GPU computing refers to using graphics processing units (GPUs) for general purpose (or non-graphics) computing. With the increasing demand for data processing, GPU computing is becoming a popular platform, ranging from smartphones to supercomputers, for high performance computing. This lecture series will cover the GPU computing architecture and the single-instruction multiple-thread (SIMT) programming model. Then, the focus will be shifted to software and hardware techniques to improve the efficiency of GPU computing. In other words, how can applications make best use of GPU resources. Detailed case studies will be used to show how to identify the performance bottlenecks and how to overcome them effectively. Finally, the lecture series will discuss some recent research works on GPU computing architecture.
Huiyang Zhou received the bachelor's degree in electrical engineering from Xian Jiaotong University, China, in 1992 and the Ph.D. degree in computer engineering from North Carolina State University in 2003. He is currently an associate professor in the Department of Electrical and Computer Engineering at North Carolina State University. Between 2003 and 2009, he was an assistant professor at the School of Electrical Engineering and Computer Science, University of Central Florida. His research focuses on high performance microarchitecture, low-power design, GPU Computing (General Purpose computing on Graphics Processing Units or GPGPU), architecture support for system dependability, and backend compiler optimization. He is a recipient of NSF CAREER award and a senior member of the ACM and IEEE.