952 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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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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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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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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Coarse Grain Parallelism with foreach and rxExec

April 2, 2015
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by Joseph Rickert I have written a several posts about the Parallel External Memory Algorithms (PEMAs) in Revolution Analytics’ RevoScaleR package, most recently about rxBTrees(), but I haven’t said much about rxExec(). rxExec() is not itself a PEMA, but it can be used to write parallel algorithms. Pre-built PEMAs such as rxBTrees(), rxLinMod(), etc are inherently parallel algorithms designed...

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Seeing the Forest and the Trees – a parallel machine learning example

April 1, 2015
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Seeing the Forest and the Trees – a parallel machine learning example

Parallelizing Random Forests in R with BatchJobs and OpenLava By: Gord Sissons and Feng Li In his series of blogs about machine learning, Trevor Stephens focuses on a survival model from the Titanic disaster and provides a tutorial explaining how decision trees tend to over-fit models yielding anomalous predictions. How do we build a better

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Parallel R with BatchJobs

March 28, 2015
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Parallel R with BatchJobs

Parallelizing R with BatchJobs – An example using k-means Gord Sissons, Feng Li Many simulations in R are long running. Analysis of statistical algorithms can generate workloads that run for hours if not days tying up a single computer. Given the amount of time R programmers can spend waiting for results, getting acquainted parallelism makes

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