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some hints for the search engines. I just did install.packages("foreach") install.packages("doMC") library(doMC) registerDoMC() library(foreach) > foreach(i = 1:3) %dopar% sqrt(i) The process has forked and you cannot use this CoreFoundation functionality safely. You MUST exec(). Break on __THE_PROCESS_HAS_FORKED_AND_YOU_CANNOT_USE_THIS_COREFOUNDATION_FUNCTIONALITY___YOU_MUST_EXEC__() to debug. The process has forke...

...tching my CPUs I see that in both cases only a single core is used, and they take about the same amount of time. Is there a limitation with how ddply() dispatches parallel jobs, or is this task not suitable for parallel computing? Cheers, Dylan Here is an example: library(plyr) library(doMC) registerDoMC(cores=2) # example data d <- data.frame(y=rnorm(1000), id=rep(letters[1:4], each=500)) # function that wastes some time f <- function(x) { m <- vector(length=10000) for(i in 1:10000) { m[i] <- mean(sample(x$y, 100)) } mean(m) } system.time(ddply(d, .(id), .fun=f, .parallel=FALSE))...

...s are not getting into the Valuation.R script then #A = MCPVMPA = 0 and B = MCMKMPA = 0 So VarX = NaN Then it does something like: if (VarZ>VarX) VarY = 0 else VarY = VarX-VarZ #This line generates the error I have all the libraries installed: library(foreach) library(doMC) registerDoMC() It's not the installation because all the example code with %dopar% I've tested work fine. So can you help me please to make my code work? Any ideas? -- L.E. Marcos Larios Sata Rosa [[alternative HTML version deleted]]

...+x^3+i/A+randvalues[i] } ?## an arbitrary function ARGV <- commandArgs(trailingOnly=TRUE) if (ARGV[1] == "do-onecore") { ?library(foreach) ?discard <- foreach(i = 1:A) %do% uniroot( minfn, c(1e-20,9e20), i ) } else if (ARGV[1] == "do-multicore") { ?library(doMC) ?registerDoMC() ?cat("You have", getDoParWorkers(), "cores\n") ?discard <- foreach(i = 1:A) %dopar% uniroot( minfn, c(1e-20,9e20), i ) } else if (ARGV[1] == "plain") ?for (i in 1:A) discard <- uniroot( minfn, c(1e-20,9e20), i ) else cat("sorry, but argument", A...

Hi, all, I have a really big matrix that I want to run k-means on. I tried: >data <- read.big.memory('mydata.csv',type='double',backingfile='mydata.bin',descriptorfile='mydata.desc') I'm using doMC to register multicore. >library(doMC) >registerDoMC(cores=8) >ans<-bigkmeans(data,k) In system monitor, it seems only one thread running R. Is there anything I did wrong? Thanks in advance for any suggestions. Best, Lishu [[alternative HTML version deleted]]

...method="bootstrap", parallel=TRUE) > \dontrun{ci(roc2, method="bootstrap", parallel=TRUE)} > \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} > stopCluster(cl) > } > #endif > #ifdef unix > if (require(doMC)) { > registerDoMC(2) > \dontrun{ci(roc2, method="bootstrap", parallel=TRUE)} > \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} > } > #endif The "superseded" part is more confusing to me, though. The doSNOW package seems to be still available on CRA...

Hello, I am trying to run a small example with foreach, but I am having some problems. Here is the code: *library(doMC) registerDoMC() zappa = list() frank = list() foreach (i = 1:4) %dopar% { zappa[[i]] = kmeans (iris[-5],4) frank[[i]] = warnings() }* The code runs without error. However the zappa and frank will be empty lists. If I use regular *for *instead, the list will be filled up with kmeans and warnings results. How...

...'m processing sequencing data trying to collapsing the locations of each unique sequence and write the results to a file (as storing that in a table will require 10GB mem at least) so I wrote a function that, given a sequence id, provide the needed line to be stored library(doMC) # load library registerDoMC(12) # assign the Number of CPU fileConn<-file(paste(fq_file,"_SeqID.txt",sep=""),open = "at") # open connection writeLines(paste("ReadID","Freq","Seq","LOC_UG","Nb_UG_Seq",sep="\t"), fileConn) # write hea...

...dontrun{ci(roc2, method="bootstrap", parallel=TRUE)} >>> \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} >>> stopCluster(cl) >>> } >>> #endif >>> #ifdef unix >>> if (require(doMC)) { >>> registerDoMC(2) >>> \dontrun{ci(roc2, method="bootstrap", parallel=TRUE)} >>> \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} >>> } >>> #endif >> The "superseded" part is more confusing to me, though. The doSNOW &g...

...tion. I have a very large dataset and so I'm hoping to make use of a parallel backend to speed up the processing time. I'm having trouble getting selecting three variables in the dataset to use in the foreach() loops. My for() loop code is: library(foreach) library(multicore) library(doMC) registerDoMC() > str(data) 'data.frame': 958 obs. of 13 variables: $ Date.Time: Factor w/ 260 levels "03/07/09 00:00",..: 1 2 2 2 3 3 3 3 3 3 ... $ ID : int 3 1 3 7 1 3 7 8 10 12 ... $ X : num 151 151 151 151 151 ... $ Y : num -33.9 -33.9 -33.9 -33.9 -33.9 ......

...?????22.3 90342??? 1980-07-14????????? 19.3??????????28.4 EXAMPLE SCRIPT FOR MODEL FITTING fitControl <- trainControl(method = "repeatedcv", number=10, repeats=3) tuning <- read.table("temptunegrid.txt",head=T,sep=",") tuning # # Model with 100 iterations registerDoMC(4) tempmod100its <- train(watmntemp~tempa + tempb + tempc + tempd + tempe + netarea + netbuffor + strmslope + netsoilprm + netslope + gwndx + mnaspect + urb + ag + forest + buffor + tempa7day + tempb7day + tempc7day + tempd7day + tempe7day + tempa30day + tempb30day + tempc30day + tempd30day...

...TRUE) > > \dontrun{ci(roc2, method="bootstrap", parallel=TRUE)} > > \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} > > stopCluster(cl) > > } > > #endif > > #ifdef unix > > if (require(doMC)) { > > registerDoMC(2) > > \dontrun{ci(roc2, method="bootstrap", parallel=TRUE)} > > \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} > > } > > #endif > > The "superseded" part is more confusing to me, though. The doSNOW > package...

Dear all, I have a question regarding the possibility of parallel computation in plyr version 1.7. The help files of the following functions mention the argument '.parallel': ddply, aaply, llply, daply, adply, dlply, alply, ldply, laply However, the help files of the following functions do not mention this argument: ?d_ply, ?aply, ?lply Is it because parallel computation is not

I'm using bigkmeans in 'biganalytics' to cluster my 60,000 by 600,000 matrix. I'm using a 8 core Linux VM. I have register parallel backend with >registerDoMC() And I checked how many cores registered with >getDoParWorkers() It returns 8, which is the number of cores I have on my machine. And I run the test below, whose results shows improved speed due to parallel. check <-function(n) { + for(i in 1:1000) + { + sme <- matrix(rnorm(100), 10,10...

Hello, I'm running some code that requires untaring many files in the first step. This takes a lot of time and I'd like to do this in parallel, if possible. If it's the disk reading speed that is the bottleneck I guess I should not expect an improvement, but perhaps it's the processor. So I want to try this out. I'm working on windows 7 with R 2.15.1 and the latest foreach

...32 cores. I'm using doMC instead of snow, because the guy at the supercomputer is in the process of getting Rmpi running. For now, 32 cores should be (more than) adequate for me. My script is of the sort: |define a bunch of functions load the data call libraries require(doMC) require(plyr) registerDoMC(32) main.function <- function(data){ *thefunction* } results= llply(1:500, function(idx){out<-main.function(data)},.parallel=TRUE) save(results,file="trestles_results")| This runs fine on my own machine (setting it to run only a few times and registering only a co...

...in a .parallel argument that when TRUE, applies functions in parallel using a parallel backend registered with the foreach package: x <- seq_len(20) wait <- function(i) Sys.sleep(0.1) system.time(llply(x, wait)) # user system elapsed # 0.007 0.005 2.005 library(doMC) registerDoMC(2) system.time(llply(x, wait, .parallel = TRUE)) # user system elapsed # 0.020 0.011 1.038 This work has been generously supported by BD (Becton Dickinson). MINOR CHANGES * a*ply and m*ply gain an .expand argument that controls whether data frames produce a single output dimensio...

...in a .parallel argument that when TRUE, applies functions in parallel using a parallel backend registered with the foreach package: x <- seq_len(20) wait <- function(i) Sys.sleep(0.1) system.time(llply(x, wait)) # user system elapsed # 0.007 0.005 2.005 library(doMC) registerDoMC(2) system.time(llply(x, wait, .parallel = TRUE)) # user system elapsed # 0.020 0.011 1.038 This work has been generously supported by BD (Becton Dickinson). MINOR CHANGES * a*ply and m*ply gain an .expand argument that controls whether data frames produce a single output dimensio...

...cat("Processing chunk", chunk, "... ") idd <- as.matrix(subset(ss,select=1:2)) newvec <- as.vector(as.matrix(subset(ss,select=3))) ans[idd] <- ans[idd] + newvec cat("OK\n") } ans } require(foreach) require(doMC) registerDoMC(cores=2) num <- 8891 nr <- 500000000L #500 million rows at a time MMM <- foreach(IT = 1:2) %dopar% { require(data.table) if (IT==1){ x <- system.time({computeAllPairSums( paste(GERMLINE,"bc.chr22.q.20.file",sep=''),num,nr)}) } #Run it on regular file PID 6...

...lue }, mc.cores=4) ans3 <- matrix(unlist(res3),ncol=4) } s3 <- sol3() ### solution 4 using plyr::maply # difficulty finding equivalent code # benefit could be: no input transform, no output transform, parallelization, and progress update library(plyr) library(abind) library(doMC) registerDoMC(cores=4) sol4 <- function() { ans4 <- maply( #.data = abind(i=Ax,j=Ay,k=Bx,l=By,along=0), #.data = abind(Ax,Ay,Bx,By,along=3), #.data = data.frame(i=Ax, j=Ay, k=Bx, l=By), #.data = cbind(i=as.vector(Ax), j=as.vector(Ay), k=as.vector(Bx), l=as.vector(By)), #.data = list(i=Ax,...