Much slower valid convolution using complementary size of kernels.
Mostrar comentarios más antiguos
I am using the valid convolution using convn( T, a, 'valid').
I have run the code below:
T = randn(384,384,8);
a = randn(5,5,8);
b = randn(380,380,1);
tic; convn(T,a,'valid'); toc
tic; convn(T,b,'valid'); toc
The reuslt in my computer is
Elapsed time is 0.002837 seconds.
Elapsed time is 0.016301 seconds.
Thus the the latter is much slower compared to fomer one.
However, in terms of flops, or only in terms of multiplications
convn(T,a,'valid')
takes 5*5*8*(384-5+1)*(384-5+1)*(8-8+1) = 28880000 multiplications
convn(T,b,'valid')
also takes 380*380*1*(384-380+1)*(384-380+1)*(8-1+1) = 28880000 multiplications
So why are the two computing time so different?
And is there some ways to implement the convn(T,b,'valid') much faster?
3 comentarios
Bruno Luong
el 22 de Dic. de 2020
"So why are the two computing time so different?"
Just a guess but convn(T,a,'valid') possible more suitable to be parallelize since the result is (380 x 380) and each can be computed independently.
Whereas convn(T,a,'valid') is harder.
Bruno Luong
el 24 de Dic. de 2020
Editada: Bruno Luong
el 24 de Dic. de 2020
No not FLOPS. As you said the FLOPS are more or less indentical.
Respuestas (3)
Bjorn Gustavsson
el 21 de Dic. de 2020
0 votos
No, n-dimensional fourier-transforms, multiplication of the Fourier-transforms of 5-5-8 a with T will be a fair bit faster than the multiplication of the 380-by-380-by-1 b with T.
HTH
Roshan Hingnekar
el 22 de Dic. de 2020
Editada: Walter Roberson
el 22 de Dic. de 2020
0 votos
T and 'a' are 3 dimensional where as 'b' is 2 dimensional, convolution of 3-dimensional with 2-dimensional will be slower than a 3-dimensional with a 3-dimensional.
refer to the below links for further insight on randn and convn functions.
1 comentario
Shen Zhao
el 24 de Dic. de 2020
Bruno Luong
el 22 de Dic. de 2020
0 votos
I would suggest to do specific conv with MEX programing.
Not sure the chance to beat MATLAB though.
1 comentario
Shen Zhao
el 24 de Dic. de 2020
Categorías
Más información sobre GPU Computing en Centro de ayuda y File Exchange.
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!