An Auto-tuning Method for Run-time Data Transformation for Sparse Matrix-Vector Multiplication
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917708745932800 |
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| author | Katagiri, Takahiro Sato, Masahiko |
| author_facet | Katagiri, Takahiro Sato, Masahiko |
| contents | In this paper, we research the run-time sparse matrix data transformation from Compressed Row Storage (CRS) to Coordinate (COO) storage and an ELL (ELLPACK/ITPACK) format with OpenMP parallelization for sparse matrix-vector multiplication (SpMV). We propose an auto-tuning (AT) method by using the $D_{mat}^i$ - $R_{ell}^i$ graph, which plots the derivation/average for the number of non-zero elements per row ($D_{mat}^i$) and the ratio, SpMV speedups/transformation time from the CRS to ELL ($R_{ell}^i$ ). The experimental results show the ELL format is very effective in the Earth Simulator 2. The speedup factor of 151 with the ELL-Row inner-parallelized format is obtained. The transformation overhead is also very small, such as 0.01 to 1.0 SpMV time with the CRS format. In addition, the $D_{mat}^i$ - $R_{ell}^i$ graph can be modeled for the effectiveness of transformation according to the $D_{mat}^i$ value. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_00019 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Auto-tuning Method for Run-time Data Transformation for Sparse Matrix-Vector Multiplication Katagiri, Takahiro Sato, Masahiko Distributed, Parallel, and Cluster Computing Performance In this paper, we research the run-time sparse matrix data transformation from Compressed Row Storage (CRS) to Coordinate (COO) storage and an ELL (ELLPACK/ITPACK) format with OpenMP parallelization for sparse matrix-vector multiplication (SpMV). We propose an auto-tuning (AT) method by using the $D_{mat}^i$ - $R_{ell}^i$ graph, which plots the derivation/average for the number of non-zero elements per row ($D_{mat}^i$) and the ratio, SpMV speedups/transformation time from the CRS to ELL ($R_{ell}^i$ ). The experimental results show the ELL format is very effective in the Earth Simulator 2. The speedup factor of 151 with the ELL-Row inner-parallelized format is obtained. The transformation overhead is also very small, such as 0.01 to 1.0 SpMV time with the CRS format. In addition, the $D_{mat}^i$ - $R_{ell}^i$ graph can be modeled for the effectiveness of transformation according to the $D_{mat}^i$ value. |
| title | An Auto-tuning Method for Run-time Data Transformation for Sparse Matrix-Vector Multiplication |
| topic | Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2407.00019 |