enDebug: A hardware-software framework for automated energy debugging

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@Article{Chen:2016:JPDC,
  author =       "Jie Chen and Guru Venkataramani",
  title =        "enDebug: A hardware-software framework for automated
                 energy debugging",
  journal =      "Journal of Parallel and Distributed Computing",
  year =         "2016",
  volume =       "96",
  pages =        "121--133",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Energy
                 profiling, Energy optimization",
  ISSN =         "0743-7315",
  DOI =          "doi:10.1016/j.jpdc.2016.05.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0743731516300351",
  abstract =     "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 uses
                 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 7percent energy savings beyond
                 the highest compiler optimization (including profile
                 guided optimization) settings on real-world Intel Core
                 i7 processors.",
}

Genetic Programming entries for Jie Chen Guru Prasadh Venkataramani

Citations