A multi-level parallel implementation of a program for finding frequent patterns in a large sparse graph

Steve Reinhardt, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Graphs capture the essential elements of many problems broadly defined as searching or categorizing. With the rapid increase of data volumes from sensors, many application disciplines need to process larger graphs quickly. This paper presents the results of parallelizing with OpenMP an algorithm that finds, in a single large labeled undirected sparse graph, the connected subgraphs with a given minimum number of edge-disjoint embeddings. Parallelism is exploited at two levels in the algorithm. The lack of a priori knowledge of the extent of parallelism for a given input required use of a dynamic, multi-level approach based on the proposed OpenMP taskq/task extensions. The parallel implementation required the addition of 21 directives and about 50 accompanying lines of code, in an original code of about 15,000 lines. Experimental results show excellent speed-up to 30 processors for the graphs used, with a best speed-up of 26.1 compared to the serial version. The taskq/task constructs show promise for problems exhibiting unstructured parallelism.

Original languageEnglish (US)
Title of host publicationProceedings - 21st International Parallel and Distributed Processing Symposium, IPDPS 2007; Abstracts and CD-ROM
DOIs
StatePublished - 2007
Event21st International Parallel and Distributed Processing Symposium, IPDPS 2007 - Long Beach, CA, United States
Duration: Mar 26 2007Mar 30 2007

Publication series

NameProceedings - 21st International Parallel and Distributed Processing Symposium, IPDPS 2007; Abstracts and CD-ROM

Other

Other21st International Parallel and Distributed Processing Symposium, IPDPS 2007
CountryUnited States
CityLong Beach, CA
Period3/26/073/30/07

Keywords

  • Data mining
  • Frequent subgraph
  • OpenMP
  • Parallel processing
  • Pattern discovery
  • Unstructured parallelism

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