Code Perforation

Automatically and Dynamically Trading Accuracy for Performance and Power

Research Abstract

Many modern computations (such as video and audio encoders, Monte Carlo simulations, and machine learning algorithms) are designed to trade off accuracy in return for increased performance. To date, such computations typically use ad-hoc, domain-specific techniques developed specifically for the computation at hand. Our research explores a new general technique, code perforation, for automatically augmenting existing computations with the capability of trading off accuracy in return for performance. In contrast to existing approaches, which typically require the manual development of new algorithms, our implemented SpeedPress compiler can automatically apply code perforation to existing computations with no developer intervention whatsoever. The result is a transformed computation that can respond almost immediately to a range of increased performance demands while keeping any resulting output distortion within acceptable user-defined bounds.

We have used SpeedPress to automatically apply code perforation to applications from the PARSEC benchmark suite. The results show that the transformed applications can run as much as two to three times faster than the original applications while distorting the output by less than 10%. Because the transformed applications can operate successfully at many points in the performance/accuracy tradeoff space, they can (dynamically and on demand) navigate the tradeoff space to either maximize performance subject to a given accuracy constraint, or maximize accuracy subject to a given performance constraint. In addition we have developed the SpeedGuard runtime system which uses code perforation to enable applications to automatically adapt to challenging execution environments such as multicore machines that suffer core failures or machines that dynamically adjust the clock speed to reduce power consumption or to protect the machine from overheating.

Publications

Papers

Sasa Misailovic, Daniel M. Roy, Martin Rinard. Probabilistically Accurate Program Transformations. SAS, September, 2011 (Previous version)
Steilos Sidiroglou, Sasa Misailovic, Henry Hoffmann, and Martin Rinard. Managing Performance vs. Accuracy Trade-offs With Loop Perforation. ESEC/FSE. September, 2011
Henry Hoffmann, Stelios Sidiroglou, Michael Carbin, Sasa Misailovic, Anant Agarwal, and Martin Rinard. Dynamic Knobs for Responsive Power-Aware Computing. ASPLOS. March, 2011 (Previous version)
Martin Rinard, Henry Hoffmann, Sasa Misailovic, Stelios Sidiroglou. Patterns and Statistical Analysis for Understanding Reduced Resource Computing. OOPSLA Onward!. October, 2010
Sasa Misailovic, Stelios Sidiroglou, Henry Hoffmann, Martin Rinard. Quality of Service Profiling. ICSE. May, 2010

Technical Reports

Henry Hoffmann, Sasa Misailovic, Stelios Sidiroglou, Anant Agarwal, Martin Rinard. Using Code Perforation to Improve Performance, Reduce Energy Consumption, and Respond to Failures. MIT-CSAIL-TR-2009-042. September, 2009

Talks

Using Dynamic Loop Perforation to Adapt to Changing Computing Environments. Invited talk at Politecnico di Milano, January 2010.

Online Resources

MIT News Article on Loop Perforation (Provides a high-level overview of the project)
YouTube Channel (Shows videos encoded with both perforated and unperforated H.264 encoders)
Code Perforation Blog (Shows examples of the types output produced by perforated applications and the types of loops selected for perforation