similar to: How to construct a valid seed for l'Ecuyer's method with given .Random.seed?

Dear expeRts, ./src/library/base/man/Random.Rd says that L'Ecuyer requires a seed of length 6. ./src/library/parallel/man/RngStream.Rd also mentions this, but only in the text part; In the "Arguments"-part, it says that "seed" has to be of length 7 Also: ,---- | > RNGkind("L'Ecuyer-CMRG") | > length(.Random.seed) | [1] 7 `---- Is the docu old? Some

Hi, This is most likely only a minor technicality, but I saw the following: On page 6 of the 'parallel' vignette (http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf), the random-number generator "L'Ecuyer-CMRG" is said to have seed "(x_n, x_{n-1}, x_{n-2}, y_n, y_{n-1}, y_{n-2})". However, in L'Ecuyer et al. (2002), the seed is given with

Dear All, Is the following expected behaviour? set.seed(1) library(parallel) cl = makeCluster(5) clusterSetRNGStream(cl, iseed = NULL) parSapply(cl, 1:5, function(i) sample(1:10, 1)) # 7 4 2 10 10 clusterSetRNGStream(cl, iseed = NULL) # 7 4 2 10 10 parSapply(cl, 1:5, function(i) sample(1:10, 1)) stopCluster(cl) The documentation could be read either way, e.g. * iseed: An integer to be

On Tue, Feb 3, 2015 at 10:39 AM, Marius Hofert <marius.hofert at uwaterloo.ca> wrote: > Hi, > > This is most likely only a minor technicality, but I saw the > following: On page 6 of the 'parallel' vignette > (http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf), > the random-number generator "L'Ecuyer-CMRG" is said to have seed >

I've got another edition of my simulation replication framework. I'm attaching 2 R files and pasting in the readme. I would especially like to know if I'm doing anything that breaks .Random.seed or other things that R's parallel uses in the environment. In case you don't want to wrestle with attachments, the same files are online in our SVN

Greetings: In R parallel's vignette, there is a comment "It would however take only slightly more work to allocate a stream to each task." (p.6). I've written down a working example that can allocate not just one, but several separate seeds for each task. (We have just a few project here that need multiple streams). I would like to help work that up for inclusion in the

Dear expeRts, This is a similar post as on R-SIG-MAC [I didn't receive an answer there; not sure if it was the right place to post either]. I'm running R version 2.12.1 (Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)) on a MacBook Pro under Mac OS X 10.6.6. The following minimal example runs fine under this setup. However, if I am connected to a server via a VPN client [Cisco

I am trying to run a monte carlo process using snow with a MPI cluster. I have ~thirty processors to run the algorithm on and I want to run it 5000 times and take the average of the output. A very simple way to do this is to divide 5000 by the number of processors to get a number n and tell each processor to run the algorithm n times. I realize there are more efficient ways to manage the

In a vanilla R 2.14.0 GUI session (on Windows XP SP3): > library(parallel) > cl<-makePSOCKcluster(2) > RNGkind() [1] "Mersenne-Twister" "Inversion" > clusterSetRNGStream(cl) > RNGkind() [1] "L'Ecuyer-CMRG" "Inversion" > stopCluster(cl) Is it intentional that clusterSetRNGStream() changes the RNG kind in the master process?

On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> wrote: (Sorry to be a bit slow responding.) You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. To your question >

Hi, When loading a package that provides the user-supplied RNG hook user_unif_rand, calling getNativeSymbolInfo("user_unif_rand") returns informations about the loaded symbol. I am using this to identify which package currently provides the RNG hook. The results are the same on windows and linux if only one library provides the hook. If one loads a second package that provides this

Dear R-devel list, R's detectCores() function internally calls "ncpus" function to get the total number of logical processors. However, this doesnot seem to take NUMA into account on Windows machines. On a machine having 48 processors (24 cores) in total and windows server 2012 installed, if NUMA is enabled and has 2 nodes (node 0 and node 1 each having 24 CPUs), then R's

Hi, Both under the current R-devel (r66456) and a version from about 3 months ago, I experience the following behavior: > parallel::detectCores(TRUE) Error in system(cmd, TRUE) : error in running command > traceback() 3: system(cmd, TRUE) 2: gsub("^ +", "", system(cmd, TRUE)[1]) 1: parallel::detectCores(TRUE) > This is on Ubuntu 14.04. Does anybody else see this? [I

Dear Tomas, thank you very much. I installed r-devel r75201 and tested. The machine with 88 cores has NUMA disabled. It therefore has 2 processor groups with 64 and 24 processors each. require(parallel) detectCores() # [1] 88 This is great! Then I went on to test with a simple 'foreach()' loop. I started with 64 processors (max limit of 1 processor group). I ran with a simple function

Dear Tomas, thank you for looking into this. Here's the output: # number of logical processors - what detectCores() should return out <- system("wmic cpu get numberoflogicalprocessors", intern=TRUE) [1] "NumberOfLogicalProcessors \r" "22 \r" "22 \r" [4] "20 \r"

Not sure what should happen theoretically for the code in vseq.c, but I see the same pattern with the R generators I tried (default, Super-Duper, and L'Ecuyer) and with with bash $RANDOM using N <- 10000 X1 <- replicate(N, as.integer(system("bash -c 'echo $RANDOM'", intern = TRUE))) X2 <- replicate(N, as.integer(system("bash -c 'echo $RANDOM'",

In the 'parallel' package there is detectCores(), which tries its best to infer the number of cores on the current machine. This is useful if you wish to utilize the *maximum* number of cores on the machine. Several are using this to set the number of cores when parallelizing, sometimes also hardcoded within 3rd-party scripts/package code, but there are several settings where you wish to

Hello, happy new year. I've come back to a problem from last spring. I need to understand what what is the technically correct method to save a seed and then re-set it. Although this example arises in the context of MT19937, I'm needing to do this with other generators as well, including L'Ecuyer streams. The puzzle is this comment in ?Random: "?set.seed? is the recommended way

Dear members, I am using arfima in an mclapply construction (from the parallel package): Browse[2]> LYG <- mclapply(LYGH, FUN = arfima, mc.cores = detectCores()) ^C Browse[2]> LYG <- mclapply(LYGH[1:10], FUN = arfima, mc.cores = detectCores()) ^C Browse[2]> LYG <- mclapply(LYGH[1:2], FUN = arfima, mc.cores = detectCores()) ^C You can see that I am

The byte pixies have rolled up R-2.14.0.tar.gz (codename "Great Pumpkin") at 9:00 this morning. This is a development release with several new features; see the list below for details. You can get it from http://cran.r-project.org/src/base/R-2/R-2.14.0.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. Binaries for various platforms will appear in due course. For