•   When: Monday, April 03, 2017 from 11:00 AM to 12:00 PM
•   Speakers: Yanfei Guo
•   Location: Research Hall, Room 163
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Abstract:

As we move to the Exascale Computing, we can no longer rely on "free performance" through processor innovation. Power and other technological limitations make such a situation impossible.  Consequently, system architectures are undergoing fundamental changes—changes that will make existing methods for programming large high-performance systems prohibitively inefficient and will have a ripple effect across the software stack to the end applications. In this talk, I will identify the major challenges of future system architectures and review some of the efforts in tackling challenges of building efficient and highly-scalable runtime systems for both conventional programming models like MPI and emerging frameworks like MapReduce. I will also discuss the future directions for HPC runtime systems.

Bio:

Yanfei Guo received his B.S. degree in Computer Science and Technology from Huazhong University of Science and Technology, China, in 2010, and Ph.D. degree in Computer Science from the University of Colorado, Colorado Springs in 2015. He is currently a Postdoc Researcher in the Argonne National Lab. His research mainly focuses on programming models and runtime systems for High-Performance Computing, Big Data Processing and Cloud Computing.