报告题目：Predictable GPGPU computing for automotive applications
报告人：Cong Liu（Assistant Professor，Department of Computer Science，University of Texas at Dallas，USA））
Graphic processing units (GPUs) have seen wide-spread use in several computing domains as they have the power to enable orders of magnitude faster and more energy-efficient execution of many applications. Unfortunately, it is not straightforward to reliably adopt GPUs in many safety-critical embedded systems that require predictable real-time correctness, one of the most important tenets in certification required for such systems. An example is the advanced automotive system where timeliness of computations is an essential requirement of correctness due to the interaction with the physical world. In this talk, I will describe several system-level and algorithmic challenges on ensuring predictable real-time correctness in GPU-enabled systems, as well as our recent research results on using suspension-based approaches to resolve some of the issues. I will also briefly describe several other projects I am currently working on, including efficient and secure GPGPU computing for the cloud, and smart battery management for electric vehicles.
Cong Liu is currently a tenure-track assistant professor in the Department of Computer Science at the University of Texas at Dallas, after obtaining his Ph.D in Computer Science from the University of North Carolina at Chapel Hill in summer 2013. His current research focuses on Real-Time and Embedded Systems, GPGPU, and battery management for electric vehicles. He is the author and co-author of over 50 papers in premier journals and conferences such as RTSS, INFOCOM, RTAS, ICCPS, EMSOFT, ICNP. He received the Best Student Paper Award at the 30th IEEE Real-Time Systems Symposium, the premier real-time and embedded systems conference; he also received the best papers award at the 17th RTCSA. He is a member of ACM and IEEE.