度差异，或者人们只是parLapply在 Windows 上使用？

我已经阅读parLapplyLB并可以看到这种方法的用途，但是如果我严格查看，mclapplyFORKparlapply方法和 PSOCK 方法的速度是否有很大差异？

我的职能性质可能决定答案；它正在使用stri_extract.

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1 回答 1

6

一些快速的基准测试表明这mclapply可能会稍微快一些，但这可能取决于特定的系统和问题。工作越平衡，实际任务越慢，您使用的功能就越不重要。

library(parallel)
library(microbenchmark)

microbenchmark(
  parLapply = {cl <- makeCluster(2)
  parLapply(cl, rep(1:7, 3), function(x) {set.seed(1); rnorm(10^x)})
  stopCluster(cl)},
  mclapply = {mclapply(rep(1:7 , 3), function(x) {set.seed(1); rnorm(10^x)}, mc.cores = 2)},
  times = 10
)

#Unit: seconds
#     expr     min      lq     mean   median       uq      max neval
#parLapply 1.85548 2.04397 3.332970 3.071284 4.323514 6.294364    10
#mclapply  1.62610 1.65288 2.217407 1.849594 2.243418 5.435189    10


microbenchmark(
  parLapply = {cl <- makeCluster(2)
  parLapply(cl, rep(6, 20), function(x) {set.seed(1); rnorm(10^x)})
  stopCluster(cl)},
  mclapply = {mclapply(rep(6, 20), function(x) {set.seed(1); rnorm(10^x)}, mc.cores = 2)},
  times = 10
)

#Unit: milliseconds
#     expr      min        lq      mean   median       uq      max neval
#parLapply 1150.657 1188.9750 1705.1364 1242.739 2071.276 3785.516    10
# mclapply  820.692  932.2262  994.4404 1000.402 1079.930 1117.863    10

sessionInfo()
#R version 3.3.1 (2016-06-21)
#Platform: x86_64-pc-linux-gnu (64-bit)
#Running under: Ubuntu 14.04.5 LTS
#
#locale:
# [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C               LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
# [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8    LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
# [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
#
#attached base packages:
#[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     
#
#other attached packages:
#[1] microbenchmark_1.4-2.1 doParallel_1.0.10      iterators_1.0.8        foreach_1.4.3         
#
#loaded via a namespace (and not attached):
# [1] colorspace_1.2-6 scales_0.4.0     plyr_1.8.4       tools_3.3.1      gtable_0.2.0     Rcpp_0.12.4     
# [7] ggplot2_2.1.0    codetools_0.2-14 grid_3.3.1       munsell_0.4.3
于 2016-08-15T16:12:49.583 回答