GraphMap: scalable iterative graph processing using NoSQL
Document Type
Article
Publication Date
9-1-2020
Abstract
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.
Publication Source (Journal or Book title)
Journal of Supercomputing
First Page
6619
Last Page
6647
Recommended Citation
Goswami, S., Pokhrel, A., Lee, K., Liu, L., Zhang, Q., & Zhou, Y. (2020). GraphMap: scalable iterative graph processing using NoSQL. Journal of Supercomputing, 76 (9), 6619-6647. https://doi.org/10.1007/s11227-019-03097-w