The weird part of the programming model is that threadblocks don't map 1:1 to warps or SMs. A single threadblock executes on a single SM, but each SM has multiple warps, and the threadblock could be the size of a single warp, or larger than the combined thread count of all warps in the SM.
So, how large do you make your threadblocks to get optimal SM/warp scheduling? Well it "depends" based on resource usage, divergence, etc. Basically run it, profile, switch the threadblock size, profile again, etc. Repeat on every GPU/platform (if you're programming for multiple GPU platforms and not just CUDA, like games do). It's a huge pain, and very sensitive to code changes.
People new to GPU programming ask me "how big do I make the threadblock size?" and I tell them go with 64 or 128 to start, and then profile and adjust as needed.
Two articles on the AMD side of things:
https://gpuopen.com/learn/occupancy-explained
https://gpuopen.com/learn/optimizing-gpu-occupancy-resource-...
So, how large do you make your threadblocks to get optimal SM/warp scheduling? Well it "depends" based on resource usage, divergence, etc. Basically run it, profile, switch the threadblock size, profile again, etc. Repeat on every GPU/platform (if you're programming for multiple GPU platforms and not just CUDA, like games do). It's a huge pain, and very sensitive to code changes.
People new to GPU programming ask me "how big do I make the threadblock size?" and I tell them go with 64 or 128 to start, and then profile and adjust as needed.
Two articles on the AMD side of things:
https://gpuopen.com/learn/occupancy-explained
https://gpuopen.com/learn/optimizing-gpu-occupancy-resource-...