PathGraph: A Path Centric Graph Processing System

doi:10.1109/TPDS.2016.2518664

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PathGraph: A Path Centric Graph Processing System

2016-10-01

PRE

袁

袁平鹏 (Pingpeng Yuan) *

Ling Liu (Ling Liu)

金

金海 (Hai Jin)

DOI：10.1109/TPDS.2016.2518664

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Abstract

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Large scale iterative graph computation presents an interesting systems challenge due to two well known problems: (1) the lack of access locality and (2) the lack of storage efficiency. This paper presents PathGraph, a system for improving iterative graph computation on graphs with billions of edges. First, we improve the memory and disk access locality for iterative computation algorithms on large graphs by modeling a large graph using a collection of tree-based partitions. This enables us to use path-centric computation rather than vertex-centric or edge-centric computation. For each tree partition, we re-label vertices using DFS in order to preserve consistency between the order of vertex ids and vertex order in the paths. Second, a compact storage that is optimized for iterative graph parallel computation is developed in the PathGraph system. Concretely, we employ delta-compression and store tree-based partitions in a DFS order. By clustering highly correlated paths together as tree based partitions, we maximize sequential access and minimize random access on storage media. Third but not the least, our path-centric computation model is implemented using a scatter/gather programming model. We parallel the iterative computation at partition tree level and perform sequential local updates for vertices in each tree partition to improve the convergence speed. To provide well balanced workloads among parallel threads at tree partition level, we introduce the concept of multiple stealing points based task queue to allow work stealings from multiple points in the task queue. We evaluate the effectiveness of PathGraph by comparing with recent representative graph processing systems such as GraphChi and X-Stream etc. Our experimental results show that our approach outperforms the two systems on a number of graph algorithms for both in-memory and out-of-core graphs. While our approach achieves better data balance and load balance, it also shows better speedup than the two systems with the growth of threads.

Keywords:

Graphs and networks

concurrent programming

graph algorithms

data storage representations

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