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ISTC-CC Abstract
Fast Iterative Graph Computation: A Path Centric Approach
Proceedings of IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC’14), November 2014.
Pingpeng Yuan, Wenya Zhang, Changfeng Xie, Hai Jin, Ling Liu*, Kisung Lee*
Huazhong University of Science and Technology
* Georgia Institute of Technology
Large scale graph processing represents an interesting systems challenge due to the lack of locality. This paper presents PathGraph, a system for improving iterative graph computation on graphs with billions of edges. Our system design has three unique features: First, we model a large graph using a collection of tree-based partitions and use pathcentric computation rather than vertex-centric or edge-centric computation. Our path-centric graph parallel computation model significantly improves the memory and disk locality for iterative computation algorithms on large graphs. Second, we design a compact storage that is optimized for iterative graph parallel computation. Concretely, we use delta-compression, partition a large graph into tree-based partitions and store trees in a DFS order. By clustering highly correlated paths together, we further maximize sequential access and minimize random access on storage media. Third but not the least, we implement the path-centric computation model by using a scatter/gather programming model, which parallels the iterative computation at partition tree level and performs sequential local updates for vertices in each tree partition to improve the convergence speed. We compare PathGraph to most recent alternative graph processing systems such as GraphChi and X-Stream, and show that the path-centric approach outperforms vertex-centric and edge-centric systems on a number of graph algorithms for both in-memory and out-of-core graphs.
FULL PAPER: pdf
