MMStencil: Optimizing High-order Stencils on Multicore CPU using Matrix Unit
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866913942585999360 |
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| author | Wang, Yinuo Mao, Tianqi Gan, Lin Wan, Wubing Song, Zeyu Fu, Jiayu He, Lanke Wang, Wenqiang Yin, Zekun Xue, Wei Yang, Guangwen |
| author_facet | Wang, Yinuo Mao, Tianqi Gan, Lin Wan, Wubing Song, Zeyu Fu, Jiayu He, Lanke Wang, Wenqiang Yin, Zekun Xue, Wei Yang, Guangwen |
| contents | Matrix-accelerated stencil computation is a hot research topic, yet its application to three-dimensional (3D) high-order stencils and HPC remains underexplored. With the emergence of matrix units on multicore CPUs, we analyze matrix-based acceleration strategies and tailor an optimal approach for 3D high-order stencils. We introduce algorithmic optimizations based on SIMD and matrix units to address strided memory accesses, alignment conflicts, and redundant accesses. We propose memory optimizations to boost on-package memory efficiency, and a novel multi-thread parallelism paradigm to overcome data-sharing challenges caused by the absence of shared data caches. MMStencil sustains consistently high hardware utilization across diverse stencil shapes and dimensions. Our DMA-based inter-NUMA communication further mitigates NUMA effects and MPI limitations in hybrid parallelism. Combining all the innovations, MMStencil outperforms state-of-the-art libraries on Nvidia A100 GPGPU by up to 2.1x. Moreover, the performance improvements translate directly to real-world HPC applications and enable RTM applications to yield 1.8x speedup versus a highly optimized industrial Nvidia A100 GPGPU version. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11067 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MMStencil: Optimizing High-order Stencils on Multicore CPU using Matrix Unit Wang, Yinuo Mao, Tianqi Gan, Lin Wan, Wubing Song, Zeyu Fu, Jiayu He, Lanke Wang, Wenqiang Yin, Zekun Xue, Wei Yang, Guangwen Distributed, Parallel, and Cluster Computing Matrix-accelerated stencil computation is a hot research topic, yet its application to three-dimensional (3D) high-order stencils and HPC remains underexplored. With the emergence of matrix units on multicore CPUs, we analyze matrix-based acceleration strategies and tailor an optimal approach for 3D high-order stencils. We introduce algorithmic optimizations based on SIMD and matrix units to address strided memory accesses, alignment conflicts, and redundant accesses. We propose memory optimizations to boost on-package memory efficiency, and a novel multi-thread parallelism paradigm to overcome data-sharing challenges caused by the absence of shared data caches. MMStencil sustains consistently high hardware utilization across diverse stencil shapes and dimensions. Our DMA-based inter-NUMA communication further mitigates NUMA effects and MPI limitations in hybrid parallelism. Combining all the innovations, MMStencil outperforms state-of-the-art libraries on Nvidia A100 GPGPU by up to 2.1x. Moreover, the performance improvements translate directly to real-world HPC applications and enable RTM applications to yield 1.8x speedup versus a highly optimized industrial Nvidia A100 GPGPU version. |
| title | MMStencil: Optimizing High-order Stencils on Multicore CPU using Matrix Unit |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2507.11067 |