Graphs, matrices, and the GraphBLAS: Seven good reasons

Kepner, Jeremy; Bader, David; Buluç, Aydın; Gilbert, John; Mattson, Timothy; Meyerhenke, Henning

doi:10.1016/j.procs.2015.05.353

Title: Graphs, matrices, and the GraphBLAS: Seven good reasons

Journal Article · Thu Jan 01 00:00:00 EST 2015 · Procedia Computer Science

DOI:https://doi.org/10.1016/j.procs.2015.05.353· OSTI ID:1208646

Kepner, Jeremy ^[1]; Bader, David ^[2]; Buluç, Aydın ^[3]; Gilbert, John ^[4]; Mattson, Timothy ^[5]; Meyerhenke, Henning ^[6]

Massachusetts Inst. of Technology., Cambridge, MA (United States)
Georgia Inst. of Technology, Atlanta, GA (United States)
Lawrence Berkeley National Lab., CA (United States)
Univ. of California, Santa Barbara, CA (United States)
Intel Corporation, Portland, OR (United States)
Karlsruhe Inst. of Technology, Karlsruhe (Germany)

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 (istcbigdata.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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1208646

Journal Information:: Procedia Computer Science, Vol. 51, Issue C; ISSN 1877-0509

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 21 works

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Scaling sparse matrix-matrix multiplication in the accumulo database Demirci, Gunduz Vehbi; Aykanat, Cevdet Distributed and Parallel Databases, Vol. 38, Issue 1 https://doi.org/10.1007/s10619-019-07257-y	journal	January 2019
From NoSQL Accumulo to NewSQL Graphulo: Design and utility of graph algorithms inside a BigTable database Hutchison, Dylan; Kepner, Jeremy; Gadepally, Vijay 2016 IEEE High Performance Extreme Computing Conference (HPEC) https://doi.org/10.1109/hpec.2016.7761577	conference	September 2016
Benchmarking the graphulo processing framework Weale, Timothy; Gadepally, Vijay; Hutchison, Dylan 2016 IEEE High Performance Extreme Computing Conference (HPEC) https://doi.org/10.1109/hpec.2016.7761640	conference	September 2016
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BigSparse: High-performance external graph analytics Jun, Sang-Woo; Wright, Andy; Zhang, Sizhuo arXiv https://doi.org/10.48550/arxiv.1710.07736	preprint	January 2017
Design, Generation, and Validation of Extreme Scale Power-Law Graphs Kepner, Jeremy; Samsi, Siddharth; Arcand, William arXiv https://doi.org/10.48550/arxiv.1803.01281	text	January 2018
A GraphBLAS Approach for Subgraph Counting Chen, Langshi; Li, Jiayu; Azad, Ariful arXiv https://doi.org/10.48550/arxiv.1903.04395	preprint	January 2019
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Fast Mapping onto Census Blocks Kepner, Jeremy; Kipf, Andreas; Engwirda, Darren arXiv https://doi.org/10.48550/arxiv.2005.03156	text	January 2020

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