To 4,000 Compute Nodes and Beyond: Network-aware Vertex Placement in Large-scale Graph Processing Systems

Karim Awara, Hani Jamjoom, Panos Kalnis

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

1 Scopus citations

Abstract

The explosive growth of "big data" is giving rise to a new breed of large scale graph systems, such as Pregel. This poster describes our ongoing work in characterizing and minimizing the communication cost of Bulk Synchronous Parallel (BSP) graph mining systems, like Pregel, when scaling to 4,096 compute nodes. Existing implementations generally assume a fixed communication cost. This is sufficient in small deployments as the BSP programming model (i.e., overlapping computation and communication) masks small variations in the underlying network. In large scale deployments, such variations can dominate the overall runtime characteristics. In this poster, we first quantify the impact of network communication on the total compute time of a Pregel system. We then propose an efficient vertex placement strategy that subsamples highly connected vertices and applies the Reverse Cuthill-McKee (RCM) algorithm to efficiently partition the input graph and place partitions closer to each other based on their expected communication patterns. We finally describe a vertex replication strategy to further reduce communication overhead. © 2013 Authors.
Original languageEnglish (US)
Title of host publicationACM SIGCOMM COMPUTER COMMUNICATION REVIEW
PublisherAssociation for Computing Machinery (ACM)
Pages501-502
Number of pages2
ISBN (Print)9781450320566
DOIs
StatePublished - Aug 27 2013

Fingerprint

Dive into the research topics of 'To 4,000 Compute Nodes and Beyond: Network-aware Vertex Placement in Large-scale Graph Processing Systems'. Together they form a unique fingerprint.

Cite this