Fredrik Kjolstad, Stephen Chou, D. Lugato, S. Kamil, Saman P. Amarasinghe
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引用次数: 37
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.
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