similar to: parallel computing package on a multicore windows workstation

Hello dear R community. I just started playing with the snowfall package (a wrapper for the snow package), and found it very convenient. (See also this great website: http://www.imbi.uni-freiburg.de/parallel/ ) I was wondering if it is possible to connect snowfall with the foreach package (since it has some connection to snow). My final goal is to do some simple parallel simulations on my two

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

People of R(th), I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to parallelize a bootstrap of a linear model on my 8-core mac. Below is the process that I want to parallelize (namely, the m2.ph.rlm.boot<-boot(m2.ph,m2.ph.fun, R = nboot) command). This is an extension of the bootstrapping linear models example in Venables and Ripley to

Dear R users, we have a problem when building R packages which depend on platform specific packages. The following example will illustrate our problem: For parallel computing (in our own package) we want to use the multicore package. Since multicore is not available for Windows we subtitute it by the snowfall package. Currently we create two packages with different "Depends" argument

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

Dear List, the advent of multicore machines in the consumer segment makes me wonder whether it would, at least in principle, be possible to divide a computational task into more slave R processes running on the different cores of the same processor, more or less in the way package SNOW would do on a cluster. I am thinking of simple 'embarassingly parallel' problems, just like inverting

?I want to take advantage of my multicore CPU to speed up a loop in a simulation program.? I didn?t write the code, but the iterations appear independent to me, at least in the sense that the results of one loop do not depend on previous ones.? Right now I?m relegated to a Windows box that runs Windows 7.? These appear to be the options: ? Pnmath-appears to parallelize non-BLAS routine but

Dear All, I have a question of how to export S4 class specification to clusters/workers in parallel computing. The package I used is snowfall. The problem is reproducible as follows. Any hint is greatly appreciated. Edwin Sun === begin =========== library(snowfall) sfInit(parallel = TRUE, cpus = 2) setClass("catt", representation(aa = "numeric")) setClass("dogg",

Hi, who of you in this forum uses R (http://www.r-project.org/) with the multicore, SNOW or CUDA packages, so for advanced calculations that need more power than a workstation CPU? On which hardware do you compute these scripts? At home/ at work or do you have data center access somewhere? The background of these questions is the following: I am currently writing my M.Sc. thesis about R and

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 architectures as

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 architectures as

Dear R users, at March 15. & 16. there will be an R course for 'Parallel Computing with R' in Munich at the super computer center. The course is part of the Munich R Course series and gives an introduction to parallel computing with R. Especially the R packages snow, snowfall, multicore, nws and Rmpi will be used. The course is a hands-on tutorial with a lot of different

Hi all, I would like to parallelize some R code and would like to use the 'foreach' package with a foreach loop. However, whenever I call a function from an enabled package outside of MASS, I get an error message that a number of the functions aren't recognized (even though the functions should be defined). For example: library(foreach) library(doSMP) library(survival) # Create the

  Hi,   The code below does exactly what I want in sequential mode. But, it is slow and I want to run it in parallel mode. I examined some windows version packages (parallel, snow, snowfall,..) but could not solve my specific problem. As far as I understood, either I have to write a new function like sfRollapplyr or I have to change my code in a way that it utilizes lapply, or sapply instead of

Hi everyone, Thanks for the help in advance. I just want to know if there is absolutely any way of using multicore if my loop can't run in parallel as each iteration depends on the previous iteration. I have Windows R 2.9.2 and REvolution R Enterprise 2.0 for Windows 64 bit. I also have if necessary R on Ubuntu. 8gb of ram and a core 2 duo processor but I could use a quad core as well. Many

I am using the package Multicore/Parallel to do importance sampling. I have 5 cores on my computer. And I have let's say 10 000 particles to generate. What I did was to send 5 particles in each time, calling the package parallel. Which means in all I am calling the parallel command 2000 times. What happens is in the end somewhere along the way I end up with the error message error in fork

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.

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]]

Hola a tod en s, estoy realizando unas operaciones con muchos datos con el paquete "vegan", concretamente la función "metaMDS" y me gustaría saber si puedo usar más de un procesador a la vez para acelerar los cálculos. He visto el paquete "multicore" pero no sé usarlo, ¿alguien lo ha usado para algo así? Gracias y saludos, Javier -- http://oikos.inf.um.es

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 previously found. My plan