Analysis of the Performance of the Matrix Multiplication Algorithm on the Cirrus Supercomputer
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916373071921152 |
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| author | Adefemi, Temitayo |
| author_facet | Adefemi, Temitayo |
| contents | Matrix multiplication is integral to various scientific and engineering disciplines, including machine learning, image processing, and gaming. With the increasing data volumes in areas like machine learning, the demand for efficient parallel processing of large matrices has grown significantly.This study explores the performance of both serial and parallel matrix multiplication on the Cirrus supercomputer at the University of Edinburgh. The results demonstrate the scalability and efficiency of these methods, providing insights for optimizing matrixmultiplication in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15384 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysis of the Performance of the Matrix Multiplication Algorithm on the Cirrus Supercomputer Adefemi, Temitayo Distributed, Parallel, and Cluster Computing Matrix multiplication is integral to various scientific and engineering disciplines, including machine learning, image processing, and gaming. With the increasing data volumes in areas like machine learning, the demand for efficient parallel processing of large matrices has grown significantly.This study explores the performance of both serial and parallel matrix multiplication on the Cirrus supercomputer at the University of Edinburgh. The results demonstrate the scalability and efficiency of these methods, providing insights for optimizing matrixmultiplication in real-world applications. |
| title | Analysis of the Performance of the Matrix Multiplication Algorithm on the Cirrus Supercomputer |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2408.15384 |