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Compiler Support for Sparse Tensor Computations in MLIR
[article]
2022
arXiv
pre-print
Preserved Fulltext
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
arXiv:2202.04305v1
fatcat:6qijxmernbev7pwmt6lgqdvziy