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Title: GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics1

Abstract

Large scale graph processing is a major research area for Big Data exploration. Vertex centric programming models like Pregel are gaining traction due to their simple abstraction that allows for scalable execution on distributed systems naturally. However, there are limitations to this approach which cause vertex centric algorithms to under-perform due to poor compute to communication overhead ratio and slow convergence of iterative superstep. In this paper we introduce GoFFish a scalable sub-graph centric framework co-designed with a distributed persistent graph storage for large scale graph analytics on commodity clusters. We introduce a sub-graph centric programming abstraction that combines the scalability of a vertex centric approach with the flexibility of shared memory sub-graph computation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, comparedmore » to Apache Giraph, the leading open source vertex centric implementation.« less

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
City of Los Angeles Department
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1332680
Report Number(s):
DOE-USC-00192-87
Journal ID: ISSN 0302--9743
DOE Contract Number:  
OE0000192
Resource Type:
Conference
Resource Relation:
Journal Volume: 8632; Conference: Euro-Par Porto, Portugal August 25-26, 2014
Country of Publication:
United States
Language:
English

Citation Formats

Simmhan, Yogesh, Kumbhare, Alok, Wickramaarachchi, Charith, Nagarkar, Soonil, Ravi, Santosh, Raghavendra, Cauligi, and Prasanna, Viktor. GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics1. United States: N. p., 2014. Web. doi:10.1007/978-3-319-09873-9_38.
Simmhan, Yogesh, Kumbhare, Alok, Wickramaarachchi, Charith, Nagarkar, Soonil, Ravi, Santosh, Raghavendra, Cauligi, & Prasanna, Viktor. GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics1. United States. doi:10.1007/978-3-319-09873-9_38.
Simmhan, Yogesh, Kumbhare, Alok, Wickramaarachchi, Charith, Nagarkar, Soonil, Ravi, Santosh, Raghavendra, Cauligi, and Prasanna, Viktor. Mon . "GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics1". United States. doi:10.1007/978-3-319-09873-9_38. https://www.osti.gov/servlets/purl/1332680.
@article{osti_1332680,
title = {GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics1},
author = {Simmhan, Yogesh and Kumbhare, Alok and Wickramaarachchi, Charith and Nagarkar, Soonil and Ravi, Santosh and Raghavendra, Cauligi and Prasanna, Viktor},
abstractNote = {Large scale graph processing is a major research area for Big Data exploration. Vertex centric programming models like Pregel are gaining traction due to their simple abstraction that allows for scalable execution on distributed systems naturally. However, there are limitations to this approach which cause vertex centric algorithms to under-perform due to poor compute to communication overhead ratio and slow convergence of iterative superstep. In this paper we introduce GoFFish a scalable sub-graph centric framework co-designed with a distributed persistent graph storage for large scale graph analytics on commodity clusters. We introduce a sub-graph centric programming abstraction that combines the scalability of a vertex centric approach with the flexibility of shared memory sub-graph computation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation.},
doi = {10.1007/978-3-319-09873-9_38},
journal = {},
number = ,
volume = 8632,
place = {United States},
year = {Mon Aug 25 00:00:00 EDT 2014},
month = {Mon Aug 25 00:00:00 EDT 2014}
}

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