From istazahn at gmail.com  Mon Aug 21 17:28:10 2017
From: istazahn at gmail.com (Ista Zahn)
Date: Mon, 21 Aug 2017 11:28:10 -0400
Subject: [Rd] Control multi-threading in standard matrix product
In-Reply-To: <49ca2606-ee0d-d6ea-b894-81816bc5fad4@inria.fr>
References: <825dace5-8fca-3660-b8b2-fd9f115b29ac@inria.fr>
 <50f32a42-786d-e7c7-71e5-ed0efa975f0a@gmail.com>
 <49ca2606-ee0d-d6ea-b894-81816bc5fad4@inria.fr>
Message-ID: <CA+vqiLE50rq-2StKUV5zxueLqxtXsDKNBVw_so9WC-kj-1T2fg@mail.gmail.com>

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
<ghislain.durif at inria.fr> 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 ? 15:53, Tomas Kalibera a ?crit :
>>
>> 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=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
>>>
>>>
>>> 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.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
>>>
>>>
>>>
>>> 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.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
>>>
>>>
>>> Thanks in advance,
>>> Best regards
>>> ||
>>>
>>
>
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[Rd] Control multi-threading in standard matrix product
Ista Zahn