The impending multi/many-core processor revolution requires that programmers leverage explicit concurrency to improve performance. Unfortunately, a large body of applications/algorithms are inherently hard to parallelize due to execution order constraints imposed by data and control dependencies or being sensitive to their input data and not scale perfectly, leaving several cores idle. The goal of this research is to enable such applications leverage multi/many-cores efficiently to improve their performance.

Hari Pyla is a Principal Software Engineer R&D, at AdaptiveApps, a startup in virtualization and cloud computing. His research interests are in the areas of systems research, concurrent programming, multi/many core architectures, compilers, runtime systems, programming models, and high-end computing. Prior to his tenure at AdaptiveApps, he worked at Microsoft Research, IBM Research Lab, Microsoft, and NetApp. He has co-authored several peer-reviewed research papers and he serves as a technical reviewer and program committee member for technical conferences. He has a Ph.D. in Computer Science from Virginia Tech.