similar to: R on Multicore for Linux

Hi all, I have multiple data frames created with equal number of columns in each data frame by using mclapply() on multicore processor. The data frames are like DF[[1]], DF[[2]], ... DF[[150]]. Now i want to bind (Similar like rbind()) all these data frames and create one single data frame called DF so that i can have the complete data for further analysis. Can someone help me in performing this

Hi all, I have the following sql query that I am executing on a machine with single core. I want to know how can I execute the same sqery on a maching that is running with 4 cores. Please provide me the code. NEW_TABLE <- rhive.query("SELECT A, B, COUNT(C) FROM TABLE_A WHERE A>='01-01-2012'") Also let me know how can I leverage only 2 / 3 cores of the machine.

Hi All, I want to join a table (Dataset) that is created in R with a table that is in oracle database. Can some one help me in accomplishing this task in R? Example Code: library(RODBC) DB_CONNECT <- odbcConnect("DSN_NAME") TABLE_JOIN <- sqlQuery(DB_CONNECT, "SELECT * FROM DB_TABLE WHERE COL_1 NOT IN (SELECT COL_1 FROM DATA_SET_R) ") Regards, Madana -- View this

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)

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]]

The attached code produces corrupted pdfs (test2.pdf, test4.pdf and test5.pdf). The resulting pdf depends on how many cores are available on the machine. I don't see why there should be any difference between the pdfs (exept for the timestamp). Doing many operations involving mclapply can increase the size of the resulting pdf by ten times! Thank you for checking this. require(multicore)

Hi i have the data in the following format: rent,100,1,common,674 pipe,200,0,usual,864 car,300,1,uncommon,392:jump,700,0,common,664 car,200,1,uncommon,864:snap,900,1,usual,746 stint,600,1,uncommon,257 pull,800,0,usual,594 where as i want the above 6 lines data into 8 lines as below (Spliting row 3 & 4 at : and sending to a new row): rent,100,1,common,674 pipe,200,0,usual,864

I'm running r 2. on a mac running 10.6.4 and a dual-core macbook pro. I'm having a funny time with multicore. When I run it with 2 cores, mclapply, R borks with the following error. 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__()

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

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.

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

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:

Hello everyone, I'm trying to parallelize an R script I have written. To do this, I am first trying to use the multicore package, because I've had some previous success with that. The function I'm trying to parallelize is illumqc. I'd like to create a separate process for each of 8 files, contained in the vector "files". Below is my code: for(i in

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 the error and I would like to ignore it and

The following program is whittled down from a much larger program that always works on Intel, and always works on AMD's threadripper with lapply but not mclappy. With mclapply on AMD, all processes go into "suspend" mode and the program then hangs. This bug is replicable on an AMD Ryzen Threadripper 2950X 16-Core Processor (128GB RAM), running latest ubuntu 18.04. The R version

I have matrices as below: a <- matrix(c(1:10, 11, 12), 3,4) aa <- data.frame(a) b <- matrix(c(10:20, 21), 4,3) bb <- data.frame(b) ... and many more matrices. st = list(aa,bb, ..... ) mclapply(st, FUN, mc.cores=6); #this function apply the function to the elements of the list 'aa', 'bb'...etc FUN = function(st) { Is there a way/function to know the index of

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

Thanks Simon, I will take note of the sensible default for core usage. I?m trying to achieve small scale parallelism, where tasks take 1-5 seconds and make fuller use of consumer hardware. Its not a HPC-worthy computation but even laptops these days come with 4 cores and I don?t see a reason to not make use of it. The goal for the current piece of code I?m working on is to bootstrap many

When I execute mclapply it creates the needed processes, but these processes never begin computing anything, they just wait indefinitely. I recently upgraded to version 2.9.1, which might have caused the problem. -- Med venlig hilsen Rune Schjellerup Philosof Ph.d.-studerende, Statistik, IST, SDU Telefon: 6550 3607 E-mail: rphilosof at health.sdu.dk Adresse: J.B. Winsl?wsvej 9, 5000 Odense

Dear all, I have an R script that uses Rcpp, and I have been trying to parallelize it using mclapply (I tried with the multicore and the parallel library) Sometimes (not always, interestingly), the CPU use for each core drops, usually so that the total over all cores reaches 100%, i.e., as fast as if using just one single core fully. I tried my code directly from within emacs, and also using a