I am an associate professor in the Computer and Information Sciences Department and a JP Morgan Chase Faculty Fellow in the Institute for Financial Services Analytics. I also have a joint appointment in the Electrical and Computer Engineering Department. My research interests are in high-performance computing, machine learning, predictive analytics, and the application of these technologies to hard problems that if solved will make a societal and/or industrial impact.
I was one of the first researchers to introduce the use of machine learning to optimize an optimizing compiler itself. Compilers typically contain many heuristics to solve hard problems approximately and efficiently. Finding heuristics that perform well on a broad range of applications and processors is one of the most complex tasks faced by compiler writers. My research involves using machine learning techniques to automatically construct compiler optimization heuristics. I have shown that this technique can completely eliminate the human from heuristic design. My research on applying machine learning to compiler optimizations received the NSF CAREER award.
* Automatic Construction of Inlining Heuristics using Machine Learning
Sameer Kulkarni, John Cavazos, Christian Wimmer, and Douglas Simon.
* Software Automatic Tuning: From Concepts to State-of-the-Art Results.
Editors : Ken Naono, Keita Teranishi, John Cavazos, and Reiji Suda.
New Benchmark Suites
* Quantlib/GPU This benchmark suite is aimed at those who work with financial code to see how certain code paths can be targeted to accelerators.
* PolyBench/GPU Is a collection of benchmark kernels that exhibit parallelism and have been ported to accelerators using a variety of different parallel languages.
This research was generously sponsored by the National Science Foundation, DARPA, JP Morgan, and Google.
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