- Boyana Norris
A growing disparity between supercomputer computation speeds and I/O rates makes it increasingly infeasible for applications to save all results for offline analysis. Instead, applications must analyze and reduce data in situ so as to output only those results needed to answer target scientific question(s). This change in focus complicates application and experiment design and introduces algorithmic, implementation, and programming model challenges that are unfamiliar to many scientists and that have major implications for the design of various elements of supercomputer systems. I will give an overview of these challenges and describe methods and tools that we are developing to enable experimental exploration of algorithmic, software, and system design alternatives, before diving into models for the optimal scheduling of in situ analyses.
Dr. Todd Munson received a B.S. in Computer Science from the University of Nebraska in 1995, and an M.S. in 1996 and Ph.D. in 2000 in Computer Science from the University of Wisconsin at Madison. He is a Computational Scientist in the Mathematics and Computer Science Division at Argonne National Laboratory, a Senior Scientist in the Consotrium for Advanced Science and Engineering at the University of Chicago and Argonne National Laboratory, and the Deputy Director for the Co-Design Center for Online Data Analysis and Reduction at the Exascale, part of the Exascale Computing Project. The primary focus of his research is developing algorithms for numerical optimization problems and variational inequalities on high-performance computers and applying these methods to novel applications. He has been widely recognized for his contributions. Among other honors he was he was awarded both a Presidential Early Career Award for Scientists and Engineers from the Executive Office of the President of the United States in 2006 and the Beale-Orchard-Hayes Prize from the Mathematical Programming Society in 2003.