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<div class="moz-cite-prefix">Le 23/09/2019 à 23:13,
<a class="moz-txt-link-abbreviated" href="mailto:stephane.mottelet@utc.fr">stephane.mottelet@utc.fr</a> a écrit :<br>
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cite="mid:20190923231316.Horde.mwrKy0SgDVsGsNj1WeNmwnB@webmail.utc.fr">
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<p>Hello Antoine,<br>
<br>
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. <br>
</p>
</blockquote>
<p>Hello Stephane,</p>
<p>I'm sorry, I did not want to sound rude calling it a "dirty
hack".<br>
It's just the explanation I got from scilab developers last time I
discussed with them about it.<br>
We were trying to get similar performances under linux/osx/windows
with some code using parallel_run.<br>
That was a dead end.<br>
</p>
<p>Having any sort of parallel computing under scilab 6.0.2 would be
a real step forward for us.<br>
I have played a bit with julia to see what it means parallelizing
some code in julia.<br>
It was quite easy.<br>
I don't know whether a similar approach can be implemented in
scilab.</p>
<p><br>
</p>
<p>Antoine<br>
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<blockquote type="cite"
cite="mid:20190923231316.Horde.mwrKy0SgDVsGsNj1WeNmwnB@webmail.utc.fr">
<p>
<br>
S. <br>
<br>
Quoting Antoine Monmayrant <<a href="mailto:amonmayr@laas.fr"
moz-do-not-send="true">amonmayr@laas.fr</a>>:</p>
<blockquote style="border-left:2px solid
blue;margin-left:2px;padding-left:12px;" type="cite">
<p>Hello,<br>
<br>
Are you using Windows or anything else?<br>
Because as far as I remember, parallel_run is just a dirty
hack that only works under windows.<br>
On my linux install of scilab 6.0.2, I also have:<br>
--> parallel_run<br>
<br>
Undefined variable: parallel_run<br>
<br>
So it might be a bug in the documentation that should mention
"windows only" like some other functions.<br>
<br>
Antoine<br>
<br>
<br>
Le Lundi, Septembre 23, 2019 15:46 CEST, fujimoto2005 <<a
href="mailto:fujimoto2005@gmail.com" moz-do-not-send="true">fujimoto2005@gmail.com</a>>
a écrit:<br>
</p>
<blockquote style="border-left:2px solid
blue;margin-left:2px;padding-left:12px;" type="cite">
<p>I wanted to use "parallel_run" and tried the sample script
in the help page.<br>
But it did not work with the error message "undefined<br>
variable:parallel_run".<br>
Please teach me what is wrong?<br>
<br>
The sample script I used is as follows<br>
******<br>
for i = 1:10<br>
res(i) = i*i;<br>
end;<br>
// for parallel_run, we need to have a function performing
the computation<br>
function a=g(arg1)<br>
a=arg1*arg1<br>
endfunction<br>
<br>
res = parallel_run(1:10, g);<br>
// => res = [1 4 9 16 25 36 49 64 81 100];<br>
*********<br>
<br>
<br>
<br>
<br>
<br>
--<br>
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