[Scilab-Dev] BLAS use in Scilab
Stéphane Mottelet
stephane.mottelet at utc.fr
Tue Feb 20 11:57:00 CET 2018
Hello,
Continuing on this subject, Hello, I discovered that the new Scilab API
allows to modify input parameters of a function (in-place assignment),
e.g. I have modified the previous daxpy such that the expression
daxpy(2,X,Y)
has no output but formally does "Y+=2*X" if such an operator would exist
in Scilab. In this case there is no matrix copy at all, hence no memory
overhead.
Was it possible to do this with the previous API ?
S.
Le 19/02/2018 à 19:15, Stéphane Mottelet a écrit :
> Hello,
>
> After some tests, for the intended use (multiply a matrix by a
> scalar), dgemm is not faster that dscal, but in the C code of
> "iMultiRealScalarByRealMatrix", the part which takes the most of the
> CPU time is the call to "dcopy". For example, on my machine, for a
> 10000x10000 matrix, the call to dcopy takes 540 milliseconds and the
> call to dscal 193 milliseconds. Continuing my explorations today, I
> tried to see how Scilab expressions such as
>
> Y+2*X
>
> are parsed and executed. To this purpose I have written an interface
> (daxpy.sci and daxpy.c attached) to the BLAS function "daxpy" which
> does "y<-y+a*x" and a script comparing the above expression to
>
> daxpy(2,X,Y)
>
> for two 10000x10000 matrices. Here are the results (MacBook air core
> i7 at 1,7GHz):
>
> daxpy(2,X,Y)
> (dcopy: 582 ms)
> (daxpy: 211 ms)
>
> elapsed time: 793 ms
>
> Y+2*X
>
> elapsed time: 1574 ms
>
> Considered the above value, the explanation is that in "Y+2*X" there
> are *two* copies of a 10000x10000 matrix instead of only one in
> "daxpy(2,X,Y)". In "Y+2*X+3*Z" there will be three copies, although
> there could be only one if daxpy was used twice.
>
> I am not blaming Scilab here, I am just blaming "vectorization", which
> can be inefficient when large objects are used. That's why explicits
> loops can sometimes be faster than vectorized operations in Matlab or
> Julia (which both use JIT compilation).
>
> S.
>
>
> Le 15/02/2018 à 17:11, Antoine ELIAS a écrit :
>> Hello Stéphane,
>>
>> Interesting ...
>>
>> In release, we don't ship the header of BLAS/LAPACK functions.
>> But you can define them in your C file as extern. ( and let the
>> linker do his job )
>>
>> extern int C2F(dgemm) (char *_pstTransA, char *_pstTransB, int *_piN,
>> int *_piM, int *_piK, double *_pdblAlpha, double *_pdblA, int *_piLdA,
>> double *_pdblB, int *_piLdB, double
>> *_pdblBeta, double *_pdblC, int *_piLdC);
>> and
>>
>> extern int C2F(dscal) (int *_iSize, double *_pdblVal, double
>> *_pdblDest, int *_iInc);
>>
>> Others BLAS/LAPACK prototypes can be found at
>> http://cgit.scilab.org/scilab/tree/scilab/modules/elementary_functions/includes/elem_common.h?h=6.0
>>
>> Regards,
>> Antoine
>> Le 15/02/2018 à 16:50, Stéphane Mottelet a écrit :
>>> Hello all,
>>>
>>> Following the recent discussion with fujimoto, I discovered that
>>> Scilab does not (seem to) fully use SIMD operation in BLAS as it
>>> should. Besides the bottlenecks of its code, there are also many
>>> operations of the kind
>>>
>>> scalar*matrix
>>>
>>> Althoug this operation is correctly delegated to the DSCAL BLAS
>>> function (can be seen in C function iMultiRealMatrixByRealMatrix in
>>> modules/ast/src/c/operations/matrix_multiplication.c) :
>>>
>>>> int iMultiRealScalarByRealMatrix(
>>>> double _dblReal1,
>>>> double *_pdblReal2, int _iRows2, int _iCols2,
>>>> double *_pdblRealOut)
>>>> {
>>>> int iOne = 1;
>>>> int iSize2 = _iRows2 * _iCols2;
>>>>
>>>> C2F(dcopy)(&iSize2, _pdblReal2, &iOne, _pdblRealOut, &iOne);
>>>> C2F(dscal)(&iSize2, &_dblReal1, _pdblRealOut, &iOne);
>>>> return 0;
>>>> }
>>> in the code below the product "A*1" is likely using only one
>>> processor core, as seen on the cpu usage graph and on the elapsed time,
>>>
>>> A=rand(20000,20000);
>>> tic; for i=1:10; A*1; end; toc
>>>
>>> ans =
>>>
>>> 25.596843
>>>
>>> but this second piece of code is more than 8 times faster and uses
>>> 100% of the cpu,
>>>
>>> ONE=ones(20000,1);
>>> tic; for i=1:10; A*ONE; end; toc
>>>
>>> ans =
>>>
>>> 2.938314
>>>
>>> with roughly the same number of multiplications. This second
>>> computation is delegated to DGEMM (C<-alpha*A*B + beta*C, here with
>>> alpha=1 and beta=0)
>>>
>>>> int iMultiRealMatrixByRealMatrix(
>>>> double *_pdblReal1, int _iRows1, int _iCols1,
>>>> double *_pdblReal2, int _iRows2, int _iCols2,
>>>> double *_pdblRealOut)
>>>> {
>>>> double dblOne = 1;
>>>> double dblZero = 0;
>>>>
>>>> C2F(dgemm)("n", "n", &_iRows1, &_iCols2, &_iCols1, &dblOne,
>>>> _pdblReal1 , &_iRows1 ,
>>>> _pdblReal2, &_iRows2, &dblZero,
>>>> _pdblRealOut , &_iRows1);
>>>> return 0;
>>>> }
>>> Maybe my intuition is wrong, but I have the feeling that using dgemm
>>> with alpha=0 will be faster than dscal. I plan to test this by
>>> making a quick and dirty code linked to Scilab so my question to
>>> devs is : which are the #includes to add on top of the source (C) to
>>> be able to call dgemm and dscal ?
>>>
>>> Thanks for your help
>>>
>>> S.
>>>
>>>
>>
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>
>
> --
> Stéphane Mottelet
> Ingénieur de recherche
> EA 4297 Transformations Intégrées de la Matière Renouvelable
> Département Génie des Procédés Industriels
> Sorbonne Universités - Université de Technologie de Compiègne
> CS 60319, 60203 Compiègne cedex
> Tel : +33(0)344234688
> http://www.utc.fr/~mottelet
>
>
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> dev mailing list
> dev at lists.scilab.org
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--
Stéphane Mottelet
Ingénieur de recherche
EA 4297 Transformations Intégrées de la Matière Renouvelable
Département Génie des Procédés Industriels
Sorbonne Universités - Université de Technologie de Compiègne
CS 60319, 60203 Compiègne cedex
Tel : +33(0)344234688
http://www.utc.fr/~mottelet
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