TY - GEN

T1 - WISE

T2 - 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

AU - Yesil, Serif

AU - Heidarshenas, Azin

AU - Morrison, Adam

AU - Torrellas, Josep

N1 - Publisher Copyright:
© 2023 ACM.

PY - 2023/2/25

Y1 - 2023/2/25

N2 - 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.

AB - 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.

KW - SpMV

KW - machine learning

KW - sparse matrix

UR - http://www.scopus.com/inward/record.url?scp=85149318937&partnerID=8YFLogxK

U2 - 10.1145/3572848.3577506

DO - 10.1145/3572848.3577506

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85149318937

T3 - Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP

SP - 329

EP - 341

BT - PPoPP 2023 - Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

PB - Association for Computing Machinery

Y2 - 25 February 2023 through 1 March 2023

ER -