A Comparison of Parallel Graph Processing Implementations

Abstract

The rapidly growing number of large network analysis problems has led to the emergence of many parallel and distributed graph processing systems-one survey in 2014 identified over 80. Since then, the landscape has evolved; some packages have become inactive while more are being developed. Determining the best approach for a given problem is infeasible for most developers. To enable easy, rigorous, and repeatable comparison of the capabilities of such systems, we present an approach and associated software for analyzing the performance and scalability of parallel, open-source graph libraries. We demonstrate our approach on five graph processing packages: Graph-Mat, the Graph500, the Graph Algorithm Platform Benchmark Suite, GraphBIG, and PowerGraph using synthetic and real-world datasets. We examine previously overlooked aspects of parallel graph processing performance such as phases of execution and energy usage for three algorithms: breadth first search, single source shortest paths, and PageRank and compare our results to Graphalytics.

An Interactive and Reductive Graph Processing Library for Edge Computing in Smart Society

“…AllegroGraph, ArangoDB, Neo4j, Infinite-Graph, MarkLogic, and OrientDB are the representative commercial/free graph databases for the management of huge graphs currently [3], [4], [16]. Meanwhile, for major graph processing systems, various GPLs and GPFs have emerged for large-scale graphs in the past few decades [4], [5], [17]. As introduced above, distributed systems and single-machine systems are the main categories of GPLs/GPFs.…”

“…The typical distributed systems contain BiGraph, PowerSwitch, Giraph, GraphX, GBASE, GraphLab, PowerGraph, Pregel, etc. [17]. Most of them are developed based on several different computation models, like MapReduce model, bulk synchronous parallel (BSP) model, gather-apply-scatter (GAS) model, and so on [4], [5].…”