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I have a question regarding the use of BLAS parallelized matrix product in R (being the default matrix product at least since R-3.4, maybe earlier).
The default behavior (at least on my machine) is now for the matrix product (c.f. example below) to use all the cores available on the machine, which can be a problem.
Do you know how to control the number of cores used for standard matrix product in R?
Thanks in advance
Example:
n=10000
p=1000
q=5000
A = matrix(runif(n*p),nrow=n, ncol=p)
B = matrix(runif(p*q),nrow=p, ncol=q)
C = A %*% B # multi-threaded matrix product
Session info:
> sessionInfo()
R version 3.4.1 (2017-06-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS
Matrix products: default
BLAS: /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so
locale:
[1] LC_CTYPE=fr_FR.utf8 LC_NUMERIC=C
[3] LC_TIME=fr_FR.utf8 LC_COLLATE=fr_FR.utf8
[5] LC_MONETARY=fr_FR.utf8 LC_MESSAGES=fr_FR.utf8
[7] LC_PAPER=fr_FR.utf8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=fr_FR.utf8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.4.1
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Concurrency and Parallelism In the same multithreaded process in a shared-memory multiprocessor environment, each thread in the process can run concurrently on a separate processor, resulting in parallel execution, which is true simultaneous execution.
You can effectively multi-thread R by using KNIME or any other program that utilizes the rserve.exe executable. In KNIME, you can put an R Snippet within a Parallel Chunk node series for operations done row-wise.
Introduction. From its inception, R was designed to use only a single thread (processor) at a time. Even today, R works that way unless linked with multi-threaded BLAS/LAPACK libraries. The multi-core machines of today offer parallel processing power.
Splitting the computation in your program into multiple threads to run multiple tasks at the same time can improve performance, but it also adds complexity. Because threads can run simultaneously, there's no inherent guarantee about the order in which parts of your code on different threads will run.
The package RhpcBLASctl does just that.
From its DESCRIPTION:
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'ACML' and 'MKL'). and possible to control the number of threads in 'OpenMP'. get a number of logical cores and physical cores if feasible.
We mention it in the CRAN Task View on HPC.
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