时间：2009 年4月17日（周五）上午 9：00
报告题目： Practical Random Linear Network Coding on GPUs
Recently, random linear network coding has been widely applied in peer-to-peer network applications. Since it is difficult to verify the integrity of the encoded data, such systems can suffer from the famous pollution attack, in which a malicious node can send bad encoded blocks that consist of bogus data. Consequently, the bogus data will be propagated into the whole network at an exponential rate. Homomorphic hash functions (HHFs) have been designed to defend systems from such pollution attacks, but with a new challenge: HHFs require that net-work coding must be performed in GF(q), where q is a very large prime number. This greatly increases the computational cost of network coding, in addition to the already computational expensive HHFs. In this talk, we will first introduce GPU (Graphic Processing Units) computing. Then we introduce how to exploit the potential of the huge computing power of GPUs to reduce the computational cost of network coding and homomorphic hashing. With our network coding and HHF implementation on GPU, we observed significant computational speedup in comparison with the best CPU implementation. This implementation can lead to a practical solution for defending the pollution attacks in distributed systems.
Dr. Chu Xiaowen received his B.Eng. degree in the Computer Science from Tsinghua University, P. R. China, in 1999, and the Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology in 2003. He is currently an Assistant professor in the Department of Computer Science, Hong Kong Baptist University.
His research interests include distributed and parallel computing, Peer-to-peer Networks, Wireless Networks, and Optical WDM Networks. He has co-chaired a number of International conferences and symposiums. He is on the Editor Board of MDPI Journal of Algorithms.