@inproceedings{b7a69bea52a745fbbb6abac81e016308,

title = "WISE: Predicting the Performance of Sparse Matrix Vector Multiplication with Machine Learning",

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.",

keywords = "SpMV, machine learning, sparse matrix",

author = "Serif Yesil and Azin Heidarshenas and Adam Morrison and Josep Torrellas",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023 ; Conference date: 25-02-2023 Through 01-03-2023",

year = "2023",

month = feb,

day = "25",

doi = "10.1145/3572848.3577506",

language = "English (US)",

series = "Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP",

publisher = "Association for Computing Machinery",

pages = "329--341",

booktitle = "PPoPP 2023 - Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming",

address = "United States",

}