修复用于不同数量内核的并行仿真运行的种子 [英] Fixing the seed for parallel simulation runs with different number of cores

问题描述

我想并行进行模拟研究以加快速度，并且我还要考虑可重复性.特别是，我希望获得与在顺序模拟运行开始时使用set.seed相同的结果. 这是我尝试设置它的示例(我在这里故意使用.inorder=T):

I'd like to parallelize a simulation study to speed it up and I'd also like to account for reproducibility. In particular, I'd like to obtain the same result as if I used set.seed at the beginning of a sequential simulation run. Here is an example how I try to set it up (I purposefully use .inorder=T here):

library(doSNOW)
library(rlecuyer)

nr.cores = 4
nr.simulations = 10 
sample.size = 100000

seed = 12345

cl = makeCluster(nr.cores)
registerDoSNOW(cl)
clusterExport(cl=cl, list=c('sample.size'), envir=environment())
clusterSetupRNGstream(cl,rep(seed,6))

result = foreach(i=1:nr.simulations, .combine = 'c', .inorder=T)%dopar%{
  tmp = rnorm(sample.size)
  tmp[sample.size]
}

stopCluster(cl)

print(paste0('nr.cores = ',nr.cores,'; seed = ',seed,'; time =',Sys.time()))
print(result)

多次运行此示例后，我有两个问题:

There are two questions that I have after running this example several times:

1. 核心数会影响生成的序列，例如，对于nr.cores=1和4，仅第一个值重合，而对于nr.cores=4和8，前四个值重合. 有没有办法使它独立于nr.cores?从概念上讲，我想我可以创建大小为nr.simulations * sample.size的RNG流，将其拆分为nr.simulations片段，并将它们始终以相同的顺序分配给节点.更简单的是，我可以修复(不同)种子的nr.simulations值，然后将它们以固定顺序再次传递给节点.可以使用某种类型的节点映射来完成此操作，节点可以使用该映射来从表中读取其适当的种子值.有办法吗?

1. The number of cores impacts the resulting sequence, e.g., for nr.cores=1 and 4 only the first values coincide, and for nr.cores=4 and 8 the first four values coincide. Is there a way to have it independent of the nr.cores? Conceptually, I’d imagine I could create an RNG stream of size nr.simulations * sample.size, split it to nr.simulations pieces and distribute them to the nodes always in the same order. Even simpler, I could fix nr.simulations values of (different) seeds and again pass them in a fixed order to the nodes. This could be done having some kind of node mapping which could be used by the nodes to read its appropriate seed value from a table. Is there a way to do it?

当我多次运行该脚本时，即使我不更改任何参数，也会发生(并非总是但不时地)对结果序列进行重新排序的情况(我只是一次又一次地获取文件) .对我来说，这似乎是一个错误，因为.inorder或clusterSetupRNGstream都失败了.还是我错过了什么?

When I run the script several times it happens (not always but from time to time) that the resulting sequence is reordered even though I do not change any of the parameters (I just source the file again and again). It just looks like a bug to me as either .inorder or clusterSetupRNGstream fail. Or am I missing something?

[1] "nr.cores = 4; seed = 12345; time =2017-09-08 19:00:24"
[1]  1.327091137 -1.800244293 -1.163391460  0.005980001  0.957521136  1.641354433 -1.219033091
[8] -0.238129356 -0.225193384  1.457018576

[1] "nr.cores = 4; seed = 12345; time =2017-09-08 19:00:28"
[1]  1.327091137 -1.800244293 -1.163391460  0.005980001 -0.238129356  0.957521136  1.641354433
[8] -1.219033091  0.870269174 -0.225193384

推荐答案

第一个问题:以下内容似乎对我有用

1st Q: The following seemed to work for me

library(parallel)
library(doParallel)
cl <- makeCluster(5)
registerDoParallel(cl)
seedlist <- c(100, 200, 300, 400, 500)
clusterExport(cl, 'seedlist')
foreach(I=1:5) %dopar% {set.seed(seedlist[I]); runif(1)}

[[1]]
[1] 0.3077661

[[2]]
[1] 0.5337724

[[3]]
[1] 0.9152467

[[4]]
[1] 0.1499731

[[5]]
[1] 0.8336


set.seed(100)
runif(1)
[1] 0.3077661

第二个问题:似乎是一个错误，但也许其他人有更好的线索

2nd Q: Seems like a bug but maybe someone else has a better clue

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