Difference between revisions of "R/Parallel"

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It is possible to run parallel jobs in R, by using the '''parallel''', the '''Rmpi''' or other packages for parallel computing. More information on parallel computing with R can be found at http://cran.r-project.org/web/views/HighPerformanceComputing.html. If you run parallel R jobs on Euler, please make sure that you do not forget to request enough cores from the batch system. Otherwise, the batch system will only reserve a single core and all threads will be executed on this single core, which is very inefficient and should be avoided.
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You can run parallel jobs in R, by using the '''parallel''', the '''Rmpi''' or other packages for parallel computing. Please find more information on parallel computing with R at http://cran.r-project.org/web/views/HighPerformanceComputing.html or in the R reference manual. If you run parallel R jobs on Euler, please make sure that you do not forget to request enough cores from the batch system. Otherwise, the batch system will only reserve a single core and all threads will be executed on this single core, which is very inefficient and should be avoided.

Latest revision as of 07:01, 10 August 2016

You can run parallel jobs in R, by using the parallel, the Rmpi or other packages for parallel computing. Please find more information on parallel computing with R at http://cran.r-project.org/web/views/HighPerformanceComputing.html or in the R reference manual. If you run parallel R jobs on Euler, please make sure that you do not forget to request enough cores from the batch system. Otherwise, the batch system will only reserve a single core and all threads will be executed on this single core, which is very inefficient and should be avoided.