search for: multicore

Hi all, I am new to this LLVM. I went through the documenation of LLVM but I didn't find any support for Multicore. Is there any such possibility where multicore architecture can be exploited using LLVM. Thanks Vijay -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20100803/3a075597/attachment.html>

Sir, I want to apply mclapply() function for my analysis. So, I have to install multicore package. But I can not install the package. >install.packages("multicore") It gives that package multicore is not available. Can you help me? Regards, Suman Dhara [[alternative HTML version deleted]]

Hi, I want to parallelize some computations when it's possible on multicore machines. Each computation produces a big objects that I don't want to store if not necessary: in the end only the object that best fits my data have to be returned. In non-parallel mode, a single gloabl object is updated if the current computation gets a better result than the best previou...

...2010/8/4 vijay kumar <vijaygbvv at gmail.com> > Yeah OpenMP support. I read that it has a front end support but not the > back end. So are there any projects or teams looking at this issue. > > On Wed, Aug 4, 2010 at 7:24 AM, Liu <proljc at gmail.com> wrote: > >> Multicore? >> You want OpenMP support? >> >> 2010/8/3 vijay kumar <vijaygbvv at gmail.com> >> >>> Hi all, >>> I am new to this LLVM. I went through the documenation of LLVM >>> but I didn't find any support for Multicore. Is there any such...

...it. > > 2010/8/4 vijay kumar <vijaygbvv at gmail.com> > Yeah OpenMP support. I read that it has a front end support but not the back end. So are there any projects or teams looking at this issue. > > On Wed, Aug 4, 2010 at 7:24 AM, Liu <proljc at gmail.com> wrote: > Multicore? > You want OpenMP support? > > 2010/8/3 vijay kumar <vijaygbvv at gmail.com> > Hi all, > I am new to this LLVM. I went through the documenation of LLVM but I didn't find any support for Multicore. Is there any such possibility where multicore architecture can be...

Is there any reason to expect a problem ? i'm running this script on the cluster down the hall: module load R/2.11.0 R library(multicore) fxx<-function(ll) runif(1) mclapply(1:10,fxx) i get: Error in fork() : Unable to fork. less /proc/cpuinfo yields: processor : 0 vendor_id : GenuineIntel cpu family : 6 model : 15 model name : Intel(R) Xeon(R) CPU X5365 @ 3.00GHz stepping :...

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architect...

dear R readers---I thought I would post the following snippet of R code that makes by() like operations easier and faster on multicore machines for R novices and amateurs. I hope it helps some. YMMV. feel free to ignore. PS: I wish R had a POD-like documentation system for end users that are not writing full libraries. because it does not, I did not provide documentation ala '?mc.by'. /iaw set.seed(0) library(multi...

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architect...

Hi, I have an R script which is time consuming because it has two nested loops in it of at least 5000 iterations each, I have tried to use the multicore package but id doesn't seem to improve the elapsed time of the script(a shorter script for example) and I can't use the mcapply because of technical reasons. I was wondering how can I make my script use more cores and memory because I am running it on a server and it is a shame that it use...

Hi, I have an R script which is time consuming because it has two nested loops in it of at least 5000 iterations each, I have tried to use the multicore package but id doesn't seem to improve the elapsed time of the script(a shorter script for example) and I can't use the mcapply because of technical reasons. I was wondering how can I make my script use more cores and memory because I am running it on a server and it is a shame that it use...

On a Unix machine I ran caret::rfe using the multicore package, and I saved the resulting object using save(lm2, file = "lm2.RData"). [Reproducible example below.] When I try to load("lm2.RData") on my Windows laptop, I get Error in loadNamespace(name) : there is no package called 'multicore' I completely understand th...

Multicore? You want OpenMP support? 2010/8/3 vijay kumar <vijaygbvv at gmail.com> > Hi all, > I am new to this LLVM. I went through the documenation of LLVM but > I didn't find any support for Multicore. Is there any such possibility where > multicore architecture can be expl...

This is a 'rather than re-invent the wheel' post. Has anyone out there re-written combn so that it can be parallelized - with multicore, snow, or otherwise? I have a job that requires large numbers of combinations, and rather than get all of the index values, then crank it through mclapply, I was wondering if there was a way to just do this natively within a function. Just curious. Thanks! -Jarrett -- View this message in cont...

dear r experts---Is there a multicore equivalent of by(), just like mclapply() is the multicore equivalent of lapply()? if not, is there a fast way to convert a data.table into a list based on a column that lapply and mclapply can consume? advice appreciated...as always. regards, /iaw ---- Ivo Welch (ivo.welch at gmail.com)

dear R experts--- I am experimenting with multicore processing, so far with pretty disappointing results. Here is my simple example: A <- 100000 randvalues <- abs(rnorm(A)) minfn <- function( x, i ) { log(abs(x))+x^3+i/A+randvalues[i] } ?## an arbitrary function ARGV <- commandArgs(trailingOnly=TRUE) if (ARGV[1] == "do-onecore&q...

...orm of structured (multiple) logs. I access the data by using all.files(). Since by default basic version of R utilizes single core, the processing of my analysis code is taking too much time. I got to know that mclapply() can be used to use all cores (processors) to make R much faster when we have multicores. Can anyone help me in understanding how to use mclapply() function in the above situation. Thanks in advance Regards, Madana -- View this message in context: http://r.789695.n4.nabble.com/R-on-Multicore-for-Linux-tp3682318p3682318.html Sent from the R help mailing list archive at Nabble.com.

Hi, i have a function: zz<- (constrOptim(c(.5,0), fr, grr, ui=rbind(c(-1,0),c(1,-1)), ci=c(-0.9,0.1))) i can get the result by using command (for example): zz$par now if i can use multicore: zz<-parallel(constrOptim(c(.5,0), fr, grr, ui=rbind(c(-1,0),c(1,-1)), ci=c(-0.9,0.1))) result < collect(zz) i cant get my the result: result$par because multicore add process id. for example: $`2493`$par [1] 0.8891335 0.7891335 i try to get the result by: result$`processID(zz)`$par but it...

Hi, in R 2.12.1, R CMD check hangs when building a vignette that uses a foreach loop with the doMC parallel backend. This does not happen in R 2.13.1, nor if I use doSEQ instead of doMC. All versions of multicore, doMC and foreach are the same on both my R installations. Has anybody encountered a similar issue? Thank you. Renaud ### UNIVERSITY OF CAPE TOWN This e-mail is subject to the UCT ICT policies and e-mai...{{dropped:5}}

Hello all, I upgraded my R workstation, and to my dismay, only one core appears to be used during intensive computation of a bioconductor function. What I have now is two dual-core Xeon 5160 CPUs and 10 GB RAM. When I fully load it, top reports about 25% user, 75% idle and 0.98 short-term load. The archives gave nothing helpful besides mention of snow. I thought of posting to HPC, but this system