WolfGraph: The edge-centric graph processing on GPU

Abstract

There is the significant interest nowadays in developing the frameworks for parallelizing the processing of large graphs such as social networks, web graphs, etc. The work has been proposed to parallelize the graph processing on clusters (distributed memory), multicore machines (shared memory) and GPU devices. Most existing research on GPU-based graph processing employs the vertex-centric processing model and the Compressed Sparse Row (CSR) form to store and process a graph. However, they suffer from irregular memory access and load imbalance in GPU, which hampers the full exploitation of GPU performance. In this paper, we present WolfGraph, a GPU-based graph processing framework that addresses the above problems. WolfGraph adopts the edge-centric processing, which iterates over the edges rather than vertices. The data structure and graph partition in WolfGraph are carefully crafted so as to minimize the graph pre-processing and allow the coalesced memory access. WolfGraph fully utilizes the GPU power by processing all edges in parallel. We also develop a new method, called Concatenated Edge List (CEL), to process a graph that is bigger than the global memory of GPU. WolfGraph allows the users to define their own graph-processing methods and plug them into the WolfGraph framework. Our experiments show that WolfGraph achieves 7-8x speedup over GraphChi and X-Stream when processing large graphs, and it also offers 65% performance improvement over the existing GPU-based, vertex-centric graph processing frameworks, such as Gunrock.