I think this may be an R 2.14 vs R 2.13 difference: like you I get different results for each run the beta of Revolution R Enterprise 6.0, which has the R 2.14.2 engine (see below). In earlier versions of R, you can manage parallel random number streams with the rsprng library.
By the way, you can always ask questions about Revolution R in the forum, which is moderated by the tech support team: http://forums.revolutionanalytics.com/forums/forum.php HTH, # David Smith library(foreach) library(doSNOW) registerDoSNOW(makeCluster(8, type = "SOCK")) getDoParWorkers() getDoParName() getDoParVersion() mySamples <- foreach (jj = 1:4, .combine=cbind) %dopar% {return(sample(1:10,10,replace=TRUE))} mySamples # > mySamples result.1 result.2 result.3 result.4 [1,] 9 4 2 1 [2,] 1 4 6 5 [3,] 6 1 10 4 [4,] 4 2 2 4 [5,] 9 1 4 8 [6,] 2 1 4 8 [7,] 3 2 3 3 [8,] 1 3 6 7 [9,] 8 8 7 5 [10,] 6 4 6 9 On Mon, May 7, 2012 at 6:18 PM, pdb <ph...@philbrierley.com> wrote: > I know this is not a revolution support forum, but as anyone noticed the > following? > > I have a foreach loop to generate random samples. If I run the exact code > below in normal r (2.14.1) it works as expected, but if I run it from > revolution 4.2.0 each loop returns the same numbers. > > The only way I can get revolution to give different numbers is using 1 > instead of 8 in > registerDoSNOW(makeCluster(8, type = "SOCK")) > > but that seems to defeat the point. > > > > > library(foreach) > library(doSNOW) > registerDoSNOW(makeCluster(8, type = "SOCK")) > getDoParWorkers() > getDoParName() > getDoParVersion() > > > mySamples <- foreach (jj = 1:4, .combine=cbind) %dopar% { > return(sample(1:10,10,replace=TRUE)) > } > > mySamples > > ########## > r 2.14.1 > ########## > > > library(foreach) > > library(doSNOW) > > registerDoSNOW(makeCluster(8, type = "SOCK")) > > getDoParWorkers() > [1] 8 > > getDoParName() > [1] "doSNOW" > > getDoParVersion() > [1] "1.0.6" > > > > > > mySamples <- foreach (jj = 1:4, .combine=cbind) %dopar% { > + return(sample(1:10,10,replace=TRUE)) > + } > > > > mySamples > result.1 result.2 result.3 result.4 > [1,] 5 3 1 4 > [2,] 1 10 10 3 > [3,] 7 9 4 9 > [4,] 2 5 9 3 > [5,] 2 7 10 1 > [6,] 7 8 10 10 > [7,] 6 9 10 4 > [8,] 8 6 6 2 > [9,] 10 7 9 4 > [10,] 2 4 1 9 > > > > > #################### > # revolution r > #################### > > > library(foreach) > Loading required package: iterators > Loading required package: codetools > foreach: simple, scalable parallel programming from REvolution Computing > Use REvolution R for scalability, fault tolerance and more. > http://www.revolution-computing.com > > library(doSNOW) > Loading required package: snow > > registerDoSNOW(makeCluster(8, type = "SOCK")) > > getDoParWorkers() > [1] 8 > > getDoParName() > [1] "doSNOW" > > getDoParVersion() > [1] "1.0.3" > > > > > > mySamples <- foreach (jj = 1:4, .combine=cbind) %dopar% { > + return(sample(1:10,10,replace=TRUE)) > + } > > > > mySamples > result.1 result.2 result.3 result.4 > [1,] 4 4 4 4 > [2,] 10 10 10 10 > [3,] 4 4 4 4 > [4,] 10 10 10 10 > [5,] 5 5 5 5 > [6,] 5 5 5 5 > [7,] 9 9 9 9 > [8,] 2 2 2 2 > [9,] 6 6 6 6 > [10,] 9 9 9 9 > > > > -- > View this message in context: > http://r.789695.n4.nabble.com/revolution-foreach-oddity-tp4616237.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- David M Smith <da...@revolutionanalytics.com> VP of Marketing, Revolution Analytics http://blog.revolutionanalytics.com Tel: +1 (650) 646-9523 (Palo Alto, CA, USA) [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
