The Ubiquitous Sparse Matrix-Matrix Products
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915430490177536 |
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| author | Buluç, Aydın |
| author_facet | Buluç, Aydın |
| contents | Multiplication of a sparse matrix with another (dense or sparse) matrix is a fundamental operation that captures the computational patterns of many data science applications, including but not limited to graph algorithms, sparsely connected neural networks, graph neural networks, clustering, and many-to-many comparisons of biological sequencing data.
In many application scenarios, the matrix multiplication takes places on an arbitrary algebraic semiring where the scalar operations are overloaded with user-defined functions with certain properties or a more general heterogenous algebra where even the domains of the input matrices can be different. Here, we provide a unifying treatment of the sparse matrix-matrix operation and its rich application space including machine learning, computational biology and chemistry, graph algorithms, and scientific computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04077 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Ubiquitous Sparse Matrix-Matrix Products Buluç, Aydın Numerical Analysis Distributed, Parallel, and Cluster Computing Machine Learning Mathematical Software Combinatorics Multiplication of a sparse matrix with another (dense or sparse) matrix is a fundamental operation that captures the computational patterns of many data science applications, including but not limited to graph algorithms, sparsely connected neural networks, graph neural networks, clustering, and many-to-many comparisons of biological sequencing data. In many application scenarios, the matrix multiplication takes places on an arbitrary algebraic semiring where the scalar operations are overloaded with user-defined functions with certain properties or a more general heterogenous algebra where even the domains of the input matrices can be different. Here, we provide a unifying treatment of the sparse matrix-matrix operation and its rich application space including machine learning, computational biology and chemistry, graph algorithms, and scientific computing. |
| title | The Ubiquitous Sparse Matrix-Matrix Products |
| topic | Numerical Analysis Distributed, Parallel, and Cluster Computing Machine Learning Mathematical Software Combinatorics |
| url | https://arxiv.org/abs/2508.04077 |