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美国阿肯色州立大学(Arkansas State University, USA)Hai Jiang教授学术报告通知
撰稿人:彭滔     发布时间:2018年07月05日 19:03
题目: GPU-Based MapReduce for Big Data Processing
时间: 2018年8月6日(星期一)下午2:30-4:00
地点: 广州大学行政西楼前座428(学院会议室)
报告人: Hai Jiang教授,美国阿肯色州立大学(Arkansas State University, USA)

MapReduce is a parallel computing framework proposed by Google in 2006. The original C-version MapReduce and Java-version Hadoop from Apache have been widely adopted in Clouds for Big Data processing. MapReduce framework works on groups of computers for scalability and fault tolerance. However, the overheads in the communication channels and file systems slow down the whole process. We have ported MapReduce from computer clusters to massive GPU (Graphics Processing Unit) cores. The shared memory architecture helps reduce the communication overhead and the much more GPU cores increase the processing abilities dramatically and efficiently. Multiple GPU cards are used to scale up the processing ability. To tackle Big Data applications, streaming technology is adopted to pipeline task sequence and overlap their processing stages for execution speedup. Multiple advanced GPU processing features have been applied. The experimental results have demonstrated the effectiveness of the proposed approach.

Hai Jiang received his B.S. degree from Beijing University of Posts and Telecommunications, China, M.A. and Ph.D. degrees from Wayne State University, Detroit, MI, USA. He is a Professor in the Department of Computer Science at Arkansas State University, USA. Before joining Arkansas State University, he has spent four years in State key Laboratory of Switching Technology and Telecommunication Networks, Beijing, China as a research fellow and five years in industry with Ford Motor Company, Dearborn, MI, USA, where he held various technical positions. His current research interests include Parallel & Distributed Systems, Cloud Computing, Big Data, Cryptography, Computer & Network Security, High Performance Computing and Communication, and Modeling & Simulation.

Dr. Jiang is a professional member of ACM and IEEE computer society. He has published three books and more than 100 papers in refereed journals, conference proceedings and book chapters. He has been involved in more than 100 conferences and workshops as a program/workshop chair or as a program committee member. He serves as an editor for International Journal of High Performance Computing and Networking, International Journal of Computational Science and Engineering, and International Journal of Embedded Systems as well as a guest editor for IEEE Systems Journal, Cluster Computing, and Concurrency and Computation: Practice and Experience. He also serves on the editorial board of International Journal of Big Data Intelligence, GSTF Journal on Social Computing, Open Journal of Internet of Things, and The Scientific World Journal. He also chaired IEEE TCSC PhD Dissertation Award Selection Committee, 2017.