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R语言中for循环的并行处理方式

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这篇文章主要介绍了R语言中for循环的并行处理方式，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

前言

本文用于记录笔者在将R语言中的for语句并行化处理中的一些问题。

实验

这里使用foreach和doParallel包提供的函数实现for语句的并行处理。

for语句脚本

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func <- function(x, y, z) {
  return(x^y/z)
}
# >>> main <<<
x <- 2
y <- 3
z <- 1:100000
start <- (proc.time())[3][[1]]
a <- 0
for (i_z in z) {
  a <- a + func(x, y, i_z)
}
end <- (proc.time())[3][[1]]
print(paste('Result = ', round(a, 2), ', time = ', (end-start), 's', sep=''))

输出：

[1] "Result = 96.72, time = 0.177s"

并行化版本

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library(foreach)
library(doParallel)
func <- function(x, y, z) {
  return(x^y/z)
}
# >>> main <<<
x <- 2
y <- 3
z <- 1:100000
start <- (proc.time())[3][[1]]
cl <- makeCluster(12)
registerDoParallel(cl)
a <- foreach(z=z, .combine='rbind') %dopar% func(x, y, z)
a <- sum(a)
stopCluster(cl)
end <- (proc.time())[3][[1]]
print(paste('Result = ', round(a, 2), ', time = ', (end-start), 's', sep=''))

输出：

[1] "Result = 96.72, time = 37.988s"

总结

1、这里发现并行化所用时间大于非并行化所用过的时间，是因为需要执行的操作（func函数）过于简单，而foreach处理时会有额外的资源消耗。此时foreach额外消耗的资源远大于需要执行的操作所需的资源，因此会导致并行化后反而使用的时间增加了。所以对于一些复杂的操作比较适合使用并行化的策略。

2、foreach函数的.packages参数可以为并行化函数传递额外需要的包。

3、foreach中的参数为需要在func中循环的变量，其他固定的变量则在func中传入。参数可以是data.frame类型。

补充：R语言--for循环语句的使用

R语言for循坏语句的使用（多个for)

对于多个for循还语句，R语言的执行顺序（以3个for为例）：从外向内单个执行，里边循还完整，再往外一层，直到全部完成。话不多说，上例子：

代码：

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library(data.table)
mm<-data.table()
m<-c(1,2,3,4,5)
n<-c('a','b','c','d','e')
o<-c(6,7,8,9,10)
for (i1 in m){
  for ( i2 in n){
     for (i3 in o){
       print(c(i1,i2,i3))
       aa<-data.table(i1,i2,i3)
       bb<-rbind(mm,aa)
       
     }
  }
}

执行结果：

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[1] "1" "a" "6"
[1] "1" "a" "7"
[1] "1" "a" "8"
[1] "1" "a" "9"
[1] "1"  "a"  "10"
[1] "1" "b" "6"
[1] "1" "b" "7"
[1] "1" "b" "8"
[1] "1" "b" "9"
[1] "1"  "b"  "10"
[1] "1" "c" "6"
[1] "1" "c" "7"
[1] "1" "c" "8"
[1] "1" "c" "9"
[1] "1"  "c"  "10"
[1] "1" "d" "6"
[1] "1" "d" "7"
[1] "1" "d" "8"
[1] "1" "d" "9"
[1] "1"  "d"  "10"
[1] "1" "e" "6"
[1] "1" "e" "7"
[1] "1" "e" "8"
[1] "1" "e" "9"
[1] "1"  "e"  "10"
[1] "2" "a" "6"
[1] "2" "a" "7"
[1] "2" "a" "8"
[1] "2" "a" "9"
[1] "2"  "a"  "10"
[1] "2" "b" "6"
[1] "2" "b" "7"
[1] "2" "b" "8"
[1] "2" "b" "9"
[1] "2"  "b"  "10"
[1] "2" "c" "6"
[1] "2" "c" "7"
[1] "2" "c" "8"
[1] "2" "c" "9"
[1] "2"  "c"  "10"
[1] "2" "d" "6"
[1] "2" "d" "7"
[1] "2" "d" "8"
[1] "2" "d" "9"
[1] "2"  "d"  "10"
[1] "2" "e" "6"
[1] "2" "e" "7"
[1] "2" "e" "8"
[1] "2" "e" "9"
[1] "2"  "e"  "10"
[1] "3" "a" "6"
[1] "3" "a" "7"
[1] "3" "a" "8"
[1] "3" "a" "9"
[1] "3"  "a"  "10"
[1] "3" "b" "6"
[1] "3" "b" "7"
[1] "3" "b" "8"
[1] "3" "b" "9"
[1] "3"  "b"  "10"
[1] "3" "c" "6"
[1] "3" "c" "7"
[1] "3" "c" "8"
[1] "3" "c" "9"
[1] "3"  "c"  "10"
[1] "3" "d" "6"
[1] "3" "d" "7"
[1] "3" "d" "8"
[1] "3" "d" "9"
[1] "3"  "d"  "10"
[1] "3" "e" "6"
[1] "3" "e" "7"
[1] "3" "e" "8"
[1] "3" "e" "9"
[1] "3"  "e"  "10"
[1] "4" "a" "6"
[1] "4" "a" "7"
[1] "4" "a" "8"
[1] "4" "a" "9"
[1] "4"  "a"  "10"
[1] "4" "b" "6"
[1] "4" "b" "7"
[1] "4" "b" "8"
[1] "4" "b" "9"
[1] "4"  "b"  "10"
[1] "4" "c" "6"
[1] "4" "c" "7"
[1] "4" "c" "8"
[1] "4" "c" "9"
[1] "4"  "c"  "10"
[1] "4" "d" "6"
[1] "4" "d" "7"
[1] "4" "d" "8"
[1] "4" "d" "9"
[1] "4"  "d"  "10"
[1] "4" "e" "6"
[1] "4" "e" "7"
[1] "4" "e" "8"
[1] "4" "e" "9"
[1] "4"  "e"  "10"
[1] "5" "a" "6"
[1] "5" "a" "7"
[1] "5" "a" "8"
[1] "5" "a" "9"
[1] "5"  "a"  "10"
[1] "5" "b" "6"
[1] "5" "b" "7"
[1] "5" "b" "8"
[1] "5" "b" "9"
[1] "5"  "b"  "10"
[1] "5" "c" "6"
[1] "5" "c" "7"
[1] "5" "c" "8"
[1] "5" "c" "9"
[1] "5"  "c"  "10"
[1] "5" "d" "6"
[1] "5" "d" "7"
[1] "5" "d" "8"
[1] "5" "d" "9"
[1] "5"  "d"  "10"
[1] "5" "e" "6"
[1] "5" "e" "7"
[1] "5" "e" "8"
[1] "5" "e" "9"
[1] "5"  "e"  "10"

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原文链接：https://www.cnblogs.com/sangf/p/14336269.html