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Can I read Compiler Support for Sparse Tensor Computations in MLIR on EtoBox?
Compiler Support for Sparse Tensor Computations in MLIR by Aart Bik; Penporn Koanantakool; Tatiana Shpeisman; Nicolas Vasilache; Bixia Zheng; Fredrik Kjolstad is a Computer Science article available to read on EtoBox.
What is Compiler Support for Sparse Tensor Computations in MLIR about?
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 article discusses integrating this idea into MLIR.
Who reads Compiler Support for Sparse Tensor Computations in MLIR?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Aart Bik; Penporn Koanantakool; Tatiana Shpeisman; Nicolas Vasilache; Bixia Zheng; Fredrik Kjolstad
- Publisher
- ACM
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)