High Performance Computing



  • High Performance Computing



The research on High Performance Computing (HPC) mainly focuses on theory, algorithms, and implementation technologies of using ultra-high performance parallel computers to solve large scale scientific and engineering problems. 


 (1)   Domestic-made high performance computers. The first China-made high performance personal computer, KD-50-I, was successfully designed by our school in 2007. Currently, we are working on the next generation of high performance computers based on the KD-50 series.


(2)   Algorithms for large scale parallel computing. Computing models are built according to the features of mainstream HPC architectures, large scale algorithms are then designed based on these models. In addition, research is also conducted on the how to measure the performance of algorithms in different parallel systems or programming environments.


(3)   System software and programming environments for HPC. We study various topics in HPC including operating system, programming, compilation, and tools/environment for programming. The goal is to design a parallel operating system that can fully exploit the power of a parallel computer; a parallel programming language with well-defined structure and easy to use; a compiler that can accelerate the speed of parallel programs; and tools/environment that make programming and debugging easy for programmers not from parallel computing area.


 (4)   Parallel software for typical large scale practical problems. In addition to the fundamental research, we also design parallel software for industry and for national security purpose. This includes the control system of the Huai River, weather forecasting, city emergency management, simulation and analysis of fire disaster, offensive and defensive communication, radar imaging, gene sequence analysis, and protein function prediction.


  • Sample Publications


Tianshi Chen, Ke Tang, Guoliang Chen, Xin Yao;Analysis of Computational Time of Simple Estimation of Distribution Algorithms;IEEE Transactions on Evolutionary Computation, 14(1): 1-22, February 2010.



Fei Peng, Ke Tang, Guoliang Chen, Xin Yao; Population-based Algorithm Portfolios for Numerical Optimization;IEEE Transactions on Evolutionary Computation, 14(5): 782-800, October 2010.


Shangfei Wang, Zhilei Liu, Siliang Lv, Yanpeng Lv, Guobing Wu, Peng Peng, Fei Chen, Xufa Wang;A Natural Visible and Infrared Facial Expression Database for Expression Recognition and Emotion Inference;IEEE trans. on Multimedia, 12(7), 682-691, 2010


Biao Xiang,Daxin Jiang, Jian Pei, Xiaohui Sun, Enhong Chen, Hang Li;Context-Aware Ranking in Web Search;ACM SIGIR 2010,451-458


Xiaoping Chen, Jianmin Ji, Jiehui Jiang, Guoqiang Jin, Feng Wang, Jiongkun Xie.   Developing High-level Cognitive Functions for Service Robots;Proc. of 9th Int. Conf. on Autonomous Agents and Multi-agent Systems (AAMAS 2010), Toronto, Canada, May 2010.


Feng Wu, Shlomo Zilberstein and Xiaoping Chen;Point-Based Policy Generation for Decentralized POMDPs;Proc. of 9th Int. Conf. on Autonomous Agents and Multi-agent Systems (AAMAS 2010), Toronto, Canada, May 2010.


Feng Wu, Shlomo Zilberstein, Xiaoping Chen;Trial-Based Dynamic Programming for Multi-Agent Planning; Proc. of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI 10), Atlanta, Georgia, USA


Feng Wu, Shlomo Zilberstein, Xiaoping Chen;Rollout Sampling Policy Iteration for Decentralized POMDPs;Proc. of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), Catalina Island, California, USA, July 8-11, 2010.


Tang Ke, Mei Yi, Yao Xin.; Memetic Algorithm with Extended Neighborhood Search for Capacitated Arc Routing Problems; IEEE Transactions on Evolutionary Computation, 13(5): 1151-1166, October, 2009.


Mei Yi, Tang Ke, Mei Yi, Yao Xin.;A Global Repair Operator for Capacitated Arc Routing Problem;IEEE Transactions on Systems, Man, and Cybernetics: Part B, 39(3): 723-734, June 2009.