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High-performance graph algorithms from parallel sparse matrices

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PARA'06: Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing

Pages 260 - 269

Published: 18 June 2006 Publication History

Abstract

Large-scale computation on graphs and other discrete structures is becoming increasingly important in many applications, including computational biology, web search, and knowledge discovery. High-performance combinatorial computing is an infant field, in sharp contrast with numerical scientific computing.

We argue that many of the tools of high-performance numerical computing - in particular, parallel algorithms and data structures for computation with sparse matrices - can form the nucleus of a robust infrastructure for parallel computing on graphs. We demonstrate this with an implementation of a graph analysis benchmark using the sparse matrix infrastructure in Star-P, our parallel dialect of the MATLAB programming language.

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Cited By

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Ashraf RGioiosa RFeng DBecker SHerbst NLeitner PPapadopoulos A(2022)Exploring the Use of Novel Spatial Accelerators in Scientific ApplicationsProceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering10.1145/3489525.3511690(47-58)Online publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1145/3489525.3511690
Nagasaka YMatsuoka SAzad ABuluç A(2018)High-Performance Sparse Matrix-Matrix Products on Intel KNL and Multicore ArchitecturesWorkshop Proceedings of the 47th International Conference on Parallel Processing10.1145/3229710.3229720(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3229710.3229720
Yang CBuluç AOwens J(2018)Implementing Push-Pull Efficiently in GraphBLASProceedings of the 47th International Conference on Parallel Processing10.1145/3225058.3225122(1-11)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3225058.3225122
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High-performance graph algorithms from parallel sparse matrices
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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PARA'06: Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing

June 2006

1191 pages

ISBN:3540757546

Editors:
Bo Kågström
Umeå University, Department of Computing Science and High Performance Computing Center North, Umeå, Sweden
,
Erik Elmroth
Umeå University, Department of Computing Science and High Performance Computing Center North, Umeå, Sweden
,
Jack Dongarra
University of Tennessee, Department of Computer Science, Knoxville, TN
,
Jerzy Waśniewski
Technical University of Denmark, Informatics and Mathematical Modelling, Lyngby, Denmark

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Springer-Verlag

Berlin, Heidelberg

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Published: 18 June 2006

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Ashraf RGioiosa RFeng DBecker SHerbst NLeitner PPapadopoulos A(2022)Exploring the Use of Novel Spatial Accelerators in Scientific ApplicationsProceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering10.1145/3489525.3511690(47-58)Online publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1145/3489525.3511690
Nagasaka YMatsuoka SAzad ABuluç A(2018)High-Performance Sparse Matrix-Matrix Products on Intel KNL and Multicore ArchitecturesWorkshop Proceedings of the 47th International Conference on Parallel Processing10.1145/3229710.3229720(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3229710.3229720
Yang CBuluç AOwens J(2018)Implementing Push-Pull Efficiently in GraphBLASProceedings of the 47th International Conference on Parallel Processing10.1145/3225058.3225122(1-11)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3225058.3225122
Sun QZhang CWu CZhang JLi L(2018)Bandwidth Reduced Parallel SpMV on the SW26010 Many-Core PlatformProceedings of the 47th International Conference on Parallel Processing10.1145/3225058.3225074(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3225058.3225074
Kunchum RChaudhry ASukumaran-Rajam ANiu QNisa ISadayappan PGropp WBeckman PLi ZCazorla F(2017)On improving performance of sparse matrix-matrix multiplication on GPUsProceedings of the International Conference on Supercomputing10.1145/3079079.3079106(1-11)Online publication date: 14-Jun-2017
https://dl.acm.org/doi/10.1145/3079079.3079106
Anh PFan RWen Y(2016)Balanced Hashing and Efficient GPU Sparse General Matrix-Matrix MultiplicationProceedings of the 2016 International Conference on Supercomputing10.1145/2925426.2926273(1-12)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1145/2925426.2926273
Greathouse JDaga MDamkroger TDongarra J(2014)Efficient sparse matrix-vector multiplication on GPUs using the CSR storage formatProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC.2014.68(769-780)Online publication date: 16-Nov-2014
https://dl.acm.org/doi/10.1109/SC.2014.68
Cicotti PCarrington LKimpe DChen YRoth PYu W(2014)A caching approach to reduce communication in graph search algorithmsProceedings of the 2014 International Workshop on Data Intensive Scalable Computing Systems10.1109/DISCS.2014.8(65-72)Online publication date: 16-Nov-2014
https://dl.acm.org/doi/10.1109/DISCS.2014.8
Checconi FPetrini F(2013)Massive data analyticsIBM Journal of Research and Development10.1147/JRD.2012.223241457:1(111-121)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1147/JRD.2012.2232414
Hossain SSteihaug T(2013)Graph models and their efficient implementation for sparse Jacobian matrix determinationDiscrete Applied Mathematics10.1016/j.dam.2012.12.010161:12(1747-1754)Online publication date: 1-Aug-2013
https://dl.acm.org/doi/10.1016/j.dam.2012.12.010
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