WISE: Predicting the Performance of Sparse Matrix Vector Multiplication with Machine Learning

Serif Yesil, Azin Heidarshenas, Adam Morrison, Josep Torrellas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse kernel. Numerous methods have been developed to accelerate SpMV. However, no single method consistently gives the highest performance across a wide range of matrices. For this reason, a performance prediction model is needed to predict the best SpMV method for a given sparse matrix. Unfortunately, predicting SpMV's performance is challenging due to the diversity of factors that impact it. In this work, we develop a machine learning framework called WISE that accurately predicts the magnitude of the speedups of different SpMV methods over a baseline method for a given sparse matrix. WISE relies on a novel feature set that summarizes a matrix's size, skew, and locality traits. WISE can then select the best SpMV method for each specific matrix. With a set of nearly 1,500 matrices, we show that using WISE delivers an average speedup of 2.4× over using Intel's MKL in a 24-core server.

Original languageEnglish
Title of host publicationPPoPP 2023 - Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
PublisherAssociation for Computing Machinery
Pages329-341
Number of pages13
ISBN (Electronic)9798400700156
DOIs
StatePublished - 25 Feb 2023
Event28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023 - Montreal, Canada
Duration: 25 Feb 20231 Mar 2023

Publication series

NameProceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP

Conference

Conference28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023
Country/TerritoryCanada
CityMontreal
Period25/02/231/03/23

Keywords

  • SpMV
  • machine learning
  • sparse matrix

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