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A Pattern-Based SpGEMM Library for Multi-Core and Many-Core Architectures

文献类型：期刊论文

作者Xie, Zhen2,3; Tan, Guangming2,3; Liu, Weifeng1; Sun, Ninghui2,3
刊名IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
出版日期2022
卷号33期号:1页码:159-175
ISSN号1045-9219
关键词Libraries Sparse matrices Prediction algorithms Neural networks Predictive models Memory management Tuners SpGEMM spare BLAS sparse format auto-tuning neural network
DOI10.1109/TPDS.2021.3090328
英文摘要General sparse matrix-matrix multiplication (SpGEMM) is one of the most important mathematical library routines in a number of applications. In recent years, several efficient SpGEMM algorithms have been proposed, however, most of them are based on the compressed sparse row (CSR) format, and the possible performance gain from exploiting other formats has not been well studied. And some specific algorithms are restricted to parameter tuning that has a significant impact on performance. So the particular format, algorithm, and parameter that yield the best performance for SpGEMM remain undetermined. In this article, we conduct a prospective study on format-specific parallel SpGEMM algorithms and analyze their pros and cons. We then propose a pattern-based SpGEMM library, that provides a unified programming interface in the CSR format, analyses the pattern of two input matrices, and automatically determines the best format, algorithm, and parameter for arbitrary matrix pairs. For this purpose, we build an algorithm set that integrates three new designed algorithms with existing popular libraries, and design a hybrid deep learning model called MatNet to quickly identify patterns of input matrices and accurately predict the best solution by using sparse features and density representations. The evaluation shows that this library consistently outperforms the state-of-the-art library. We also demonstrate its adaptability in an AMG solver and a BFS algorithm with 30 percent performance improvement.
资助项目National Key Research and Development Program of China[2017YFB0202105] ; National Key Research and Development Program of China[2016YFB0201305] ; National Key Research and Development Program of China[2016YFB0200803] ; National Key Research and Development Program of China[2016YFB0200300] ; National Natural Science Foundation of China[61521092] ; National Natural Science Foundation of China[91430218] ; National Natural Science Foundation of China[31327901] ; National Natural Science Foundation of China[61472395] ; National Natural Science Foundation of China[61432018] ; National Natural Science Foundation of China[61671151]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:000673452600001
源URL[http://119.78.100.204/handle/2XEOYT63/17499]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xie, Zhen
作者单位1.China Univ Petr, Dept Comp Sci & Technol, Beijing 102249, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100864, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100864, Peoples R China
推荐引用方式
GB/T 7714		 Xie, Zhen,Tan, Guangming,Liu, Weifeng,et al. A Pattern-Based SpGEMM Library for Multi-Core and Many-Core Architectures[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2022,33(1):159-175.
APA Xie, Zhen,Tan, Guangming,Liu, Weifeng,&Sun, Ninghui.(2022).A Pattern-Based SpGEMM Library for Multi-Core and Many-Core Architectures.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,33(1),159-175.
MLA Xie, Zhen,et al."A Pattern-Based SpGEMM Library for Multi-Core and Many-Core Architectures".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 33.1(2022):159-175.

入库方式： OAI收割

来源：计算技术研究所

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