Graphs, Matrices, and the GraphBLAS: Seven Good Reasons

被引：29
作者：
Kepner, Jeremy [1 ]
Bader, David [2 ,3 ]
Buluc, Aydin
Gilbert, John [4 ]
Mattson, Timothy [5 ]
Meyerhenke, Henning [6 ]
机构：
[1] MIT, Cambridge, MA 02139 USA
[2] Georgia Inst Technol, Atlanta, GA 30332 USA
[3] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA
[4] Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA
[5] Intel Corp, Portland, OR USA
[6] Karlsruhe Inst Technol, D-76021 Karlsruhe, Germany
来源：
INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2015 COMPUTATIONAL SCIENCE AT THE GATES OF NATURE | 2015年 / 51卷
基金：
美国国家科学基金会;
关键词：
graphs; algorithms; matrices; linear algebra; software standards;
D O I：
10.1016/j.procs.2015.05.353
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istc-bigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.
引用
收藏
页码：2453 / 2462
页数：10
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