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R: Difference between revisions
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R is a system for statistical computation and graphics. It consists of a language plus a | R is a system for statistical computation and graphics. It consists of a language plus a runtime environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files. | ||
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User Julie Fortin has written a blog post, [https://medium.com/the-nature-of-food/how-to-run-your-r-script-with-compute-canada-c325c0ab2973 "How to run your R script with Compute Canada"] which you might find useful. | |||
== The R interpreter == <!--T:3--> | == The R interpreter == <!--T:3--> | ||
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A simple | A simple job script looks like this: | ||
</translate> | </translate> | ||
{{File | {{File | ||
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==== Installing for one or many R versions ==== <!--T:79--> | ==== Installing for one or many R versions ==== <!--T:79--> | ||
Specify the local installation directory according to | Specify the local installation directory according to the R module that is currently loaded. | ||
{{Commands | {{Commands | ||
|mkdir -p ~/.local/R/$EBVERSIONR/ | |mkdir -p ~/.local/R/$EBVERSIONR/ | ||
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Using the R command <tt>system()</tt> you can execute commands in the ambient environment from inside R. On | Using the R command <tt>system()</tt> you can execute commands in the ambient environment from inside R. On our clusters, this can lead to problems because R will give an incorrect value to the environment variable <tt>LD_LIBRARY_PATH</tt>. You can avoid this problem by using the syntax <tt>system("LD_LIBRARY_PATH=$RSNT_LD_LIBRARY_PATH <my system call>")</tt> in your R system calls. | ||
== Passing arguments to R scripts == <!--T:84--> | == Passing arguments to R scripts == <!--T:84--> | ||
Sometimes it can be useful to pass parameters as arguments to R scripts, to avoid having to either change the R script for every job or having to manage multiple copies of otherwise identical scripts. This can be useful to specify the names for input | Sometimes it can be useful to pass parameters as arguments to R scripts, to avoid having to either change the R script for every job or having to manage multiple copies of otherwise identical scripts. This can be useful to specify the names for input or output files, or maybe numerical parameters. | ||
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The processors on | The processors on our clusters are quite ordinary. | ||
What makes these supercomputers ''super'' is that you have access to thousands of CPU cores with a high-performance network. | What makes these supercomputers ''super'' is that you have access to thousands of CPU cores with a high-performance network. | ||
In order to take advantage of this hardware you must run code "in parallel" | In order to take advantage of this hardware you must run code "in parallel." Note however that prior to investing a lot of time and effort | ||
in parallelizing your R code, you should first ensure that your serial implementation is as efficient as possible. As an interpreted | in parallelizing your R code, you should first ensure that your serial implementation is as efficient as possible. As an interpreted | ||
language, the use of loops in R, and especially nested loops, constitutes a significant performance bottleneck. Whenever possible you | language, the use of loops in R, and especially nested loops, constitutes a significant performance bottleneck. Whenever possible you | ||
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The [https://cran.r-project.org/web/views/HighPerformanceComputing.html CRAN Task View on High-Performance and Parallel Computing with R] | The [https://cran.r-project.org/web/views/HighPerformanceComputing.html CRAN Task View on High-Performance and Parallel Computing with R] | ||
describes a bewildering collection of | describes a bewildering collection of interrelated R packages for parallel computing. | ||
In the following subsections we present two methods of parallelizing an R code, both of which | In the following subsections, we present two methods of parallelizing an R code, both of which | ||
are supported on | are supported on our clusters. | ||
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'''A note on terminology:''' In most | '''A note on terminology:''' In most of our documentation the term 'node' refers | ||
to an individual machine, also called a 'host', and a collection of such nodes makes up a 'cluster'. | to an individual machine, also called a 'host', and a collection of such nodes makes up a 'cluster'. | ||
In a lot of R documentation however, the term 'node' refers to a worker process and a 'cluster' is a | In a lot of R documentation however, the term 'node' refers to a worker process and a 'cluster' is a | ||