726 search results for "parallel"

HPC news from the useR2011 conference

August 18, 2011
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It was an exciting useR2011 conference at the University of Warwick, Coventry, UK. Thanks a lot to the local organizing and program committee for having this great conference. I enjoyed the variety of talks, the poster session and the conference dinner and everything within walking distance. In view of HPC for R I learned: The

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The R-Files: Martyn Plummer

August 17, 2011
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The R-Files: Martyn Plummer

"The R-Files" is an occasional series from Revolution Analytics, where we profile prominent members of the R Community. Name: Martyn Plummer Occupation: Statistician at International Agency for Research on Cancer Nationality: British Years Using R: 16 Known for: Member of R core group; member of R Journal editorial board Martyn Plummer is a longtime contributor to the R community...

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Lee E. Edlefsen – Scalable Data Analysis in R (useR! 2011)

August 17, 2011
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Lee E. Edlefsen – Scalable Data Analysis in R (useR! 2011)

The RevoScaleR package isn’t open source, but it is free for academic users. Collect and storing data has outpaced our ability to analyze it. Can R cope with this challenge? The RevoScaleR package is part of the revolution R Enterprise. This package provides data management and data analysis. Uses multiple cores and should scale. Scalability

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R Code Optimization

August 16, 2011
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R Code Optimization

Handling Large Data with R The following experiments are inspired from this excellent presentation by Ryan Rosario: http://statistics.org.il/wp-content/uploads/2010/04/Big_Memory%20V0.pdf. R presents many I/O functions to the users for reading/writing data such as ‘read.table’ , ‘write.table’ -> http://cran.r-project.org/doc/manuals/R-intro.html#Reading-data-from-files. With data growing larger by the day many new methodologies are available in order to achieve faster I/O operations.

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Brian Ripley on The R Development Process

August 16, 2011
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R Core member Professor Brian Ripley from Oxford University gave the first keynote presentation of useR! 2011 today, and gave some insights into what goes on behind the scenes to create two updates to R (plus several patches) every year. He began with some facts about the history of R (noting that if they'd known R would take off...

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High Performance Computing

August 16, 2011
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High Performance Computing

Wilem Ligtenberg – GPU computing and R Why GPU computing – theoretical GFLOPs for a GPU is three times greater than a CPU. Use GPUs for same instruction multiple data problems (SIMD). Initially GPUs were developed for texture problems. For example, a wall smashed into lots of pieces. Each core handled a single piece. CUDA

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Brian Ripley – The R Development Process (useR! 2011)

August 16, 2011
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Brian Ripley – The R Development Process (useR! 2011)

There are my notes on the User2011 invited talk. Brian Ripley has been a member of R core since 1998 The R Development Process – A insideR’s view R Timeline: JCGS paper submitted in 1995. 1997: CRAN(Mar), Core team(Aug), CVS (Sept) R 1.0.0 Feb 2000 – 2.8MB. Many people don’t take 0.X.X seriously R 2.0.0 Oct

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High-Performance in Cloud Computing

August 11, 2011
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High-Performance in Cloud Computing

Very often scientists are worried about performance and security in cloud computing. Especially, when talking about High-Performance Computing (HPC) in the cloud it is a very important aspect to proof efficient calculation speed in the cloud. Cloud computing describes a new delivery model for IT services based on Internet protocols, and it typically involves provisioning

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Using OpenMP-ized C code with R

August 11, 2011
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What is OpenMP? Basically a standard compiler extension allowing one to easily distribute calculations over multiple processors in a shared-memory manner (this is especially important when dealing with large data — simple separate-process approach usually requires as many copies of the working data as there are threads, and this may easily be an overkill even

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Multiple cores in R, revisited

August 10, 2011
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The bigmemory package in combination with doMC provides at least a partial solution for sharing a large data set across multiple cores in R. With this solution you can work on the same matrix using several threads. It is also a very scalable solution. ...

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