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What’s the most efficient way to do image pyramiding in CUDA? I have written my own kernels to do so but imagine we can do better.
Binding to an OpenGL texture using OpenGL interop and using the hardware mipmapping would probably be much faster. Any pointers on how to do this or other
MipMaps are setup when accessed/initialized in OpenGL/DirectX. A CUDA kernel can do the same thing if you allocate a texture 50% wider (or higher) than the initial texture and use the kernel to down-sample the texture and write the result beside the original texture. The kernel will probably work best where each thread evaluates a pixel in the next down-sampled image. It’s up to you to determine the sampling-scheme and choose appropriate weights for combining the pixels. Try bilinear to start with, then once it’s working you can setup trilinear (cubic) or other sampling schemes like anisotropic etc. Simple sampling (linear and cubic) will likely be more efficient since coalesced memory access will occur (refer to the CUDA SDK programming guide). You will probably need to tile the kernel execution since the thread-count is limited for parallel invokation (too many pixels, too few threads = use tiling to chunk parallel execution).You might find Mesa3D useful as a reference (it’s an open-source implementation of OpenGL).