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Distributed SociaLite: A Datalog-Based Language for Large-Scale Graph Analysis
| Citations: | 5 - 0 self |
BibTeX
@MISC{Seo_distributedsocialite:,
author = {Jiwon Seo and Jongsoo Park and Jaeho Shin and Monica S. Lam},
title = {Distributed SociaLite: A Datalog-Based Language for Large-Scale Graph Analysis},
year = {}
}
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Abstract
Large-scale graph analysis is becoming important with the rise of world-wide social network services. Recently in SociaLite, we proposed extensions to Datalog to efficiently and succinctly implement graph analysis programs on sequential machines. This paper describes novel extensions and optimizations of SociaLite for parallel and distributed executions to support large-scale graph analysis. With distributed SociaLite, programmers simply annotate how data are to be distributed, then the necessary communication is automatically inferred to generate parallel code for cluster of multi-core machines. It optimizes the evaluation of recursive monotone aggregate functions using a delta stepping technique. In addition, approximate computation is supported in SociaLite, allowing programmers to trade off accuracy for less time and space. We evaluated SociaLite with six core graph algorithms used in many social network analyses. Our experiment with 64 Amazon EC2 8-core instances shows that SociaLite programs performed within a factor of two with respect to ideal weak scaling. Compared to optimized Giraph, an opensource alternative of Pregel, SociaLite programs are 4 to 12 times faster across benchmark algorithms, and 22 times more succinct on average. As a declarative query language, SociaLite, with the help of a compiler that generates efficient parallel and approximate code, can be used easily to create many social apps that operate on large-scale distributed graphs. 1.
Keyphrases
large-scale graph analysis datalog-based language socialite program declarative query language weak scaling multi-core machine approximate code necessary communication benchmark algorithm distributed execution opensource alternative implement graph analysis program sequential machine approximate computation novel extension many social apps efficient parallel core graph algorithm distributed socialite many social network analysis amazon ec2 recursive monotone aggregate function world-wide social network service parallel code
