GPUCONV2 1.0

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');

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Compiled,  Compiler,  Conv,  Create,  Files,  Gaussian,  Image,  Kernel
Users rating:	0/10