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## GPUCONV2

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Example, Matlab R2010B Cuda CONV2 on GPU using Cuda kernels

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GPUCONV2 Two dimensional convolution on the GPU using Cuda.

C = GPUCONV2(A, B) performs the 2-D convolution of matrices A and B.
If [ma,na] = size(A), [mb,nb] = size(B), and [mc,nc] = size(C), then
mc = max([ma+mb-1,ma,mb]) and nc = max([na+nb-1,na,nb]).

C = GPUCONV2(A, B, SHAPE) returns a subsection of the 2-D
convolution with size specified by SHAPE:
'full' - (default) returns the full 2-D convolution,
'same' - returns the central part of the convolution
that is the same size as A.
'valid' - returns only those parts of the convolution
that are computed without the zero-padded edges.
size(C) = max([ma-max(0,mb-1),na-max(0,nb-1)],0).

Note!
This function depends on two Cuda kernel files
conv2_float.cu and conv2_double.cu which needs to be compiled
to .ptx files using the nvcc compiler.

See also CONV2, NVCC

Example,
Load an image
I=im2double(imread('moon.tif'));
Create a Gaussian filtering kernel
H = fspecial('gaussian',[20 20],3);
Perform the convolution on the CPU
J = conv2(I,H);
Perform the convolution on the GPU
Jcuda = gpuconv2(I,H);
Show the results
figure,
imshow(J,[]); title('CPU filtering');
imshow(Jcuda,[]); title('GPU filtering');
imshow(Jcuda-J,[]); title('Difference');

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##### MATLAB Release
MATLAB 7.11 (R2010b)
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