Sadayappan is a Professor in the Department of Computer Science and Engineering at The Ohio State University. His primary research interests center around performance optimization and compiler/runtime systems for high-performance computing, with special emphasis on high-performance frameworks that enable high productivity for application developers in scientific computing. Two recent projects include a polyhedral framework for automatic parallelization and data locality optimization, and the Tensor Contraction Engine - a domain-specific compiler/runtime system to automatically transform high-level specifications into efficient parallel programs, for a class of high accuracy ab initio models in quantum chemistry.
Recent trends in architecture are making multicore parallelism as well as heterogeneity ubiquitous. This creates significant chalenges to application developers as well as compiler implementations. Currently it is impossible to achieve performance portability of high-performance applications from a single version of a program - different implementations are necessary for different target platforms, e.g., for multicore CPUs versus GPUs.
A promising approach to seek performance portability, i.e., "write once, execute anywhere," is via identifying suitable domain-specific abstractions and compiler techniques to transform high-level specifications automatically to high-performance implementations on different targets. This talk will discuss efforts to develop performance-portable compiler techniques for domain-specific abstractions.