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Unweaving warp specialization on modern tensor core GPUs
Recently, I have been thinking deeply about warp specialization in the context of high performance kernels for modern Tensor Core GPUs like NVIDIA’s H100 and B200. My understanding of what warp specialization achieves has deepened and led me to the interesting question of: do we actually need warp specialization (and the complexity that it entails)? My conclusion is that the answer is indeed yes, but it might not be as mandatory as it seems.
The complexity of this Flash Attention implementation inspired me to take a step back and investigate the role of warp specialization in achieving high performance with the Tensor Cores. An SM has some fixed number of compute resources available (i.e. ALU’s, LSU’s, a Tensor Core) and issue slots per clock cycle, regardless of how many warps a thread block uses. For example, a GEMM implementation on the Ampere architecture has similar complications as H100 around asynchronous and variable latency load instructions, but NVIDIA engineers found that high performance was achievable with acceptable complexity without warp specialization.
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