Design and Implementation of Adaptive SpMV Library for Multicore and Many-Core Architecture

doi:10.1145/3218823

	Design and Implementation of Adaptive SpMV Library for Multicore and Many-Core Architecture
	Tan, Guangming 1,3; Liu, Junhong 1,3; Li, Jiajia 2
	2018-08-01
发表期刊	ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
ISSN	0098-3500
卷号	44 期号:4 页码:25
摘要	Sparse matrix vector multiplication (SpMV) is an important computational kernel in traditional highperformance computing and emerging data-intensive applications. Previous SpMV libraries are optimized by either application-specific or architecture-specific approaches but present difficulties for use in real applications. In this work, we develop an auto-tuning system (SMATER) to bridge the gap between specific optimizations and general-purpose use. SMATER provides programmers a unified interface based on the compressed sparse row (CSR) sparse matrix format by implicitly choosing the best format and fastest implementation for any input sparse matrix during runtime. SMATER leverages a machine-learning model and retargetable back-end library to quickly predict the optimal combination. Performance parameters are extracted from 2,386 matrices in the SuiteSparse matrix collection. The experiments show that SMATER achieves good performance (up to 10 times that of the Intel Math Kernel Library (MKL) on Intel E5-2680 v3) while being portable on state-of-the-art x86 multicore processors, NVIDIA GPUs, and Intel Xeon Phi accelerators. Compared with the Intel MKL library, SMATER runs faster by more than 2.5 times on average. We further demonstrate its adaptivity in an algebraic multigrid solver from the Hypre library and report greater than 20% performance improvement.
关键词	Sparse matrix vector multiplication auto-tuning multicore machine learning
DOI	10.1145/3218823
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFB0201305] ; National Key Research and Development Program of China[2016YFB0200504] ; National Key Research and Development Program of China[2017YFB0202105] ; 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]
WOS研究方向	Computer Science ; Mathematics
WOS类目	Computer Science, Software Engineering ; Mathematics, Applied
WOS记录号	WOS:000445637100010
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：29[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4937
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Tan, Guangming
作者单位	1.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China 2.Georgia Inst Technol, Computat Sci & Engn, Atlanta, GA 30332 USA 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Tan, Guangming,Liu, Junhong,Li, Jiajia. Design and Implementation of Adaptive SpMV Library for Multicore and Many-Core Architecture[J]. ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE,2018,44(4):25.
APA	Tan, Guangming,Liu, Junhong,&Li, Jiajia.(2018).Design and Implementation of Adaptive SpMV Library for Multicore and Many-Core Architecture.ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE,44(4),25.
MLA	Tan, Guangming,et al."Design and Implementation of Adaptive SpMV Library for Multicore and Many-Core Architecture".ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 44.4(2018):25.