Atune-IL: An Instrumentation Language for Auto-tuning Parallel Applications. Auto-tuners automate the performance tuning of parallel applications. Three major drawbacks of current approaches are 1) they mainly focus on numerical software; 2) they typically do not attempt to reduce the large search space before search algorithms are applied; 3) the means to provide an auto-tuner with additional information to improve tuning are limited. Our paper tackles these problems in a novel way by focusing on the interaction between an auto-tuner and a parallel application. In particular, we introduce Atune-IL, an instrumentation language that uses new types of code annotations to mark tuning parameters, blocks, permutation regions, and measuring points. Atune-IL allows a more accurate extraction of meta-information to help an auto-tuner prune the search space before employing search algorithms. In addition, Atune-IL’s concepts target parallel applications in general, not just numerical programs. Atune-IL has been successfully evaluated in several case studies with parallel applications differing in size, programming language, and application domain; one case study employed a large commercial application with nested parallelism. On average, Atune-IL reduced search spaces by 78%. In two corner cases, 99% of the search space could be pruned.

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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. Ivanenko, P. A.; Doroshenko, A. Yu.: Method of automated generation of autotuners for parallel programs (2014) ioport
  3. Schaefer, Christoph A.; Pankratius, Victor; Tichy, Walter F.: Atune-IL: An instrumentation language for auto-tuning parallel applications (2009) ioport