Taco: A tool to generate tensor algebra kernels
Name
taco-tools.pdf
Description
Accepted version
Size
371.53 KB
Format
Unknown
Checksum (MD5)
c365d95a0404db5cd750a291263a82be
Author(s)
Kjolstad, Fredrik
Chou, Stephen
Lugato, David
Kamil, Shoaib
Amarasinghe, Saman P
Date Issued
October 2017
Publisher
IEEE
Citation
Kjolstad, Fredrik, Chou, Stephen, Lugato, David, Kamil, Shoaib and Amarasinghe, Saman. 2017. "Taco: A tool to generate tensor algebra kernels."
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ase.2017.8115709
