Compiler Support for Sparse Tensor Computations in MLIR [article]

Aart J.C. Bik, Penporn Koanantakool, Tatiana Shpeisman, Nicolas Vasilache, Bixia Zheng, Fredrik Kjolstad
2022 arXiv   pre-print
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
more » ... inition of the computation. This paper discusses integrating this idea into MLIR.
arXiv:2202.04305v1 fatcat:6qijxmernbev7pwmt6lgqdvziy