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matlabgpu
Steven Du
Steven Du

Reputation: 1691

matlab convn function GPU slower than CPU?

I test the convn on two GPU: Quadro 6000 and Titan both take longer time than cpu.

A quick test can be done in matlab:

maxloop=1000;
for i=1:maxloop
   output2= convn(rand(320,1), rand([6,1,300]),'full');
end

for i=1:maxloop    
    goutput2= convn(gpuArray.rand(320,1),gpuArray.rand([6,1,300]), 'full');
end

It takes, 0.52s on CPU, but 7s on Quadro 6000 and 15s+- on Titan.

What I had tested:

1) If change the rand input to fixed, predefined values does not give any improvement.

2) Predefine GPU output(goutput2) doesn't help so much.

Quadro

Quadro 6000

Titan

Titan

I do run the same test as the first answer:

Matlab Convolution using gpu

Same result obtained when m=1000; n=100; k=5;

Elapsed time is 2.367453 seconds. %%%%GPU

Elapsed time is 27.502952 seconds. %%%%CPU

My question is what and why my own test code is running slower on GPU?

Upvotes: 2

Views: 920

Answers (1)

Steven Du
Steven Du

Reputation: 1691

It looks like question Matlab Convolution using gpu at first glances. But after increase data size from [6,1,300] to [1000,1,1000], there is no improvement in the GPU loop. So it's not about "size" of data.

When I reshape data from [1000,1,1000] to [1000,1000], GPU runs faster and CPU runs slower than previous test. Code and timing are listed below:

clear all;

maxloop=1000;

r5=rand(5,1);
r1000=rand([1000,1,1000]); %3d array

for i=1:maxloop   
   cpu_output1= convn(r5, r1000,'full');  %3d cpu array
end

r1000=reshape(r1000,[1000,1000]);
for i=1:maxloop
   cpu_output2= convn(r5, r1000,'full'); %2d cpu array
end

gr5=gpuArray.rand(5,1);
gr1000=gpuArray.rand([1000,1,1000]);

for i=1:maxloop    
    gpu_output1= convn(gr5,gr1000, 'full'); %3d gpu array
end

gr3_1000=reshape(gr1000,[1000,1000]);
for i=1:maxloop    
    gpu_output2= convn(gr5,gr3_1000, 'full'); %2d gpu array
end

enter image description here

Upvotes: 1

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