How to set seed for random simulations with foreach and doMC packages?

Question

I need to do some simulations and for debugging purposes I want to use set.seed to get the same result. Here is the example of what I am trying to do:

library(foreach) library(doMC) registerDoMC(2)  set.seed(123) a <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)} set.seed(123) b <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)} 

Objects a and b should be identical, i.e. sum(abs(a-b)) should be zero, but this is not the case. I am doing something wrong, or have I stumbled on to some feature?

I am able to reproduce this on two different systems with R 2.13 and R 2.14

Answer 1

My default answer used to be "well then don't do that" (using foreach) as the snow package does this (reliably!) for you.

But as @Spacedman points out, Renaud's new doRNG is what you are looking for if you want to remain with the doFoo / foreach family.

The real key though is a clusterApply-style call to get the seeds set on all nodes. And in a fashion that coordinated across streams. Oh, and did I mention that snow by Tierney, Rossini, Li and Sevcikova has been doing this for you for almost a decade?

Edit: And while you didn't ask about snow, for completeness here is an example from the command-line:

edd@max:~$ 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 edd@max:~$ 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 edd@max:~$  

Edit: And for completeness, here is your example combined with what is in the docs for 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>  

Answer 2

Using set.seed(123, kind = "L'Ecuyer-CMRG") also does the trick and does not require an extra package:

set.seed(123, kind = "L'Ecuyer-CMRG") a <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)}  set.seed(123, kind = "L'Ecuyer-CMRG") b <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)}  identical(a,b) # TRUE