r - 如何在 R 中并行运行 for 循环

Question

假设我有一个函数f()和一个向量d

f <- function(x) dexp(x, 2)
d <- runif(10, 1, 5)

现在我想执行一个 for 循环

dnew <- numeric(length(d))
for (i in seq_along(dnew)){
   dnew[i] <- f(d[i])
}

. 我怎样才能并行执行此操作？

标签： rparallel-processingparallel.foreach

Answer 1

• 示例代码在没有 for 循环的情况下更快：

  dnew2 <- f(d)          # 'f()' and 'd' from question
  all.equal(dnew, dnew2) # 'dnew' from question 
  [1] TRUE
  
  library(microbenchmark)
  microbenchmark('for loop' = for (i in seq_along(dnew)){ dnew[i] <- f(d[i]) },
                 'vectorized' = { dnew2 = f(d) })
  Unit: microseconds
         expr    min      lq     mean  median     uq    max neval
     for loop 15.639 16.4455 17.66640 17.0045 18.089 43.938   100
   vectorized  1.249  1.3140  1.44039  1.3845  1.516  2.424   100
  

• 它可以与foreach并行化：

  library(foreach)
  library(doParallel); registerDoParallel(2)
  dnew3 <- foreach(i=seq_along(dnew), .combine=c) %dopar% {
      f(d[i])
  }
  all.equal(dnew, dnew3)
  [1] TRUE
  

  并行化版本较慢，因为并行开销大于收益。

  microbenchmark('for loop' = for (i in seq_along(dnew)){ dnew[i] <- f(d[i]) },
                 'foreach' = { dnew3 <- foreach(i=seq_along(dnew), .combine=c) %dopar% {
                                        f(d[i]) } 
                              })
  Unit: microseconds
       expr       min        lq        mean     median         uq       max neval
   for loop    17.799    22.048    31.01027    32.7615    37.0945    67.265   100
    foreach 11875.845 13003.558 13576.64759 13427.1015 14041.3455 17782.638   100
  

• 如果f()需要更多时间进行评估，则foreach版本更快：

  f <- function(x){
      Sys.sleep(.3)
      dexp(x, 2)
  }
  microbenchmark('for loop' = for (i in seq_along(dnew)){ dnew[i] <- f(d[i]) },
                 'foreach' = {dnew3 <- foreach(i=seq_along(dnew), .combine=c) %dopar% {
                                       f(d[i]) }
                  }, times=2)
  Unit: seconds
       expr      min       lq     mean   median       uq      max neval
   for loop 3.004271 3.004271 3.004554 3.004554 3.004837 3.004837     2
    foreach 1.515458 1.515458 1.515602 1.515602 1.515746 1.515746     2