ARM SVE Unleashed: Performance and Insights Across HPC Applications on Nvidia Grace
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908363455987712 |
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| author | Shi, Ruimin Schieffer, Gabin Gokhale, Maya Lin, Pei-Hung Patel, Hiren Peng, Ivy |
| author_facet | Shi, Ruimin Schieffer, Gabin Gokhale, Maya Lin, Pei-Hung Patel, Hiren Peng, Ivy |
| contents | Vector architectures are essential for boosting computing throughput. ARM provides SVE as the next-generation length-agnostic vector extension beyond traditional fixed-length SIMD. This work provides a first study of the maturity and readiness of exploiting ARM and SVE in HPC. Using selected performance hardware events on the ARM Grace processor and analytical models, we derive new metrics to quantify the effectiveness of exploiting SVE vectorization to reduce executed instructions and improve performance speedup. We further propose an adapted roofline model that combines vector length and data elements to identify potential performance bottlenecks. Finally, we propose a decision tree for classifying the SVE-boosted performance in applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09462 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARM SVE Unleashed: Performance and Insights Across HPC Applications on Nvidia Grace Shi, Ruimin Schieffer, Gabin Gokhale, Maya Lin, Pei-Hung Patel, Hiren Peng, Ivy Distributed, Parallel, and Cluster Computing Vector architectures are essential for boosting computing throughput. ARM provides SVE as the next-generation length-agnostic vector extension beyond traditional fixed-length SIMD. This work provides a first study of the maturity and readiness of exploiting ARM and SVE in HPC. Using selected performance hardware events on the ARM Grace processor and analytical models, we derive new metrics to quantify the effectiveness of exploiting SVE vectorization to reduce executed instructions and improve performance speedup. We further propose an adapted roofline model that combines vector length and data elements to identify potential performance bottlenecks. Finally, we propose a decision tree for classifying the SVE-boosted performance in applications. |
| title | ARM SVE Unleashed: Performance and Insights Across HPC Applications on Nvidia Grace |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.09462 |