Le 24/09/2019 à 09:29, Antoine Monmayrant a écrit :
Le 23/09/2019 à 23:13, stephane.motte...@utc.fr a écrit :
Hello Antoine,
Before judging that some code is just a "dirty hack" you should first
get into the code and see hows things are written. The implementation
in 5.5.2 was using OpenMP so I don't see anything dirty here. The
implementation was unstable under OSX and Linux, and under Windows
parallel_run was just making calls sequentially. So no "dirty hack"
under Windows but just a shortcut. Octave does also the same with
Matlatb's parfor : it just does things sequentially. However, even
unstable, parallel_run was very useful (I used it a lot under Linux
to do some Monte Carlo estimation on a 40 processor machine). It is a
pity that it's re-engineering for 6.0 familiy was not a priority.
Hello Stephane,
I'm sorry, I did not want to sound rude calling it a "dirty hack".
It's just the explanation I got from scilab developers last time I
discussed with them about it.
We were trying to get similar performances under linux/osx/windows
with some code using parallel_run.
Under OSX parallel_run used to work (a little bit) up to 5.5.1 version,
then it was almost unusable.
That was a dead end.
Having any sort of parallel computing under scilab 6.0.2 would be a
real step forward for us.
I have played a bit with julia to see what it means parallelizing some
code in julia.
It was quite easy.
Yeah, Julia has many interesting features but things have been
developped with such a kind of orthodoxy that using it is sometimes very
baffling...
I don't know whether a similar approach can be implemented in scilab.
The rewritten internals (ast module) would allow to implement (at least)
something similar to Matlab's parfor. parallel_run worked by forking the
main Scilab process, that's why it was not transposable to Windows (no
fork()). Maybe could it be possible with threads with the new
architecture ? The only thing that's certain is that without an active
collaboration of Antoine Elias this feature will never be developped.
The only way is to persuade ESI that it is a strategic feature for their
business with Scilab !
S.
Antoine
S.
Quoting Antoine Monmayrant <amonm...@laas.fr <mailto:amonm...@laas.fr>>:
Hello,
Are you using Windows or anything else?
Because as far as I remember, parallel_run is just a dirty hack that
only works under windows.
On my linux install of scilab 6.0.2, I also have:
--> parallel_run
Undefined variable: parallel_run
So it might be a bug in the documentation that should mention
"windows only" like some other functions.
Antoine
Le Lundi, Septembre 23, 2019 15:46 CEST, fujimoto2005
<fujimoto2...@gmail.com <mailto:fujimoto2...@gmail.com>> a écrit:
I wanted to use "parallel_run" and tried the sample script in the
help page.
But it did not work with the error message "undefined
variable:parallel_run".
Please teach me what is wrong?
The sample script I used is as follows
******
for i = 1:10
res(i) = i*i;
end;
// for parallel_run, we need to have a function performing the
computation
function a=g(arg1)
a=arg1*arg1
endfunction
res = parallel_run(1:10, g);
// => res = [1 4 9 16 25 36 49 64 81 100];
*********
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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
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