Seminars & Colloquia

Xipeng Shen

The College of William and Mary

"An Input-Centric Paradigm for Program Dynamic Optimizations"

Thursday December 02, 2010 09:30 AM
Location: 3211, Engineering Building II NCSU Centennial Campus
(Visitor parking instructions)

 

Abstract:

Revolutions in program analysis and optimizations are imperative for meeting the challenges imposed by the trend towards exascale computing and the processor development towards massive parallelism and heterogeneity. This research addresses these challenges in two aspects. The first is on how to make compilers and runtime systems fully understand and accurately predict a program's dynamic behaviors. Such predictive capability is fundamental: Virtually all optimizations rely on the knowledge of how a program will behave to make appropriate decisions. The second is on how to translate the understanding of program behaviors into performance by effectively exploiting the memory hierarchy and growing concurrency in modern computing systems.

The talk starts with an overview on both aspects, and then concentrate on a new paradigm---input-centric paradigm---for program behavior analysis and optimizations. The new paradigm is distinctive in bringing program inputs into the focus of program optimizations. It addresses some dilemma in traditional program behavior analysis, and creates many new opportunities for modern computing.

Short Bio:

Xipeng Shen is an Assistant Professor of Computer Science at the College of William and Mary. He received his Ph.D. and Master degrees in Computer Science from University of Rochester. He is an IBM CAS Faculty Fellow, and a recipient of the Best Paper Award of ACM PPoPP 2010 and the National Science Foundation CAREER Award. His research in Compiler Technology and Programming Systems aims at helping programmers achieve high performance as well as good programming productivity on both uniprocessor and multiprocessor architectures. He is particularly interested in the effective usage of memory hierarchies, the exploitation of program inputs in program behavior analysis, and the employment of statistical learning in runtime systems and dynamic program optimizations.

Host: Frank Mueller/Jon Doyle, Dept. of Computer Science - NCSU

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