Proactive Control of Approximate Programs
| Autor: | Donald S. Fussell, Xin Sui, Keshav Pingali, Andrew Lenharth |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
010302 applied physics
Mathematical optimization Approximate computing Computer science Control (management) Constrained optimization Open-loop controller 020207 software engineering General Medicine 02 engineering and technology Energy minimization 01 natural sciences Computer Graphics and Computer-Aided Design 020202 computer hardware & architecture Set (abstract data type) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences Energy (signal processing) Software General Environmental Science |
| Zdroj: | ASPLOS |
| ISSN: | 1558-1160 0362-1340 |
| Popis: | Approximate computing trades off accuracy of results for resources such as energy or computing time. There is a large and rapidly growing literature on approximate computing that has focused mostly on showing the benefits of approximate computing. However, we know relatively little about how to control approximation in a disciplined way. In this paper, we address the problem of controlling approximation for 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. We formulate this control problem as a constrained optimization problem, and describe a system called Capri that uses machine learning to learn cost and error models for the program, and uses these models to determine, for a desired level of approximation, knob settings that optimize metrics such as running time or energy usage. Experimental results with complex benchmarks from different problem domains demonstrate the effectiveness of this approach. |
| Databáze: | OpenAIRE |
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