Compiler Support for Sparse Tensor Computations in MLIR
Sparse tensors arise in problems in science, engineering, machine learning, and data analytics. Programs that operate on such tensors can exploit sparsity to reduce storage requirements and computational time. Developing and maintaining sparse software by hand, however, is a complex and error-prone...
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| Veröffentlicht in: | arXiv.org 2022-02 |
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| creator | Bik, Aart J C Koanantakool, Penporn Shpeisman, Tatiana Vasilache, Nicolas Zheng, Bixia Kjolstad, Fredrik |
| description | Sparse tensors arise in problems in science, engineering, machine learning, and data analytics. Programs that operate on such tensors can exploit sparsity to reduce storage requirements and computational time. Developing and maintaining sparse software by hand, however, is a complex and error-prone task. Therefore, we propose treating sparsity as a property of tensors, not a tedious implementation task, and letting a sparse compiler generate sparse code automatically from a sparsity-agnostic definition of the computation. This paper discusses integrating this idea into MLIR. |
| doi_str_mv | 10.48550/arxiv.2202.04305 |
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| subjects | Compilers Computer Science - Programming Languages Computing time Machine learning Mathematical analysis Sparsity Tensors |
| title | Compiler Support for Sparse Tensor Computations in MLIR |
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