LAGraph: Linear Algebra, Network Analysis Libraries, and the Study of Graph Algorithms

被引:4
|
作者
Szarnyas, Gabor [1 ]
Bader, David A. [2 ]
Davis, Timothy A. [3 ]
Kitchen, James [4 ]
Mattson, Timothy G. [5 ]
McMillan, Scott [6 ]
Welch, Erik [4 ]
机构
[1] CWI Amsterdam, Amsterdam, Netherlands
[2] New Jersey Inst Technol, Newark, NJ 07102 USA
[3] Texas A&M Univ, College Stn, TX 77843 USA
[4] Anaconda Inc, Austin, TX USA
[5] Intel Corp, Santa Clara, CA 95051 USA
[6] Carnegie Mellon Univ, Software Engn Inst, Pittsburgh, PA 15213 USA
基金
美国安德鲁·梅隆基金会;
关键词
Graph Processing; Graph Algorithms; Graph Analytics; Linear Algebra; GraphBLAS;
D O I
10.1109/IPDPSW52791.2021.00046
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a library of low-level building blocks for such algorithms that targets algorithm developers. LAGraph builds on top of the GraphBLAS to target users of graph algorithms with high-level algorithms common in network analysis. In this paper, we describe the first release of the LAGraph library, the design decisions behind the library, and performance using the GAP benchmark suite. LAGraph, however, is much more than a library. It is also a project to document and analyze the full range of algorithms enabled by the GraphBLAS. To that end, we have developed a compact and intuitive notation for describing these algorithms. In this paper, we present that notation with examples from the GAP benchmark suite.
引用
收藏
页码:243 / 252
页数:10
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