A hierarchical approach to reducing communication in parallel graph algorithms

Handle URI:
http://hdl.handle.net/10754/597283
Title:
A hierarchical approach to reducing communication in parallel graph algorithms
Authors:
Harshvardhan,; Amato, Nancy M.; Rauchwerger, Lawrence
Abstract:
Large-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. This is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. The hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5 × - 8× improvement for a variety of graph algorithms at 12, 000+ cores.
Citation:
Harshvardhan, Amato NM, Rauchwerger L (2015) A hierarchical approach to reducing communication in parallel graph algorithms. Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming - PPoPP 2015. Available: http://dx.doi.org/10.1145/2688500.2700994.
Publisher:
Association for Computing Machinery (ACM)
Journal:
Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming - PPoPP 2015
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
2015
DOI:
10.1145/2688500.2700994
Type:
Conference Paper
Sponsors:
We would like to thank our anonymous reviewers. This research is supported in part by NSF awards CCF 0702765, CNS-0551685, CCF-0833199, CCF-1439145, CCF-1423111, CCF-0830753, IIS-0917266, by DOE awards DE-AC02-06CH11357, DE-NA0002376, B575363, by Samsung, IBM, Intel, and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). This research used resources of the National Energy
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Full metadata record

DC FieldValue Language
dc.contributor.authorHarshvardhan,en
dc.contributor.authorAmato, Nancy M.en
dc.contributor.authorRauchwerger, Lawrenceen
dc.date.accessioned2016-02-25T12:29:47Zen
dc.date.available2016-02-25T12:29:47Zen
dc.date.issued2015en
dc.identifier.citationHarshvardhan, Amato NM, Rauchwerger L (2015) A hierarchical approach to reducing communication in parallel graph algorithms. Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming - PPoPP 2015. Available: http://dx.doi.org/10.1145/2688500.2700994.en
dc.identifier.doi10.1145/2688500.2700994en
dc.identifier.urihttp://hdl.handle.net/10754/597283en
dc.description.abstractLarge-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. This is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. The hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5 × - 8× improvement for a variety of graph algorithms at 12, 000+ cores.en
dc.description.sponsorshipWe would like to thank our anonymous reviewers. This research is supported in part by NSF awards CCF 0702765, CNS-0551685, CCF-0833199, CCF-1439145, CCF-1423111, CCF-0830753, IIS-0917266, by DOE awards DE-AC02-06CH11357, DE-NA0002376, B575363, by Samsung, IBM, Intel, and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). This research used resources of the National Energyen
dc.publisherAssociation for Computing Machinery (ACM)en
dc.subjectBig Dataen
dc.subjectDistributed computingen
dc.subjectGraph analyticsen
dc.subjectParallel graph processingen
dc.titleA hierarchical approach to reducing communication in parallel graph algorithmsen
dc.typeConference Paperen
dc.identifier.journalProceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming - PPoPP 2015en
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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