[Rd] Control multi-threading in standard matrix product
Dear R Core Team, I wish to report what can be viewed as a bug or at least a strange behavior in R-3.4.1. I ask my question here (as recommended on https://www.r-project.org/bugs.html) since I am not member of the R's Bugzilla. When running 'R --vanilla' from the command line, the standard matrix product is by default based on BLAS and multi-threaded on all cores available on the machine, c.f. following examples: n=1 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 However, the default behavior to use all available cores can be an issue, especially on shared computing resources or when the matrix product is used in parallelized section of codes (for instance with 'mclapply' from the 'parallel' package). For instance, the default matrix product is single-threaded in R-3.3.2 (I ran a test on my machine), this new features will deeply affect the behavior of existing R packages that use other multi-threading solutions. Thanks to this stackoverflow question (https://stackoverflow.com/questions/45794290/in-r-how-to-control-multi-threading-in-blas-parallel-matrix-product), I now know that it is possible to control the number of BLAS threads thanks to the package 'RhpcBLASctl'. However, being able to control the number of threads should maybe not require to use an additional package. In addition, the doc 'matmult' does not mention this point, it points to the 'options' doc page and especially the 'matprod' section, in which the multi-threading is not discussed. Here is the results of the 'sessionInfo()' function on my machine for R-3.4.1: 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.utf8LC_COLLATE=fr_FR.utf8 [5] LC_MONETARY=fr_FR.utf8LC_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 and for R-3.3.2: R version 3.3.2 (2016-10-31) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 16.04.3 LTS locale: [1] LC_CTYPE=fr_FR.utf8 LC_NUMERIC=C [3] LC_TIME=fr_FR.utf8LC_COLLATE=fr_FR.utf8 [5] LC_MONETARY=fr_FR.utf8LC_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 Thanks in advance, Best regards || -- Ghislain Durif -- Research engineer THOTH TEAM INRIA Grenoble Alpes (France) [[alternative HTML version deleted]] __ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Control multi-threading in standard matrix product
Hi Tomas, Thanks for your answer. Indeed, I checked and my R-3.4.1 installed from the ubuntu repository use 'libopenblasp-r0.2.18.so' while my R-3.3.2 that I did compiled on my machine use 'libRblas.so' which explain the difference of behavior. I will use RhpcBLASctl to avoid issue when combining matrix product and other multi-threading package. Maybe this point regarding multi-threading with BLAS could be added in the R doc. Thanks again, Best, Ghislain Ghislain Durif -- Research engineer THOTH TEAM INRIA Grenoble Alpes (France) Le 21/08/2017 à 15:53, Tomas Kalibera a écrit : Hi Ghislain, I think you might be comparing two versions of R with different BLAS implementations, one that is single threaded (is your 3.3.2 used with reference blas?) and one that is multi threaded (3.4.1 with openblas). Could you check with "perf"? E.g. run your benchmark with "perf record" in both cases and you should see the names of the hot BLAS functions and this should reveal the BLAS implementation (look for dgemm). In Ubuntu, if you install R from the package system, whenever you run it it will use the BLAS currently installed via the package system. However if you build R from source on Ubuntu, by default, it will use the reference BLAS which is distributed with R. Section "Linear algebra" of "R Installation and Administration" has details on how to build R with different BLAS/LAPACK implementations. Sadly there is no standard way to specify the number of BLAS worker threads. RhpcBLASctl has specific code for several existing implementations, but R itself does not attempt to control BLAS multi threading in any way. It is expected the user/system administrator will configure their BLAS implementation of choice to use the number of threads they need. A similar problem exists in other internally multi-threaded third-party libraries, used by packages - R cannot control how many threads they run. Best Tomas On 08/21/2017 02:55 PM, Ghislain Durif wrote: Dear R Core Team, I wish to report what can be viewed as a bug or at least a strange behavior in R-3.4.1. I ask my question here (as recommended on https://www.r-project.org/bugs.html) since I am not member of the R's Bugzilla. When running 'R --vanilla' from the command line, the standard matrix product is by default based on BLAS and multi-threaded on all cores available on the machine, c.f. following examples: n=1 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 However, the default behavior to use all available cores can be an issue, especially on shared computing resources or when the matrix product is used in parallelized section of codes (for instance with 'mclapply' from the 'parallel' package). For instance, the default matrix product is single-threaded in R-3.3.2 (I ran a test on my machine), this new features will deeply affect the behavior of existing R packages that use other multi-threading solutions. Thanks to this stackoverflow question (https://stackoverflow.com/questions/45794290/in-r-how-to-control-multi-threading-in-blas-parallel-matrix-product), I now know that it is possible to control the number of BLAS threads thanks to the package 'RhpcBLASctl'. However, being able to control the number of threads should maybe not require to use an additional package. In addition, the doc 'matmult' does not mention this point, it points to the 'options' doc page and especially the 'matprod' section, in which the multi-threading is not discussed. Here is the results of the 'sessionInfo()' function on my machine for R-3.4.1: 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.utf8LC_COLLATE=fr_FR.utf8 [5] LC_MONETARY=fr_FR.utf8LC_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 and for R-3.3.2: R version 3.3.2 (2016-10-31) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 16.04.3 LTS locale: [1] LC_CTYPE=fr_FR.utf8 LC_NUMERIC=C [3] LC_TIME=fr_FR.utf8LC_COLLATE=fr_FR.utf8 [5] LC_MONETARY=fr_FR.utf8LC_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 grDevice
Re: [Rd] Control multi-threading in standard matrix product
Hi Ista,
Maybe a little comment in the 'matmult {base}' doc page or on the
'options {base}' in the field 'matprod' would be useful to remind users
to be cautious regarding BLAS multi-threading?
I understand why this is a BLAS related issue and not directly an R
related issue. Nonetheless, my concern was for non-advanced R users,
that may don't even know what BLAS is. For instance, I have a package on
the CRAN that use 'mclapply' from the 'parallel' package and the
multi-threading from BLAS (when using OpenBLAS) totally messes up with
my multi-threading (regarding computing performance). Hence, I think
that some of the users of my package, if not aware of that, may
encounter severe issues, especially because this package is mainly used
to analyse bioinformatics data, which requires important computing
resources.
Anyway, it works well on my machine now, and I will modify my package to
ensure that users won't encounter this case of "multi" multi-threading.
Thanks again,
Best,
Ghislain
Ghislain Durif
--
Research engineer THOTH TEAM
INRIA Grenoble Alpes (France)
Le 21/08/2017 à 17:28, Ista Zahn a écrit :
Hi Ghislain,
The documentation at
https://cran.r-project.org/doc/manuals/r-release/R-admin.html#BLAS
provides a fair bit of information. What specifically would you like
to see added?
Best,
Ista
On Mon, Aug 21, 2017 at 10:13 AM, Ghislain Durif
wrote:
Hi Tomas,
Thanks for your answer.
Indeed, I checked and my R-3.4.1 installed from the ubuntu repository use
'libopenblasp-r0.2.18.so' while my R-3.3.2 that I did compiled on my machine
use 'libRblas.so' which explain the difference of behavior.
I will use RhpcBLASctl to avoid issue when combining matrix product and
other multi-threading package.
Maybe this point regarding multi-threading with BLAS could be added in the R
doc.
Thanks again,
Best,
Ghislain
Ghislain Durif
--
Research engineer THOTH TEAM
INRIA Grenoble Alpes (France)
Le 21/08/2017 à 15:53, Tomas Kalibera a écrit :
Hi Ghislain,
I think you might be comparing two versions of R with different BLAS
implementations, one that is single threaded (is your 3.3.2 used with
reference blas?) and one that is multi threaded (3.4.1 with openblas). Could
you check with "perf"? E.g. run your benchmark with "perf record" in both
cases and you should see the names of the hot BLAS functions and this should
reveal the BLAS implementation (look for dgemm).
In Ubuntu, if you install R from the package system, whenever you run it
it will use the BLAS currently installed via the package system. However if
you build R from source on Ubuntu, by default, it will use the reference
BLAS which is distributed with R. Section "Linear algebra" of "R
Installation and Administration" has details on how to build R with
different BLAS/LAPACK implementations.
Sadly there is no standard way to specify the number of BLAS worker
threads. RhpcBLASctl has specific code for several existing implementations,
but R itself does not attempt to control BLAS multi threading in any way. It
is expected the user/system administrator will configure their BLAS
implementation of choice to use the number of threads they need. A similar
problem exists in other internally multi-threaded third-party libraries,
used by packages - R cannot control how many threads they run.
Best
Tomas
On 08/21/2017 02:55 PM, Ghislain Durif wrote:
Dear R Core Team,
I wish to report what can be viewed as a bug or at least a strange
behavior in R-3.4.1. I ask my question here (as recommended on
https://www.r-project.org/bugs.html) since I am not member of the R's
Bugzilla.
When running 'R --vanilla' from the command line, the standard matrix
product is by default based on BLAS and multi-threaded on all cores
available on the machine, c.f. following examples:
n=1
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
However, the default behavior to use all available cores can be an
issue, especially on shared computing resources or when the matrix
product is used in parallelized section of codes (for instance with
'mclapply' from the 'parallel' package). For instance, the default
matrix product is single-threaded in R-3.3.2 (I ran a test on my
machine), this new features will deeply affect the behavior of existing
R packages that use other multi-threading solutions.
Thanks to this stackoverflow question
(https://stackoverflow.com/questions/45794290/in-r-how-to-control-multi-threading-in-blas-parallel-matrix-product),
I now know that it is possible to control the number of BLAS threads
thanks to the package 'RhpcBLASctl'. However, being able to control the
number of threads should maybe not require to use an additional package.
In a
