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2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) (2017)
Urbana, IL, USA
Oct. 30, 2017 to Nov. 3, 2017
ISBN: 978-1-5386-3976-4
pp: 943-948
Fredrik Kjolstad , MIT CSAIL, USA
Stephen Chou , MIT CSAIL, USA
David Lugato , CEA, France
Shoaib Kamil , Adobe, USA
Saman Amarasinghe , MIT CSAIL, USA
ABSTRACT
Tensor algebra is an important computational abstraction that is increasingly used in data analytics, machine learning, engineering, and the physical sciences. However, the number of tensor expressions is unbounded, which makes it hard to develop and optimize libraries. Furthermore, the tensors are often sparse (most components are zero), which means the code has to traverse compressed formats. To support programmers we have developed taco, a code generation tool that generates dense, sparse, and mixed kernels from tensor algebra expressions. This paper describes the taco web and command-line tools and discusses the benefits of a code generator over a traditional library. See also the demo video at tensor-compiler.org/ase2017.
INDEX TERMS
Tensile stress, Tools, Kernel, Indexes, Libraries, Linear algebra
CITATION

F. Kjolstad, S. Chou, D. Lugato, S. Kamil and S. Amarasinghe, "Taco: A tool to generate tensor algebra kernels," 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana, IL, USA, 2017, pp. 943-948.
doi:10.1109/ASE.2017.8115709
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