Together with other members of Andreas Beyer's research group, I participated in the DREAM 8 toxicogenetics challenge. While the jury is still out on the results, I want to introduce my improvement of the R randomForest package, namely parall...

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Last week I attended a workshop on how to run highly parallel distributed jobs on the Open Science Grid (osg). There I met Derek Weitzel who has made an excellent contribution to advancing R as a high performance computing language by developing BoscoR. BoscoR greatly facilitates the use of the already existing package “GridR” by The post Easy...

Exegetic Analytics extols the wonders of foreach package for iterative operations that go beyond the standard "for" loop in R. For example, here's a neat (if not optimally efficient) construct using filters to calculate the primes less than 100: foreach(n = 1:100, .combine = c) %:% when (isPrime(n)) %do% n The open-source team at Revolution Analytics created the foreach...

Multicore (parallel) processing in R from Wallace Campbell on Vimeo. If you're not programming in parallel, you're only using a fraction of your computer's power! I demonstrate how to run "for" loops in parallel using the mclapply function from the multicore library. The code can be scaled to any number of available cores.

To my surprise and disappointment, popular scientific libraries like Boost or GSL provide no native support for parallel random number generation. Recently I came across TRNG, an excellent random number generation library for C++ built specifically with parallel architectures in mind. Over the last few days I’ve been trawling internet forums and reading discussions about The post Parallel...

One innovative feature of pqR (my new, faster, version of R), is that it can perform some numeric computations in “helper” threads, in parallel with other such numeric computations, and with interpretive operations performed in the “master” thread. This can potentially speed up your computations by a factor as large as the number of processor cores

by Joseph Rickert Random number generation is fundamental to doing computational statistics. As you might expect, R is very rich in random number resources. The R base code provides several high quality random number generators including: Wichmann-Hill, Marsaglia-Multicarry, Super-Duper, Mersenne-Twister, Knuth-TAOCP-2002 and L’Ecuyer-CMRG. (See Random for details.) And, there are at least three packages, rspring, rlecuyer, and rstream for...

In my previous post (http://statcompute.wordpress.com/2013/05/25/test-drive-of-parallel-computing-with-r) on 05/25/2013, I’ve demonstrated the power of parallel computing with various R packages. However, in the real world, it is not straight-forward to utilize these powerful tools in our day-by-day computing tasks without carefully formulate the problem. In the example below, I am going to show how to use the

Yesterday, Daniel Marcelino published an interesting post on his blog, untitled Parallel Processing: When does it worth ? I was asking myself the same question for a chapter I am currently writing. And I did like his approach, so I tried, on my computer to do the same. I did use three packages to run parallel R codes, >...