GraSP: distributed streaming graph partitioning
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hdl:2117/76383
Tipus de documentText en actes de congrés
Data publicació2015
EditorBarcelona Supercomputing Center
Condicions d'accésAccés obert
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Abstract
This 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.
CitacióBattaglino, 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.
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