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Title:
Performance testing and analysis of MAGMA library on hybrid architecture (CPU+GPU)
Author: Xiao, Xuan-Ji (1) ; Zhang, Yun-Quan (1) ; Li, Yu-Cheng (1) ; Yuan, Liang (1)
Corresponding Author: Xiao, X.-J.(growj@126.com)
Source: Ruan Jian Xue Bao/Journal of Software
Issued Date: 2013
Volume: 24, Issue:SUPPL.2, Pages:118-126
Indexed Type: EI
Department: (1) Laboratory of Parallel Computing, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China; (2) State Key Laboratory of Computer Science, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China; (3) University of Chinese Academy of Sciences, Beijing 100190, China
Abstract: MAGMA is an open source high performance linear algebra package first developed for next-generation of heterogeneous/ hybrid architectures (CPUs+GPUs) with a dense linear algebra library similar to LAPACK in functionality, data storage, and interface. This paper presents performance testing and analysis of MAGMA. It first studies the matrix decomposition algorithm in MAGMA, then provides some useful suggestions of MAGMA usage and optimization through massive testing and source code analysis, and finally proposes a method for auto-tuning matrix decomposition block algorithms. In this test, the speedup is 1.09 for SGEQRF of square matrix and 1.8 for CGEQRF in terms of tall and skin matrix. © Institute of Software Chinese Academy of Sciences.
English Abstract: MAGMA is an open source high performance linear algebra package first developed for next-generation of heterogeneous/ hybrid architectures (CPUs+GPUs) with a dense linear algebra library similar to LAPACK in functionality, data storage, and interface. This paper presents performance testing and analysis of MAGMA. It first studies the matrix decomposition algorithm in MAGMA, then provides some useful suggestions of MAGMA usage and optimization through massive testing and source code analysis, and finally proposes a method for auto-tuning matrix decomposition block algorithms. In this test, the speedup is 1.09 for SGEQRF of square matrix and 1.8 for CGEQRF in terms of tall and skin matrix. © Institute of Software Chinese Academy of Sciences.
Language: 中文
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/17045
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Xiao, Xuan-Ji ,Zhang, Yun-Quan ,Li, Yu-Cheng ,et al. Performance testing and analysis of MAGMA library on hybrid architecture (CPU+GPU)[J]. Ruan Jian Xue Bao/Journal of Software,2013-01-01,24(SUPPL.2):118-126.
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