Hello,
Don't even think about using parallel_run under OSX. It used to work
(with some tweaking) with scilab-5.5.1 under Mavericks, but since
5.5.2 version, it has become completely unstable.
S.
Heinz <heinznabie...@me.com> a écrit :
My latest tictoc is 366: would that be 6.1 minutes?
I am away from my 2 iMacs at home and running Scilab on a lowly
Intel Pentium CPU N3540 @ 2.16GHz Win10 laptop. Your "2.498" for
40,000 points are phenomenal regarding the fact that the running
time increases with the square of the number of points.
The later iMac has a Quad-Core-i7 at 3.2 GHz: can I run your code
version there under the older Scilab? Not that I understand how
parallel processing works...
Heinz
-----Original Message-----
From: users [mailto:users-boun...@lists.scilab.org] On Behalf Of
Stéphane Mottelet
Sent: 31 January 2018 16:12
To: users@lists.scilab.org
Subject: Re: [Scilab-users] {EXT} need a more efficient and faster
code: suggestions welcome
Dear Heinz,
Here is what can be done in Scilab-5.5.2 to accelerate your
computations on a multi-core architecture under Linux :
function out=distances(X)
function out=distance(k)
this=X(k,:);XX=X;XX(k,:)=[];
DIFF=XX-ONE*this;
out=sqrt(min((DIFF.*DIFF)*[1;1;1]));
endfunction
n=size(X,1);
ONE=ones(n-1,1);
out=parallel_run(1:n,distance);
endfunction
n=40000;
r=23;
radius = r*grand(n,1,'def').^(1/3);
phi = 2*%pi*grand(n,1, 'def');
costheta = 1 - 2*grand(n,1, 'def');
radsintheta = radius.*sin(acos(costheta)); X =
[radsintheta.*cos(phi),radsintheta.*sin(phi), radius.*costheta];
On a modest server (two 10-core/20-threads Intel(R) Xeon(R) CPU E5-2660
v2 @ 2.20GHz = 40 threads),
-->tic;distances(X);toc
ans =
13.063
On a smarter server (sixteen 6-core/6-threads Intel(R) Xeon(R) CPU
X7542 @ 2.67GHz = 96 threads)
-->tic;d=distances(X);toc
ans =
2.498
I hope that parallel_run will be available again in the next version
of scilab.
S.
Le 31/01/2018 à 10:53, Stéphane Mottelet a écrit :
Replacing
MinDist=[MinDist sqrt(min(sum(DIFF.^2,2)))];
by
MinDist=[MinDist sqrt(min(sum(DIFF.*DIFF,2)))];
will be at least twice faster. Crunching elapsed time could be done by
using parallel_run (with 5.5.2 version) if you have a multi-core
processor.
S.
Le 31/01/2018 à 09:36, Dang Ngoc Chan, Christophe a écrit :
Hello,
The following suggestions will probably not have a drastic influence
(I don't see how it could be more vectorised) but his a little thing
I see:
De : users [mailto:users-boun...@lists.scilab.org] De la part de
Heinz Nabielek Envoyé : mercredi 31 janvier 2018 00:13
MinDist=[MinDist sqrt(min(sum(DIFF.^2,2)))];
Maybe you could concatenate the squares of the distance and then
compute the square root of the whole vector in the end:
sqMinDist=[sqMinDist min(sum(DIFF.^2,2))];
…
end
…
MinDist = sqrt(sqMinDist)
Hope this helps,
Regards
--
Christophe Dang Ngoc Chan
Mechanical calculation engineer
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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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