Automatic generation of efficient sparse tensor format conversion routines
Name
2001.02609.pdf
Description
Accepted version
Size
1.04 MB
Format
Unknown
Checksum (MD5)
1407435dc74945142ccec6c0c5ef2158
Author(s)
Chou, Stephen
Kjolstad, Fredrik
Amarasinghe, Saman P
Date Issued
2020
Journal
Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)
Publisher
ACM
Version
Author's final manuscript
Abstract
© 2020 Owner/Author. This paper shows how to generate code that efficiently converts sparse tensors between disparate storage formats (data layouts) such as CSR, DIA, ELL, and many others. We decompose sparse tensor conversion into three logical phases: coordinate remapping, analysis, and assembly. We then develop a language that precisely describes how different formats group together and order a tensor's nonzeros in memory. This lets a compiler emit code that performs complex remappings of nonzeros when converting between formats. We also develop a query language that can extract statistics about sparse tensors, and we show how to emit efficient analysis code that computes such queries. Finally, we define an abstract interface that captures how data structures for storing a tensor can be efficiently assembled given specific statistics about the tensor. Disparate formats can implement this common interface, thus letting a compiler emit optimized sparse tensor conversion code for arbitrary combinations of many formats without hard-coding for any specific combination. Our evaluation shows that the technique generates sparse tensor conversion routines with performance between 1.00 and 2.01× that of hand-optimized versions in SPARSKIT and Intel MKL, two popular sparse linear algebra libraries. And by emitting code that avoids materializing temporaries, which both libraries need for many combinations of source and target formats, our technique outperforms those libraries by 1.78 to 4.01× for CSC/COO to DIA/ELL conversion.
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/10.1145/3385412.3385963
