Current programming models for Graphics Processing Units (GPUs), such as OpenCL and CUDA, require each computational node to be equipped with one or more local GPUs for applications to be able to use them. Recent advances in cloud computing systems, on the other hand, have advocated using virtualization techniques to decouple the application view of "local hardware resources" from the physical hardware itself, thus allowing applications to transparently utilize remote hardware. In this seminar, I will talk about VOCL (standing for Virtual OpenCL), which is a new implementation of the OpenCL programming model that provides the OpenCL API while allowing an application to view all GPUs in the system (including remote GPUs) as local virtual GPUs. I will describe the overall idea of VOCL, its expected role in cloud computing systems such as Magellan, the research ideas that went into designing and optimizing it, and some performance numbers. I will also briefly talk about some of the extensions for VOCL that we are currently working on to allow efficient coordinated resource management with virtualized GPU resources.

Shucai Xiao is a PhD candidate in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, under the supervision of Prof. Wu-chun Feng. He received his BS and MS degrees from the Beijing University of Posts and Telecommunications and Tsinghua University, respectively, of China. His research interest is programming abstractions and optimizations for general purpose computation on graphics processing units (GPUs). He was an intern at Argonne National Laboratory for transparent virtualization of GPUs in cloud computing systems.