Serif Yesil, Azin Heidarshenas, Adam Morrison, J. Torrellas
{"title":"明智的","authors":"Serif Yesil, Azin Heidarshenas, Adam Morrison, J. Torrellas","doi":"10.1145/3572848.3577506","DOIUrl":null,"url":null,"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.","PeriodicalId":233744,"journal":{"name":"Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"WISE\",\"authors\":\"Serif Yesil, Azin Heidarshenas, Adam Morrison, J. Torrellas\",\"doi\":\"10.1145/3572848.3577506\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":233744,\"journal\":{\"name\":\"Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3572848.3577506\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3572848.3577506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.