ADAPT: Automated De-coupled Adaptive Program Transformation. Dynamic program optimization offers performance improvements far beyond those possible with traditional compile-time optimization. These gains are due to the ability to exploit both architectural and input data set characteristics that are unknown prior to execution time. In this paper, we propose a novel framework for dynamic program optimization, ADAPT (Automated De-coupled Adaptive Program Transformation), that builds on the strengths of existing approaches. The key to our framework is the de-coupling of the dynamic compilation of new code variants from the dynamic selection of these variants at their points of use. This allows code generation to occur concurrently with program execution, removing dynamic compilation overheads from the critical path. We present a compilation system, based on the Polaris optimizing compiler, that automatically applies this framework to general ”plugged-in” optimization techniques. We evaluate our system on three programs from the SPEC floating point benchmark suite by dynamically applying loop distribution, loop unrolling, loop tiling and automatic parallelization. We show that our techniques can improve performance by as much as 70% over statically optimized code.

References in zbMATH (referenced in 6 articles )

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  1. Gadioli, Davide; Vitali, Emanuele; Palermo, Gianluca; Silvano, Cristina: mARGOt: a dynamic autotuning framework for self-aware approximate computing (2019)
  2. Sokolov, R. A.; Ermolovich, A. V.: Background optimization in full system binary translation (2012) ioport
  3. Fursin, Grigori; Kashnikov, Yuriy; Memon, Abdul Wahid; Chamski, Zbigniew; Temam, Olivier; Namolaru, Mircea; Yom-Tov, Elad; Mendelson, Bilha; Zaks, Ayal; Courtois, Eric; Bodin, Francois; Barnard, Phil; Ashton, Elton; Bonilla, Edwin; Thomson, John; Williams, Christopher K. I.; O’Boyle, Michael: Milepost GCC: Machine learning enabled self-tuning compiler (2011) ioport
  4. Lee, Jaejin; Park, Jung-Ho; Kim, Honggyu; Jung, Changhee; Lim, Daeseob; Han, Sangyong: Adaptive execution techniques of parallel programs for multiprocessors (2010)
  5. Lee, Yoon-Ju; Diniz, Pedro C.; Hall, Mary W.; Lucas, Robert: Empirical optimization for a sparse linear solver: A case study (2005) ioport
  6. Eigenmann, Rudolf; Voss, Michael J.: Towards a compilation paradigm for computational applications on the information power grid (2000)