search for: registerdosnow

...he 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(samp...

I am trying to do parallel programming and I tried this library(doSNOW) library(foreach) testfunc<-function(x){ x<-x+1 x } noc<-2 cl <- makeCluster(do.call(rbind,rep(list("localhost"),noc)), type = "SOCK") registerDoSNOW(cl) clusterExport(cl=cl,c("testfunc.r")) testl<-foreach(pp=1:2) %dopar% { testfunc(pp) } And this works but if I try to enclose my commands inside a text file to be sourced it doesn't work noc<-2 testfunc<-function(x){ x<-x+1 x } cl <- makeCluster(do.call(rbind,r...

...o the same thing twice with foreach # no problem at all foreach(i=1:2) %do% { coeftest(lm(length ~ age, data=Mandible, subset=(age <= 28))) } # Do the same thing twice with foreach and doSNOW # it fails!! the error msg was (could not find function "coeftest") cl<-makeCluster(2) registerDoSNOW(cl) foreach(i=1:2) %dopar% { coeftest(lm(length ~ age, data=Mandible, subset=(age <= 28))) } stopCluster(cl) # However if i put require(lmtest) inside the foreach loop, it works again! cl<-makeCluster(2) registerDoSNOW(cl) foreach(i=1:2) %dopar% { require(lmtest) coef...

...nd [the batch system is "LSF"]?) and let me know if you need further. > > ## ==== minimal example ==== > > library(doSNOW) # loads foreach > library(Rmpi) # for default in makeCluster() > cl<- makeCluster(3) # create cluster object with the given number of slaves > registerDoSNOW(cl) # register the cluster object with foreach > x<- foreach(i = 1:3) %dopar% { # simple test > sqrt(i) > } > x > stopCluster(cl) # properly shut down the cluster > > ## ==== minimal example ==== > > ## ==== output ==== > > Sender: LSF System<lsfadmin at a...

Hello, I was using snow to parallel-process some code in R-2.10 (32-bit windows. ). The code is as follows: require(foreach) require(doSNOW) cl <- makeCluster(6, type='SOCK') registerDoSNOW(cl) bl2 <- foreach(i=icount(length(unqmrno))) %dopar% { (some code here) } stopCluster(cl) When I run the same code in Windows R-2.11 (either 32-bit or 64-bit), R hangs at cl<-makeCluster(6, type='SOCK') and no R processes are spawned. I was wondering if others have encountered thi...

Dear list, When I run an R CMD check --as-cran on my package (pROC) I get the following note: > Uses the superseded package: ?doSNOW? The fact that it uses the doSNOW package is correct as I have the following example in an .Rd file: > #ifdef windows > if (require(doSNOW)) { > registerDoSNOW(cl <- makeCluster(2, type = "SOCK")) > ci(roc2, method="bootstrap", parallel=TRUE) > \dontrun{ci(roc2, method="bootstrap", parallel=TRUE)} > \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} > stopCluster(cl) &gt...

...I start the job from the command line via R CMD BATCH. What's going on? Is this a known issue? Cheers, Marius library(doSNOW) library(Rmpi) library(rlecuyer) library(foreach) cl <- makeCluster(mpi.universe.size(), type ="MPI") tmp <- clusterSetupRNG(cl, seed=rep(1,6)) registerDoSNOW(cl) counter <- 0 res <- foreach(k = 1:1000) %do% { counter <- counter + 1 } tmp <- stopCluster(cl) unlist(res)

...the first time. I can see all my processor running at full performance when I use a smaller dataset require(snow) require(doSNOW) require(foreach) #change the 8 to however many cores\phys processors you have on your machine cl.tmp = makeCluster(rep("localhost",4), type="SOCK") registerDoSNOW(cl.tmp) optData.df <- head(pristine,100000) system.time( optData.df$impliedVol <- foreach(i=1:NROW(optData.df),.packages="RQuantLib") %dopar% with(optData.df[i,], tryCatch({EuropeanOptionImpliedVolatility(type,value,underlying, strike, dividendYield, riskFreeRate, maturity, vola...

...now) library(doSNOW) testfun=function(){ options(digits.secs=6) set.seed(slave.seed <- substring(Sys.time(), 21, 26)) a <- rnorm(5,0,1) return(a) } maxkk <- 5 cl <- makeCluster(5, type = "SOCK") registerDoSNOW(cl) results=foreach (i = 1:maxkk, .combine = 'rbind') %dopar% testfun() results stopCluster(cl) ################# 2. Suppose I want to use *snow*, according to some reference, I need to use *rsprng* as a generator. Does this generator relay on seed as well? But it seems to me that if I do...

...ectCores() # [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") registerDoSNOW(cl) system.time(foreach(i=1:64) %dopar% Sys.sleep(0.5)) # user system elapsed # 0.06 0.00 0.64 system.time(foreach(i=1:65) %dopar% Sys.sleep(0.5)) # user system elapsed # 0.03 0.01 1.04 stopCluster(cl) With a cluster of 64 processors and loop running with 64 iterations, it...

...mal 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.seed does not exist - in l'Ecuyer setting") .t <- snow::clusterSetupRNG(cl, seed=.Random.seed[2:7]) # =>...

...package (pROC) I get the >> following note: >>> Uses the superseded package: ?doSNOW? >> The fact that it uses the doSNOW package is correct as I have the >> following example in an .Rd file: >>> #ifdef windows >>> if (require(doSNOW)) { >>> registerDoSNOW(cl <- makeCluster(2, type = "SOCK")) >>> ci(roc2, method="bootstrap", parallel=TRUE) >>> \dontrun{ci(roc2, method="bootstrap", parallel=TRUE)} >>> \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} >...

...gt; 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") > registerDoSNOW(cl) > system.time(foreach(i=1:64) %dopar% Sys.sleep(0.5)) > # user system elapsed > # 0.06 0.00 0.64 > system.time(foreach(i=1:65) %dopar% Sys.sleep(0.5)) > # user system elapsed > # 0.03 0.01 1.04 > stopCluster(cl) > > With a cluster of 64 processors...

...D check --as-cran on my package (pROC) I get the > following note: > > Uses the superseded package: ?doSNOW? > The fact that it uses the doSNOW package is correct as I have the > following example in an .Rd file: > > #ifdef windows > > if (require(doSNOW)) { > > registerDoSNOW(cl <- makeCluster(2, type = "SOCK")) > > ci(roc2, method="bootstrap", parallel=TRUE) > > \dontrun{ci(roc2, method="bootstrap", parallel=TRUE)} > > \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} > >...

...he directory containing rtest.R it is run from the command line with: > "mpirun -n --hostfile /home/hostfile --no-save -f rtest.R" > > Here is the code for rtest.R: > > ################# > library(doSNOW) > library(panel) > > cl<-makeMPIcluster(3) > registerDoSNOW(cl) > > clusterEvalQ(cl,library(panel)) > > res<-clusterCall(cl, function(){Sys.info()["nodename"]}) > print(do.call(rbind,res)) > > sme<- matrix(rnorm(100),10,10) > clusterExport(cl,"sme") > > myfun<-function() > { > for(i in...

...n bigkmeans >ans <- bigkmeans(data,200,nstart=5,iter.max=20) I see only one R process in system monitor, and only one CPU usage is high. I guess it's not really parallel. I also tried DoSNOW, though it's used for multi clusters. >cl <- makeCluster(8,type="SOCK") >registerDoSNOW(cl) >ans <- bigkmeans(data,200,nstart = 30) There are 8 R processes but only 1 running. Is it because I have something misconfigured? Or is the bigkmeans do not support parallel? Thanks in advance to any advise. Regards, Lishu -- View this message in context: http://r.789695.n4.nabble...

Hi, I want to impute a data set multiple times with Amelia, but the data set is large so it takes a long time. As a result, I'm trying to run the multiple imputation with parallel processors in Windows, but am having trouble. Here is a quick example: ###### library(foreach) library(doSNOW) registerDoSNOW(makeCluster(4, type = "SOCK")) getDoParWorkers() getDoParName() getDoParVersion() data(africa) iterations <- 4 foreach(i = 1:iterations) %dopar% { amelia(x = africa, m=1, cs = "country", ts = "year", logs = "gdp_pc") } ###### When I run the foreach lo...

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"