Large scale graph processing represents an interesting challenge due to the lack of locality. This paper presents PathGraph 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 an path-centric computation rather than vertex-centric or edge-centric computation. Our parallel computation model significantly improves the memory and disk locality for performing iterative computation algorithms. Second, we design a compact storage that further maximize sequential access and minimize random access on storage media. Third, 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 updates for vertices in each partition tree. The experimental results show that the path-centric approach outperforms vertexcentric and edge-centric systems on a number of graph algorithms for both in-memory and out-of-core graphs.
PathGraph REF proposes a path-centric model to improve the memory and disk access locality.
Fast iterative graph computation: a path centric approach
SC14: International Conference for High Performance Computing, Networking, Storage and Analysis
SC14: International Conference for High Performance Computing, Networking, Storage and Analysis
Computer Science