1088 search results for "parallel"

Parallel Computing for Data Science

July 8, 2015
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Hot off the press, Norman Matloff's book, Parallel Computing for Data Science: With Examples in R, C++ and CUDA  (Chapman and Hall/ CRC Press, 2015) should appeal to a lot of the readers of this blog.The book's coverage is clear from the following chapter titles:1. Introduction to Parallel Processing in R2. Performance Issues: General3. Principles of Parallel...

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R-devel in parallel to regular R installation

July 1, 2015
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R-devel in parallel to regular R installation

Unfortunately, you need both: R-devel (development version of R) if you want to submit your packages to CRAN, and regular R for your research (you don’t want the unstable release for that). Fortunately, installing R-devel in parallel is less trouble than one might think. Say, we want to install R-devel into a directory called ~/R-devel/,

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R-devel in parallel to regular R installation

July 1, 2015
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R-devel in parallel to regular R installation

Unfortunately, you need both: R-devel (development version of R) if you want to submit your packages to CRAN, and regular R for your research (you don’t want the unstable release for that). Fortunately, installing R-devel in parallel is less trouble than one might think. Say, we want to install R-devel into a directory called ~/R-devel/,

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Parallel and a new laptop

June 14, 2015
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Parallel and a new laptop

I am thinking about a new laptop. For one thing a 1366*768 resolution just seems to get impractically small. Secondly, faster comutations, more memory.Regarding CPU speed, my current laptop has a lowly Celeron 877. From what I see at my computers activ...

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Fast parallel computing with Intel Phi coprocessors

May 19, 2015
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Fast parallel computing with Intel Phi coprocessors

by Andrew Ekstrom Recovering physicist, applied mathematician and graduate student in applied Stats and systems engineering We know that R is a great system for performing statistical analysis. The price is quite nice too ;-) . As a graduate student, I need a cheap replacement for Matlab and/or Maple. Well, R can do that too. I’m running a large...

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rstanmulticore: A cross-platform R package to automatically run RStan MCMC chains in parallel

May 1, 2015
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*** This work has been supported by a grant from the Spencer Foundation (#201400002). The views expressed are those of the author and do not necessarily reflect those of the Spencer Foundation. *** It seems that the heir to WinBUGS is Stan. With Stan, reasonably complex Bayesian models can be expressed in a compact way

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rstanmulticore: A cross-platform R package to automatically run RStan MCMC chains in parallel

May 1, 2015
By

*** This work has been supported by a grant from the Spencer Foundation (#201400002). The views expressed are those of the author and do not necessarily reflect those of the Spencer Foundation. *** It seems that the heir to WinBUGS is Stan. With Stan, reasonably complex Bayesian models can be expressed in a compact way

Read more »

Parallel Simulation of Heckman Selection Model

April 22, 2015
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Parallel Simulation of Heckman Selection Model

Parallel Simulation of Heckman Selection Model One of the, if not the, fundamental problems in observational data analysis is the estimation of the value of the unobserved choice. If the (i^{text{th}}) unit chooses the value of (t) on the basis of some factors (mathbf{x_i}), which may include...

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Parallel Simulation of Heckman Selection Model

April 22, 2015
By
Parallel Simulation of Heckman Selection Model

Parallel Simulation of Heckman Selection Model One of the, if not the, fundamental problems in observational data analysis is the estimation of the value of the unobserved choice. If the (i^{text{th}}) unit chooses the value of (t) on the basis of some factors (mathbf{x_i}), which may include...

Read more »

Accelerating R with multi-node parallelism – Rmpi, BatchJobs and OpenLava

Accelerating R with multi-node parallelism –  Rmpi, BatchJobs and OpenLava

Gord Sissons, Feng Li In a previous blog we showed how we could use the R BatchJobs package with OpenLava to accelerate a single-threaded k-means calculation by breaking the workload into chunks and running  them as serial jobs. R users frequently need to find solutions to parallelize workloads, and while solutions like multicore and socket

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