LRAMM -- Low precision approximates GEMM via RSVD
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929359817801728 |
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| author | Gu, Hongyaoxing |
| author_facet | Gu, Hongyaoxing |
| contents | Matrix multiplication computation acceleration has been a research hotspot across various domains. Due to the characteristics of some applications, approximate matrix multiplication can achieve significant performance improvements without losing much precision.
In this paper, we propose LRAMM - a high-performance matrix multiplication approximation algorithm that combines mixed-precision quantized matrix multiplication with RSVD techniques, further enhancing efficiency within the error range of low-precision matrix multiplication by utilizing matrix low-rank decomposition technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16917 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LRAMM -- Low precision approximates GEMM via RSVD Gu, Hongyaoxing Numerical Analysis Performance Matrix multiplication computation acceleration has been a research hotspot across various domains. Due to the characteristics of some applications, approximate matrix multiplication can achieve significant performance improvements without losing much precision. In this paper, we propose LRAMM - a high-performance matrix multiplication approximation algorithm that combines mixed-precision quantized matrix multiplication with RSVD techniques, further enhancing efficiency within the error range of low-precision matrix multiplication by utilizing matrix low-rank decomposition technology. |
| title | LRAMM -- Low precision approximates GEMM via RSVD |
| topic | Numerical Analysis Performance |
| url | https://arxiv.org/abs/2405.16917 |