Past Event: Babuška Forum

Model-based Control of Approximate Programs

Keshav Pingali, Professor, ICES, CS, UT Austin

Abstract

Approximate computing trades off accuracy of results for resources such as energy, computing time, and memory usage. There is a large and rapidly growing literature on approximate computing that has focused mostly on showing the benefits of approximate computing or on programming constructs for verifying that approximation is confined to certain parts of the program such as computations that do not affect control-flow. However, we know relatively little about how to control approximation in a disciplined way. In this talk, we address the problem of proactive control of approximation in non-streaming programs that have a set of "knobs'' that can be dialed up or down to control the level of approximation of different components in the program. Our approach uses machine learning techniques to learn cost and error models for the program, and uses these models to choose, for a desired level of approximation, knob settings that minimize running time or energy consumption. Experimental results with several complex benchmarks from different problem domains show that this is a promising approach for proactive control of approximate programs. Bio: Dr. Pingali is a Professor in the Department of Computer Science at the University of Texas at Austin, and he holds the W.A."Tex" Moncrief Chair of Computing in the Institute for Computational Engineering and Sciences (ICES) at UT Austin. He was on the faculty of the Department of Computer Science at Cornell University from 1986 to 2006, where he held the India Chair of Computer Science. Prof. Pingali is a Fellow of the ACM, IEEE and AAAS, and a Distinguished Alumnus of IIT Kanpur, India. He was the co-Editor-in-chief of the ACM Transactions on Programming Languages and Systems, and currently serves on the editorial boards of the ACM Transactions on Parallel Computing, the International Journal of Parallel Programming and Distributed Computing. He has also served on the NSF CISE Advisory Committee (2009-2012).