Date of event：2018-07-17

Time of event: 08:30

Lecturer：Dr. Ligang He, Associate Professor, Department of Computer Science, University of Warwick, UK

Venue: Rm.3524. Wenjin bldg.Changan Campus

Hosted by: Pervasive computing research group, School of Computer Science

About the Lecturer：

Ligang He received the Ph.D degree in Computer Science at the University of Warwick, United Kingdom, and worked as a post-doctoral researcher at the University of Cambridge, UK. From 2006, he worked in the Department of Computer Science at the University of Warwick as Assistant Professor and then as Associate Professor. His research interests focus on parallel and distributed processing, high performance computing, cloud computing. He has published more than 100 papers in international conferences and journals, such as IEEE Transactions on Parallel and Distributed Systems, IPDPS, CCGrid, MASCOTS. He has been a co-chair or a member of the program committee for a number of international conferences, and been the reviewers for many international journals.

There is 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 exploit of GPU performance. In this talk, I will present WolfGraph, a GPU-based graph processing framework that overcomes 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 fully coalesced memory access. WolfGraph fully utilizes the GPU power by processing all edges in parallel. We also developed 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.