search for: detectcor

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 currently have quite a heavy workload, otherw...

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 no...

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 of 0.5s sleep. require(snow) require(doSNOW) require(foreach) cl <- makeCluster(64L, "SOCK") regist...

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" "22...

A summary for reference: the new detectCores() for Windows in R-devel seems to be working both for logical and physical cores on systems with >64 logical processors? (thanks to Arun for testing!). If the feature is important for anyone particularly using an older version of Windows and/or on a system with >64 logical processors,...

Dear Arun, thank you for checking the workaround scripts. I've modified detectCores() to use GetLogicalProcessorInformationEx. It is in revision 75198 of R-devel, could you please test it on your machines? For a binary, you can wait until the R-devel snapshot build gets to at least this svn revision. Thanks for the link to the processor groups documentation. I don't have...

Dear Arun, thank you for the report. I agree with the analysis, detectCores() will only report logical processors in the NUMA group in which R is running. I don't have a system to test on, could you please check these workarounds for me on your systems? # number of logical processors - what detectCores() should return out <- system("wmic cpu get numberof...

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, b...

In R 3.3.2 detectCores() in package parallel reports 2 rather than 1 on Raspberry Pi B+ running Raspbian. (This report is just 'for the record'. The model is superseded and I think no longer produced.) The problem seems to be caused by grep processor /proc/cpuinfo processor : 0 model name : ARMv6-compati...

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 aborting the execution of mclapply. It doesn't finish even if I reduce the elements to be...

...eed for l'Ecuyer's rng with just the information in .Random.seed? Thanks & Cheers, Marius Here is the minimal example: require(doSNOW) require(foreach) doForeach <- function(n, seed=1, type="MPI") { ## create cluster object cl <- snow::makeCluster(parallel::detectCores(), type=type) on.exit(snow::stopCluster(cl)) ## shut down cluster and terminate execution environment registerDoSNOW(cl) ## register the cluster object with foreach ## seed if(seed=="L'Ecuyer-CMRG") { if(!exists(".Random.seed")) stop(".Random....

...n try mclapply. On May 16, 2023 3:10:45 PM PDT, akshay kulkarni <akshay_e4 at hotmail.com> wrote: >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 aborting the execution of mclapply. It doesn't finish even if...

...y, makeCluster() hangs when I run it. I first noticed the problem when trying to run Rstan with multiple cores and the traced it back to the core package parallel. The following results in R hanging after the call to makeCluster. library(parallel) # Calculate the number of cores no_cores <- detectCores() - 1 # Initiate cluster cl <- makeCluster(no_cores) I'm running MacOS High Sierra 10.13.3 (17D47) on a MacbookPro 2017 laptop with 4 cores. platform?????? x86_64-apple-darwin15.6.0 arch?????????? x86_64 os???????????? darwin15.6.0 system???????? x86_64, darwin15.6.0 status major??????...

...in order to compute f(x, y) parallelly, I had to define f(x, y) as f(x) and tried to access y within the function, whereas y was defined outside of f(x). Script: library(parallel) f <- function(x) { z <- 2 * x + .GlobalEnv$y # Try to access y in the global scope. return(z) } np <- detectCores(logical = FALSE) # Two cores of my laptop x <- seq(1, 10, by = 1) y <- 0.5 # Y may be an array in reality. cl <- makeCluster(np) # initiate the cluster r <- parLapply(cl, x, f) # apply f to x for parallel computing stopCluster(cl) The r was a list with 10 empty elements which m...

...n parApply returns a matrix? library(parallel) x <- matrix(rnorm(8), nc = 2) apply(x, 2, function(y) y) [,1] [,2] [1,] -0.9649685 0.91339851 [2,] -1.4313140 0.13457671 [3,] 1.0499248 1.58967879 [4,] -1.8974411 0.03639876 cl <- makeCluster(getOption("cl.cores", detectCores())) parApply(cl, x, 2, function(y) y) [,1] [,2] [1,] -0.9649685 0.91339851 [2,] -1.4313140 0.13457671 [3,] 1.0499248 1.58967879 [4,] -1.8974411 0.03639876 parCapply(cl, x, function(y) y) [1] -0.96496852 -1.43131396 1.04992479 -1.89744113 0.91339851 0.13457671 [7] 1.58967...

...nameToID<-function(name, dbConn){ #dbConn : active db connection formed via odbcDriverConnect #name : a char string sqlQuery(dbConn, paste("select id from table where name='", name, "'", sep="")) } mc<-detectCores() cl<-makeCluster(mc) clusterExport(cl, c("sqlQuery", "dbConn")) parSapply(cl, names, nameToID, dbConn=dbConn) #incorrect passing of nameToID's second argument ### As in the comment, this is not the correct way to assign the second argument to nameToID. I have also...

This is the specific error messsage from R CMD check --as-cran Error in .check_ncores(length(names)) : 16 simultaneous processes spawned Calls: prepost -> makeCluster -> makePSOCKcluster -> .check_ncores Execution halted Thanks, Ravi ________________________________ From: Ravi Varadhan Sent: Saturday, July 1, 2023 1:15 PM To: R-Help <r-help at r-project.org> Subject: Number

...problem when trying >> to run Rstan with multiple cores and the traced it back to the core package >> parallel. The following results in R hanging after the call to makeCluster. >> >> library(parallel) >> >> # Calculate the number of cores >> no_cores <- detectCores() - 1 >> >> # Initiate cluster >> cl <- makeCluster(no_cores) >> >> I'm running MacOS High Sierra 10.13.3 (17D47) on a MacbookPro 2017 laptop >> with 4 cores. >> >> platform x86_64-apple-darwin15.6.0 >> arch x86_64 >&...

Dear List Sorry if this question seems very basic. Is there a function to pro grammatically find number of processors in my system _ I want to pass this as a parameter to snow in some serial code to parallel code functions Regards Ajay Websites- http://decisionstats.com http://dudeofdata.com Linkedin- www.linkedin.com/in/ajayohri

...l::mc*** API? <== > > (*) One can also somewhat disable forking in 'parallel' by using > 'cgroups' limiting the process to a single core (see also > https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17641). That will > handle code that uses mc.cores = parallel::detectCores(), which there > is a lot of. I guess it will cause run-time error (on 'mc.cores' must > be >= 1) for code that uses the second most common used mc.cores = > parallel::detectCores() - 1, which is unfortunately also very common. > I find the use of hardcoded detectCores() u...