Keynote 1

Michael Hind
Research Staff Member and Manager, Dynamic Optimization Group
IBM T. J. Watson Research Center
 

Abstract

Modern commercial software is written in languages that execute on a virtual machine.  Such languages often have dynamic features that require rich runtime support and preclude traditional static optimization.  Implementations of these languages have employed dynamic optimization strategies to achieve significant performance improvements.

In this talk I will describe some of these strategies and demonstrate their effectiveness. I will then argue that further advances in this field are being hindered by our bias toward adapting traditional static optimization techniques.  Instead, we need to think more like a computer architect to create new approaches to optimization in virtual machines.

Bio:

Michael Hind received his Ph.D. from New York University in 1991. From 1992-1998, Michael was an assistant and associate professor of computer science at the State University of New York at New Paltz. In 1998, Michael became a Research Staff Member in the Software Technology Department at the IBM T.J. Watson Research Center, working on the Jalapeno project, the project that produced the open source Jikes RVM. In 2000, he became the manager of the Dynamic Optimization Group at IBM Research. Michael has served on over a dozen program committees, given talks at top universities and conferences, and co-authored over 35 publications. His research interests include adaptive optimization, program analysis, and software optimizations to address memory latency.