A New Sparse GEneral Matrix-matrix Multiplication Method for Long Vector Architecture by Hierarchical Row Merging
Pages 756 - 759
Abstract
Vector processors have become essential to high-performance computing in scientific and engineering applications, especially in numerical calculations that leverage data parallelism. With escalating computational demands, the efficient execution of Sparse GEneral Matrix-matrix Multiplication (SpGEMM) on vector processors has become crucial. However, it brings challenges for vector processors due to its complex data structures and irregular memory access patterns. This paper presents a new method designed to perform SpGEMM on vector processors, inspired by Iterative Row Merging. The proposed method hierarchically merges rows by utilizing long vector instructions. We evaluate the proposed method against other methods across 27 sparse matrices. The results indicate that the proposed method outperforms other methods for 22 out of the 27 sparse matrices, reaching up to 31.9 times better performance in the best case.
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- A New Sparse GEneral Matrix-matrix Multiplication Method for Long Vector Architecture by Hierarchical Row Merging
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Published In
November 2023
2180 pages
ISBN:9798400707858
DOI:10.1145/3624062
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Published: 12 November 2023
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SC-W 2023
SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
November 12 - 17, 2023
CO, Denver, USA
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