Well I bought a new computer a month back (i7 8GB memory). Finally more than one core and a chance to try parallelization. I saw this blog post a while back and was intrigued and was further intriqued when I … Continue reading →
Posts Tagged ‘ parallel computing ’
Parallelization: Speed up Functions in a Package
Online resources for handling big data and parallel computing in R
by Yanchang Zhao, RDataMining.com Compared with many other programming languages, such as C/C++ and Java, R is less efficient and consumes much more memory. Fortunately, there are some packages that enables parallel computing in R and also packages for processing … Continue reading →
CPU and GPU trends over time
GPUs seem to be all the rage these days. At the last Bayesian Valencia meeting, Chris Holmes gave a nice talk on how GPUs could be leveraged for statistical computing. Recently Christian Robert arXived a paper with parallel computing firmly in mind. In two weeks time I’m giving an internal seminar on using GPUs for 
Quick and dirty parallel processing in R
R has some powerful tools for parallel processing, which I discovered while searching for ways to fully utilize my 8-core computer at work. What surprised me is how easy it is…about 6 lines of code, if that. Given that I wasn’t allowed to install heavy duty parallel-processing systems like MPICH on the computer, I found 
Scheduled Parallel Computing with R: R + Rmpi + OpenMPI + Sun Grid Engine (SGE)
Recently I’ve learned how to do parallel computing in R on a cluster of machines thanks to the R packages snowfall, snow, and Rmpi. I’ve been using the SOCKET method with snowfall since together they make things simple. With these tools, I can reduce day/week long jobs to hours or a day across many (100)
Parallel computing in R: snowfall/snow
I finally have time to try parallel computing in R using snowfall/snow thanks to this article in the 1st issue of R journal, which replaces R news. I didn’t try it before because i didn’t have a good toy example, and it seemed like a steep learning curve (i only guessed what parallel computing was).
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
