Le 04/24/2015 03:19 PM, Brian Bouterse a écrit :
I don't know much about scilab optimizations, but trying to
hand-optimize auto-generated code almost certainly will result in
correctness problems. It's likely better to make your improvements in
the software that is generating the scilab code. I think the main
feature there is to have it generate a vectorized implementation
instead of a procedural one (as you have now).
I agree with you: scilab is fast with linear algebra and slow for the
rest and so far you have not generated your scilab code with scilab
strength and weakness in mind.
So if you stick with Scilab, you should go for a matrix form (maybe a
sparse matrix) or follow Samuel's version.
Cheers,
Antoine
-Brian
On Fri, Apr 24, 2015 at 8:34 AM, Stéphane Mottelet
<[email protected] <mailto:[email protected]>> wrote:
Hello,
this is not trivial indexing, in fact some terms are linear
combination of v's components
M1_v=[v(17)
v(104)
v(149)
-(v(18)+v(63)+v(103))
-(v(18)+v(63)+v(103))
v(17)
...
v(104)
v(149)
]
How do you take this into account in your proposed method ? These
combinations are sums of influxes in a metabolic network, and the
code is automatically generated.
S.
Le 24/04/2015 13:48, Samuel Gougeon a écrit :
Le 24/04/2015 13:36, Samuel Gougeon a écrit :
Hello Stephane,
You can speed up by a factor larger than 100 just by calling v
once (or 3 times) instead of ~1000, as shown by this test:
Actually, to be more accurate, the right comparative test is the
following:
function test2()
v = rand(172,1);
p = grand(1,839,"unf",1,173)
// Part 1: 1 call to v()
tic()
for i=1:1000
m1_v = v(p)
end
disp(toc())
// Part 2 : 839 calls to v()
deff("test3()", "for i=1:1000, M1_v = ["+strcat("v("+string(p)+")")+"],
end")
tic()
test3()
disp(toc())
endfunction
In this version, the compilation time used by execstr() is no
longer taken into account.
The results are still explicit:
-->test2()
0.016
0.78
So, a speed-up by ~x 50
Samuel
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--
Brian Bouterse
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