Plain old multiplication much slower than Matlab 2013a

[bdqp]

Julia 0.4:
julia> A=rand(3000,3000);B=rand(3000,3000);
julia> tic();for k=1:50;C=A.*B;end;toc()
elapsed time: 2.386786733 seconds
2.386786733

Matlab 2013a:

A=rand(3000);B=rand(3000);
tic;for k=1:50;C=A.*B;end;toc
Elapsed time is 1.501691 seconds.

This is on a 2 core machine. I also ran it on another computer with 6 cores and the difference is greater (about 4x). So it seems Matlab is using parallelism for .* while Julia isn't. A little experimentation in Matlab shows that it drops to 1 core when the matrix size is 191x191 or smaller.

[KristofferC]

What is the issue here? Julia does not run multithreaded code yet so naturally it will be slower. Your best bet right now is the ParallelAccelerator.jl package.

[JeffBezanson]

Duplicate of #1802

[iamed2]

in Julia 0.5 (it's roughly the same as 0.4):

julia> A=rand(3000,3000);B=rand(3000,3000);

julia> tic();for k=1:50;C=A.*B;end;toc()
elapsed time: 2.408215731 seconds
2.408215731

julia> tic();for k=1:50;C=A.*B;end;toc()
elapsed time: 1.662830871 seconds
1.662830871

MATLAB 2012b:

>> A=rand(3000);B=rand(3000);
tic;for k=1:50;C=A.*B;end;toc
Elapsed time is 1.429565 seconds.
>> tic;for k=1:50;C=A.*B;end;toc
Elapsed time is 1.455223 seconds.

Also on a 2 core machine. Seems like there is an initial compilation pass involved that is skewing the difference?

[KristofferC]

Closing as dup of #1802.