Verification Challenges in Sparse Matrix Vector Multiplication in High Performance Computing: Part I
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909847659741184 |
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| author | Zhang, Junchao |
| author_facet | Zhang, Junchao |
| contents | Sparse matrix vector multiplication (SpMV) is a fundamental kernel in scientific codes that rely on iterative solvers. In this first part of our work, we present both a sequential and a basic MPI parallel implementations of SpMV, aiming to provide a challenge problem for the scientific software verification community. The implementations are described in the context of the PETSc library. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13427 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Verification Challenges in Sparse Matrix Vector Multiplication in High Performance Computing: Part I Zhang, Junchao Logic in Computer Science Distributed, Parallel, and Cluster Computing Mathematical Software Sparse matrix vector multiplication (SpMV) is a fundamental kernel in scientific codes that rely on iterative solvers. In this first part of our work, we present both a sequential and a basic MPI parallel implementations of SpMV, aiming to provide a challenge problem for the scientific software verification community. The implementations are described in the context of the PETSc library. |
| title | Verification Challenges in Sparse Matrix Vector Multiplication in High Performance Computing: Part I |
| topic | Logic in Computer Science Distributed, Parallel, and Cluster Computing Mathematical Software |
| url | https://arxiv.org/abs/2510.13427 |