[Scilab-users] {EXT} need a more efficient and faster code: suggestions welcome

[Heinz]

On my puny 250 Euro Win10 travel-laptop, I have achieved 66 tictocs for 20,000 random points with your code [not that I would understand it] in Scilab 5.5.2 and that is phenomenal and opens up new simulation possibilities.....

Thanks so much....
Heinz

-----Original Message-----
From: users [mailto:users-bounces at lists.scilab.org] On Behalf Of Stéphane Mottelet
Sent: 31 January 2018 16:12
To: users at 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-bounces at 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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