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:
function test2()
// part 1 : only 1 call to v
v = rand(172,1);
p = grand(1,839,"unf",1,173)
tic()
for i=1:1000
m1_v = v(p)
end
disp(toc())
// part 2 : 839 calls to v
e = "M1_v = ["+strcat("v("+string(p)+")")+"]"
tic()
for i=1:1000
execstr(e)
end
disp(toc())
// part 3 : calls to "empty" execstr(), to be substrated from the previous
result
e = "M1_v = 0";
tic()
for i=1:1000
execstr(e)
end
disp(toc())
endfunction
On my PC, i get:
-->test2
0.015
1.888
0.015
So, you do not really need to go to compiled solutions, just to rewrite
839 lines into ~10 lines.
Regards
Samuel
Le 23/04/2015 23:51, Stéphane Mottelet a écrit :
Hello,
I am currently working on a project where Scilab code is automatically
generated, and after many code optimization, the remaining bottleneck
is the time that Scilab spends to execute simple code like this (full
script (where the vector has 839 lines) with timings is attached) :
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)
]
This kind of large vectors are the used to build a sparse matrix each
time the vector v changes, but with a constant sparsity pattern.
Actually, the time spent by Scilab in the statement
M1=sparse(M1_ij,M1_v,[n1,n2])
is negligible compared to the time spent to build f M1_v...
I have also noticed that if you need to define such a matrix with more
that one column, the time elapsed is not linear with respect to the
number of columns: typically 4 times slower for 2 columns. In fact the
statement
v=[1 1
...
1000 1000]
is even two times slower than
v1=[1
...
1000];
v2=[1
....
1000];
v=[v1 v2];
So my question to users who have the experience of dynamic link of
user code : do you think that using dynamic link of compiled generated
C code could improve the timings ?
In advance, thanks for your help !
S.
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