我的默认回答“那么不这样做”(使用foreach) (可靠!)给你。
但正如@Spacedman指出的那样,如果您想留在doFoo
/foreach家族中,Renaud的新doRNG是您正在寻找的。
尽管真正的关键是通过clusterApply风格的调用来获取在所有节点上设置的种子。并且以跨越流的方式进行协调。哦,我有没有提到Tierney,Rossini,Li和Sevcikova的snow已经为你做了近十年?
编辑:虽然你并没有问snow,为了完整性这里是在命令行的例子:
[email protected]:~$ r -lsnow -e'cl <- makeSOCKcluster(c("localhost","localhost"));\
clusterSetupRNG(cl);\
print(do.call("rbind", clusterApply(cl, 1:4, \
function(x) { stats::rnorm(1) })))'
Loading required package: utils
Loading required package: utils
Loading required package: rlecuyer
[,1]
[1,] -1.1406340
[2,] 0.7049582
[3,] -0.4981589
[4,] 0.4821092
[email protected]:~$ r -lsnow -e'cl <- makeSOCKcluster(c("localhost","localhost"));\
clusterSetupRNG(cl);\
print(do.call("rbind", clusterApply(cl, 1:4, \
function(x) { stats::rnorm(1) })))'
Loading required package: utils
Loading required package: utils
Loading required package: rlecuyer
[,1]
[1,] -1.1406340
[2,] 0.7049582
[3,] -0.4981589
[4,] 0.4821092
[email protected]:~$
编辑:以及物品是否完整,这里就是你们的榜样合并什么是在文档为doRNG
> library(foreach)
R> library(doMC)
Loading required package: multicore
Attaching package: ‘multicore’
The following object(s) are masked from ‘package:parallel’:
mclapply, mcparallel, pvec
R> registerDoMC(2)
R> library(doRNG)
R> set.seed(123)
R> a <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)}
R> set.seed(123)
R> b <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)}
R> identical(a,b)
[1] FALSE ## ie standard approach not reproducible
R>
R> seed <- doRNGseed()
R> a <- foreach(i=1:2,combine=cbind) %dorng% { rnorm(5) }
R> b <- foreach(i=1:2,combine=cbind) %dorng% { rnorm(5) }
R> doRNGseed(seed)
R> a1 <- foreach(i=1:2,combine=cbind) %dorng% { rnorm(5) }
R> b1 <- foreach(i=1:2,combine=cbind) %dorng% { rnorm(5) }
R> identical(a,a1) && identical(b,b1)
[1] TRUE ## all is well now with doRNGseed()
R>
谢谢以雪为例。我不熟悉R中并行编程的复杂性,所以我开始使用'foreach'从无并行代码到并行的无痛转换。我知道我错过了一些东西。 – mpiktas
好吧,这就是为什么我们都在几年前开始下雪,因为从标准* apply()函数转换到并行函数很容易:) –