sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Scaling Iterative Graph Computations with GraphMap

SESSION: Management of Graph Workloads


EVENT TAG(S): System Software, Clouds and Distributed Computing

TIME: 4:00PM - 4:30PM


AUTHOR(S):Kisung Lee, Ling Liu, Karsten Schwan, Calton Pu, Qi Zhang, Yang Zhou, Emre Yigitoglu, Pingpeng Yuan



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
read-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.

Chair/Author Details:

Manoj Kumar (Chair) - IBM Corporation|

Kisung Lee - Louisiana State University

Ling Liu - Georgia Institute of Technology

Karsten Schwan - Georgia Institute of Technology

Calton Pu - Georgia Institute of Technology

Qi Zhang - Georgia Institute of Technology

Yang Zhou - Georgia Institute of Technology

Emre Yigitoglu - Georgia Institute of Technology

Pingpeng Yuan - Huazhong University of Science and Technology

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society