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 - 2023/2/11
Y1 - 2023/2/11
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 - https://www.scopus.com/pages/publications/85149318937
U2 - 10.1145/3572848.3577506
DO - 10.1145/3572848.3577506
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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 -