GRE: A Graph Runtime Engine for Large-Scale Distributed Graph-Parallel Applications

TL;DR

GRE introduces a novel graph-parallel framework with new abstractions that significantly improves performance and memory efficiency for large-scale distributed graph processing on multi-core clusters.

Contribution

GRE proposes two new abstractions, Scatter-Combine and Agent-Graph, to enhance parallelism and graph partitioning, outperforming existing frameworks like PowerGraph.

Findings

GRE achieves 2.5-17x performance improvement over PowerGraph.

GRE can process graphs with 1 billion vertices and 17 billion edges.

GRE uses less memory, enabling larger graph processing.

Abstract

Large-scale distributed graph-parallel computing is challenging. On one hand, due to the irregular computation pattern and lack of locality, it is hard to express parallelism efficiently. On the other hand, due to the scale-free nature, real-world graphs are hard to partition in balance with low cut. To address these challenges, several graph-parallel frameworks including Pregel and GraphLab (PowerGraph) have been developed recently. In this paper, we present an alternative framework, Graph Runtime Engine (GRE). While retaining the vertex-centric programming model, GRE proposes two new abstractions: 1) a Scatter-Combine computation model based on active message to exploit massive fined-grained edge-level parallelism, and 2) a Agent-Graph data model based on vertex factorization to partition and represent directed graphs. GRE is implemented on commercial off-the-shelf multi-core cluster. We experimentally evaluate GRE with three benchmark programs (PageRank, Single Source Shortest Path and Connected Components) on real-world and synthetic graphs of millions billion of vertices. Compared to PowerGraph, GRE shows 2.5~17 times better performance on 8~16 machines (192 cores). Specifically, the PageRank in GRE is the fastest when comparing to counterparts of other frameworks (PowerGraph, Spark,Twister) reported in public literatures. Besides, GRE significantly optimizes memory usage so that it can process a large graph of 1 billion vertices and 17 billion edges on our cluster with totally 768GB memory, while PowerGraph can only process less than half of this graph scale.