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dc.contributor.authorBattaglino, Casey
dc.contributor.authorPienta, Pienta
dc.contributor.authorVuduc, Richard
dc.identifier.citationBattaglino, Casey; Pienta, Pienta; Vuduc, Richard. GraSP: distributed streaming graph partitioning. A: HPGM: High Performance Graph Mining. "1st High Performance Graph Mining workshop, Sydney, 10 August 2015". 2015.
dc.description.abstractThis paper presents a distributed, streaming graph parti- tioner, Graph Streaming Partitioner (GraSP), which makes partition decisions as each vertex is read from memory, sim- ulating an online algorithm that must process nodes as they arrive. GraSP is a lightweight high-performance comput- ing (HPC) library implemented in MPI, designed to be easily substituted for existing HPC partitioners such as ParMETIS. It is the rst MPI implementation for streaming partition- ing of which we are aware, and is empirically orders-of- magnitude faster than existing partitioners while providing comparable partitioning quality. We demonstrate the scala- bility of GraSP on up to 1024 compute nodes of NERSC's Edison supercomputer. Given a minute of run-time, GraSP can partition a graph three orders of magnitude larger than ParMETIS can.
dc.relation.ispartofHigh Performance Graph Mining workshop (1st: 2015: Sydney)
dc.rightsAttribution-ShareAlike 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica discreta
dc.subject.otherstreaming algorithms
dc.subject.othergraph partitioning
dc.subject.otherdistributed-memory algorithms
dc.titleGraSP: distributed streaming graph partitioning
dc.typeConference report
dc.rights.accessOpen Access
upcommons.citation.contributorHPGM: High Performance Graph Mining
upcommons.citation.publicationName1st High Performance Graph Mining workshop, Sydney, 10 August 2015

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