Parallel Sparse Matrix-Vector Multiplication Using Accelerators

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• 关键词：SpMV ; Accelerator ; GPU ; MIC ; Cluster
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9787
• 期：1
• 页码：3-18
• 全文大小：1,232 KB
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• 作者单位：Hiroshi Maeda (22)
  Daisuke Takahashi (23)
  
  22. Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan
  23. Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
  
• 丛书名：Computational Science and Its Applications – ICCSA 2016
• ISBN：978-3-319-42108-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9787

文摘

Sparse matrix-vector multiplication (SpMV) is an essential computational kernel for many applications such as scientific computing. Recently, the number of computing systems equipped with NVIDIA’s GPU and Intel’s Xeon Phi coprocessor based on the MIC architecture has been increasing. Therefore, the importance of effective algorithms for SpMV in these systems is increasing. To the best of our knowledge, while previous studies have reported CPU and GPU implementations of SpMV for a cluster and MIC implementations for a single node, implementations of SpMV for the MIC cluster have not yet been reported. In this paper, we implemented and evaluated parallel SpMV on a GPU cluster and a MIC cluster. As shown by the results, the implementation for MIC achieved relatively high performance in some matrices with a single process, but it could not achieve higher performance than other implementations with 64 MPI processes. Therefore, we implemented and evaluated the single SpMV kernel to improve the performance of parallel SpMV.