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arXiv · 2407.00044

Stream-K Optimization and Exploration

Nick Rackley·Bryan Gonzalez·Casey Morrison
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Abstract

We explore optimization options for the Stream-K algorithm, a work-centric parallelization of matrix multiplication (GEMM). In our study, we investigated differences between the theoretical and practical implementations, particularly noting the impact of padding. Our debugging efforts revealed a persistent bug related to block mapping, which we could not fully resolve, but we managed to implement some optimizations. Setting the padding to zero for the M, N, and K dimensions resulted in an average 0.6% improvement in performance, achieving 1.44 ms, 89.37 TFlops, and 66.91 GB/s. However, adjusting the block size and parameters led to the process getting stuck, indicating a need for further tuning. Additionally, exploring the potential of Block2Time highlighted its promise in enhancing runtime predictions and optimizing load balancing.

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Nick Rackley, Bryan Gonzalez, Casey Morrison. 2024-06-01. Stream-K Optimization and Exploration. https://arxiv.org/abs/2407.00044

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