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SparseAuto: An Auto-scheduler for Sparse Tensor Computations using Recursive Loop Nest Restructuring

Authors:

Logan Anderson,

Kirshanthan Sundararajah,

Artem Pelenitsyn,

Milind KulkarniAuthors Info & Claims

Proceedings of the ACM on Programming Languages, Volume 8, Issue OOPSLA2

Article No.: 290, Pages 527 - 556

https://doi.org/10.1145/3689730

Published: 08 October 2024 Publication History

Abstract

Automated code generation and performance enhancements for sparse tensor algebra have become essential in many real-world applications, such as quantum computing, physical simulations, computational chemistry, and machine learning. General sparse tensor algebra compilers are not always versatile enough to generate asymptotically optimal code for sparse tensor contractions. This paper shows how to generate asymptotically better schedules for complex sparse tensor expressions using kernel fission and fusion. We present generalized loop restructuring transformations to reduce asymptotic time complexity and memory footprint. Furthermore, we present an auto-scheduler that uses a partially ordered set (poset)-based cost model that uses both time and auxiliary memory complexities to prune the search space of schedules. In addition, we highlight the use of Satisfiability Module Theory (SMT) solvers in sparse auto-schedulers to approximate the Pareto frontier of better schedules to the smallest number of possible schedules, with user-defined constraints available at compile-time. Finally, we show that our auto-scheduler can select better-performing schedules and generate code for them. Our results show that the auto-scheduler provided schedules achieve orders-of-magnitude speedup compared to the code generated by the Tensor Algebra Compiler (TACO) for several computations on different real-world tensors.

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Cited By

Kanakagiri RSolomonik EAgrawal KPetrank E(2024)Minimum Cost Loop Nests for Contraction of a Sparse Tensor with a Tensor NetworkProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3659985(169-181)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3626183.3659985

Index Terms

SparseAuto: An Auto-scheduler for Sparse Tensor Computations using Recursive Loop Nest Restructuring
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
      1. Source code generation
    2. Context specific languages
      1. Domain specific languages

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cover image Proceedings of the ACM on Programming Languages

Proceedings of the ACM on Programming Languages Volume 8, Issue OOPSLA2

October 2024

2691 pages

EISSN:2475-1421

DOI:10.1145/3554319

Editor:
Michael Hicks
Amazon, USA

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Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Published: 08 October 2024

Published in PACMPL Volume 8, Issue OOPSLA2

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Cited By

Kanakagiri RSolomonik EAgrawal KPetrank E(2024)Minimum Cost Loop Nests for Contraction of a Sparse Tensor with a Tensor NetworkProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3659985(169-181)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3626183.3659985

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