Automated Empirical Optimization of High Performance Floating Point Kernels

R. Clint Whaley
University of Texas, San Antonio
Wachman 447
Monday, March 25, 2013 - 11:00

In AEOS (Automated Empirical Optimization of Software), an automated suite of searches are combined with context-sensitive timers and various methods of performing code transformations to auto-adapt high performance kernels to hardware evolving at the frantic pace dictated by Moore's Law. The author's widely used ATLAS (Automatically Tuned Linear Algebra Software) was one of the pioneering packages that made AEOS the state- of-the-art way to produce and maintain HPC kernels. This talk outlines our approach to this critical area of investigation, the types of research that are required to advance the field, and future plans.