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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:2.0
BEGIN:VEVENT
DTSTART:20151118T220000Z
DTEND:20151118T223000Z
LOCATION:19AB
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Scaling large-scale graph processing has been a heated research topic in recent years. Existing distributed graph systems are based on a distributed memory architecture. These distributed solutions heavily rely on distributed memory and thus suffer from poor scalability when the compute cluster can no longer hold the graph and all the intermediate results in memory. We present GraphMap, a distributed iterative graph computation framework, which effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. GraphMap has three salient features: (1) We distinguish those data states that are mutable during iterative computations from those that are=0Aread-only in all iterations to maximize sequential accesses and minimize random accesses. (2) We devise a two-level graph-partitioning algorithm to enable balanced workloads and locality-optimized data placement. (3) We propose a suite of locality-based optimizations to maximize computation efficiency.
SUMMARY:Scaling Iterative Graph Computations with GraphMap
PRIORITY:3
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