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Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU
- Publication Year :
- 2023
-
Abstract
- We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra. Whereas contemporary decompositions are primarily tile-based, our method operates by partitioning an even share of the aggregate inner loop iterations among physical processing elements. This provides a near-perfect utilization of computing resources, regardless of how efficiently the output tiling for any given problem quantizes across the underlying processing elements. On GPU processors, our Stream-K parallelization of GEMM produces a peak speedup of up to 14$\times$ and 6.7$\times$, and an average performance response that is both higher and more consistent across 32,824 GEMM problem geometries than state-of-the-art math libraries such as CUTLASS and cuBLAS. Furthermore, we achieve this performance from a single tile size configuration per floating-point precision, whereas today's math libraries employ complex kernel-selection heuristics to select from a large ensemble of kernel variants.<br />Comment: This work previously appeared in the author's PhD dissertation, available at arXiv:2212.08964
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2301.03598
- Document Type :
- Working Paper