GraphMap: scalable iterative graph processing using NoSQL

Despite having several distributed graph processing frameworks, scalable iterative processing of large graphs is a challenging problem since the graph and intermediate data need a global view of the graph topology in distributed memory. Although some systems support out-of-core iterative computation...

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Bibliographic Details
Published in:The Journal of supercomputing 2020-09, Vol.76 (9), p.6619-6647
Main Authors: Goswami, Sayan, Pokhrel, Ayam, Lee, Kisung, Liu, Ling, Zhang, Qi, Zhou, Yang
Format: Article
Language:English
Subjects:
Compilers
Computer Science
Distributed memory
Graphs
Interpreters
Iterative methods
Microprocessors
Processor Architectures
Programming Languages
Topology
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Summary:Despite having several distributed graph processing frameworks, scalable iterative processing of large graphs is a challenging problem since the graph and intermediate data need a global view of the graph topology in distributed memory. Although some systems support out-of-core iterative computations, they use a single machine and often require fast storage. In this paper, we present a new distributed iterative graph computation framework, called GraphMap, that utilizes a disk-based NoSQL database system for scalable graph processing while ensuring competitive performance. Extensive experiments on several real-world graphs show that GraphMap is more scalable and often faster than existing distributed memory-based systems for various graph processing workloads.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-019-03097-w