Energy consumption by software applications is one of the critical issues that determine the future of multicore software development. Inefficient software has been often cited as a major reason for wasteful energy consumption in computing systems. Without adequate tools, programmers and compilers are often left to guess the regions of code to optimize, that results in frustrating and unfruitful attempts at improving application energy. In this paper, we propose enDebug, an energy debugging framework that aims to automate the process of energy debugging. It first profiles the application code for high energy consumption using a hardware-software cooperative approach. Based on the observed application energy profile, an automated recommendation system that utilizes artificial selection genetic programming is used to generate the energy optimizing program mutants while preserving functional accuracy. We demonstrate the usefulness of our framework using several Splash-2, PARSEC-1.0 and SPEC CPU2006 benchmarks, where we were able to achieve up to 7% energy savings beyond the highest compiler optimization (including profile guided optimization) settings on real-world Intel Core i7 processors.
Chen, J., & Venkataramani, G. (2016). EnDebug: A hardware-software framework for automated energy debugging. Journal of Parallel and Distributed Computing, 96, 121–133. https://doi.org/10.1016/j.jpdc.2016.05.005