I've been experimenting with CUDA kernels for days to perform a fast 2D convolution between a 500x500 image (but I could also vary the dimensions) and a very small 2D kernel (a laplacian 2d kernel, so it's a 3x3 kernel.. too small to take a huge advantage with all the cuda threads). I created a CPU classic implementation (two for loops, as easy as you would think) and then I started creating CUDA kernels. After a few disappointing attempts to perform a faster convolution I ended up with this code: http://www.evl.uic.edu/sjames/cs525/final.html (see the Shared Memory section), it basically lets a 16x16 threads block load all the convolution data he needs in the shared memory and then performs the convolution. Nothing, the CPU is still a lot faster. I didn't try the FFT approach because the CUDA SDK states that it is efficient with large kernel sizes. Whether or not you read everything I wrote, my question is: how can I perform a fast 2D convolution between a relatively large image and a very small kernel (3x3) with CUDA?