skip to main content
10.1145/2833179.2833188acmconferencesArticle/Chapter ViewAbstractPublication PagesscConference Proceedingsconference-collections
short-paper
Public Access

Improving graph partitioning for modern graphs and architectures

Published: 15 November 2015 Publication History

Abstract

Graph partitioning is an important preprocessing step in applications dealing with sparse-irregular data. As such, the ability to efficiently partition a graph in parallel is crucial to the performance of these applications. The number of compute cores in a compute node continues to increase, demanding ever more scalability from shared-memory graph partitioners. In this paper we present algorithmic improvements to the multithreaded graph partitioner mt-Metis. We experimentally evaluate our methods on a 36 core machine, using 20 different graphs from a variety of domains. Our improvements decrease the runtime by 1.5-11.7X and improve strong scaling by 82%.

References

[1]
B. F. Auer and R. H. Bisseling. Graph coarsening and clustering on the gpu. Graph Partitioning and Graph Clustering, 588:223, 2012.
[2]
A. Buluç, H. Meyerhenke, I. Safro, P. Sanders, and C. Schulz. Recent advances in graph partitioning. CoRR, abs/1311.3144, 2013.
[3]
Ü. V. Çatalyürek, M. Deveci, K. Kaya, and B. Ucar. Multithreaded clustering for multi-level hypergraph partitioning. In Parallel & Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International, pages 848--859. IEEE, 2012.
[4]
C. Chevalier and F. Pellegrini. Pt-scotch: A tool for efficient parallel graph ordering. Parallel Computing, 34(6):318--331, 2008.
[5]
T. A. Davis and Y. Hu. The university of florida sparse matrix collection. ACM Transactions on Mathematical Software (TOMS), 38(1):1, 2011.
[6]
I. S. Duff and J. K. Reid. Exploiting zeros on the diagonal in the direct solution of indefinite sparse symmetric linear systems. ACM Tran. Math. Soft., 22(2):227--257, 1996.
[7]
B. Hendrickson and R. Leland. A multilevel algorithm for partitioning graphs. Technical Report SAND93-1301, Sandia National Labratories, 1993.
[8]
B. Hendrickson and E. Rothberg. Improving the run time and quality of nested dissection ordering. SIAM Journal on Scientific Computing, 20(2):468--489, 1998.
[9]
M. Holtgrewe, P. Sanders, and C. Schulz. Engineering a scalable high quality graph partitioner. In Parallel & Distributed Processing (IPDPS), 2010 IEEE International Symposium on, pages 1--12. IEEE, 2010.
[10]
G. Karypis and V. Kumar. Parallel multilevel k-way partitioning scheme for irregular graphs. Supercomputing '96, Washington, DC, USA, 1996. IEEE Computer Society.
[11]
G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput., 20(1):359--392, Dec. 1998.
[12]
D. LaSalle and G. Karypis. Multi-threaded graph partitioning. In Parallel & Distributed Processing (IPDPS), 2013 IEEE 27th International Symposium on, pages 225--236. IEEE, 2013.
[13]
D. LaSalle and G. Karypis. Multi-threaded modularity based graph clustering using the multilevel paradigm. Journal of Parallel and Distributed Computing, 2014.
[14]
J. Leskovec and A. Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014.
[15]
J.-S. Park, M. Penner, and V. K. Prasanna. Optimizing graph algorithms for improved cache performance. Parallel and Distributed Systems, IEEE Transactions on, 15(9):769--782, 2004.
[16]
F. Pellegrini and J. Roman. Scotch: A software package for static mapping by dual recursive bipartitioning of process and architecture graphs. HPCN, pages 493--498, London, UK, 1996. Springer-Verlag.
[17]
P. Sanders and C. Schulz. Engineering multilevel graph partitioning algorithms. In ESA 2011, volume 6942 of Lecture Notes in Computer Science, pages 469--480. Springer Berlin / Heidelberg, 2011.
[18]
H. D. Simon. Partitioning of unstructured problems for parallel processing. Computing Systems in Engineering, 2(2):135--148, 1991.
[19]
X. Sui, D. Nguyen, M. Burtscher, and K. Pingali. Parallel graph partitioning on multicore architectures. In Languages and Compilers for Parallel Computing, pages 246--260. Springer, 2011.

Cited By

View all
  • (2024)Scalable High-Quality Hypergraph PartitioningACM Transactions on Algorithms10.1145/362652720:1(1-54)Online publication date: 22-Jan-2024
  • (2024)Sparse Matrix Reordering Method Selection with Parallel Computing and Deep Learning2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651141(1-8)Online publication date: 30-Jun-2024
  • (2024)Optimizing Task Orchestration for Distributed Real-Time Electromagnetic Transient SimulationIEEE Access10.1109/ACCESS.2024.340407012(74818-74830)Online publication date: 2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
IA3 '15: Proceedings of the 5th Workshop on Irregular Applications: Architectures and Algorithms
November 2015
79 pages
ISBN:9781450340014
DOI:10.1145/2833179
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2015

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Short-paper

Funding Sources

Conference

SC15
Sponsor:

Acceptance Rates

IA3 '15 Paper Acceptance Rate 6 of 24 submissions, 25%;
Overall Acceptance Rate 18 of 67 submissions, 27%

Upcoming Conference

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)131
  • Downloads (Last 6 weeks)19
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Scalable High-Quality Hypergraph PartitioningACM Transactions on Algorithms10.1145/362652720:1(1-54)Online publication date: 22-Jan-2024
  • (2024)Sparse Matrix Reordering Method Selection with Parallel Computing and Deep Learning2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651141(1-8)Online publication date: 30-Jun-2024
  • (2024)Optimizing Task Orchestration for Distributed Real-Time Electromagnetic Transient SimulationIEEE Access10.1109/ACCESS.2024.340407012(74818-74830)Online publication date: 2024
  • (2023)DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by ChunksProceedings of the ACM on Management of Data10.1145/36267241:4(1-25)Online publication date: 12-Dec-2023
  • (2023)PeeK: A Prune-Centric Approach for K Shortest Path ComputationProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607110(1-14)Online publication date: 12-Nov-2023
  • (2023)More Recent Advances in (Hyper)Graph PartitioningACM Computing Surveys10.1145/357180855:12(1-38)Online publication date: 2-Mar-2023
  • (2023)End-to-end programmable computing systemsCommunications Engineering10.1038/s44172-023-00127-72:1Online publication date: 24-Nov-2023
  • (2023)Distributed Deep Multilevel Graph PartitioningEuro-Par 2023: Parallel Processing10.1007/978-3-031-39698-4_30(443-457)Online publication date: 24-Aug-2023
  • (2022)iSpan: Parallel Identification of Strongly Connected Components with Spanning TreesACM Transactions on Parallel Computing10.1145/35435429:3(1-27)Online publication date: 18-Aug-2022
  • (2022)A Multilevel Spectral Framework for Scalable Vectorless Power/Thermal Integrity VerificationACM Transactions on Design Automation of Electronic Systems10.1145/352953428:1(1-25)Online publication date: 10-Dec-2022
  • Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media