Monday February 10, 12pm-1pm, 1116-E Klaus
Automating the Actions of Dense Linear Algebra Experts
Ph.D. candidate in The University of Texas at Austin
After decades of studying dense linear algebra (DLA) algorithms and their implementations on many hardware architectures, experts still spend a lot of time porting code to new architectures. Entire libraries are developed by experts re-applying their design knowledge repeatedly. We explore how to encode that knowledge of core algorithms and software components in the style of Design by Transformation (DxT) to generate high-performance code automatically for distributed-memory systems. We compare our performance results to that of hand-written code, our results demonstrate knowledge reuse, and we explore how proper abstractions in domain-specific languages and domain representations are essential to this work. Finally, we explain what it takes to retarget a DxT knowledge base to generate shared-memory code for some base DLA operations, using code generation to be more productive in exploring implementation ideas.
Bryan Marker is a Ph.D. candidate in computer science at The University of Texas at Austin. He was previously a software engineering at National Instruments implementing programming constructs for a graphical, dataflow language and earned bachelors in computer science and mathematics from The University of Texas at Austin before that. His research overlaps high-performance computing, software engineering, programming languages, and compilers to demonstrate how the design decisions of expert developers can be encoded and automatically applied to generate code. Bryan holds a National Science Foundation Graduate Research Fellowship and a Sandia Graduate Fellowship.