Design and Implementation of Adaptive SpMV Library for Multicore and Many-Core Architecture
文献类型：期刊论文
| 作者 | Tan, Guangming2,3; Liu, Junhong2,3; Li, Jiajia1 |
| 刊名 | ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
 |
| 出版日期 | 2018-08-01 |
| 卷号 | 44期号:4页码:25 |
| 关键词 | Sparse matrix vector multiplication auto-tuning multicore machine learning |
| ISSN号 | 0098-3500 |
| DOI | 10.1145/3218823 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000445637100010 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/4936]  |
| 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.Georgia Inst Technol, Computat Sci & Engn, Atlanta, GA 30332 USA 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, 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. |
入库方式： OAI收割
来源：计算技术研究所
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