Program autotuning has been demonstrated in many domains to achieve better or more portable performance. However, autotuners themselves are often not very portable between projects because using a domain informed search space representation is critical to achieving good results and because no single search technique performs best for all problems. OpenTuner is a new framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the tuned program. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously, techniques which perform well will receive larger testing budgets and techniques which perform poorly will be disabled.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
- Gadioli, Davide; Vitali, Emanuele; Palermo, Gianluca; Silvano, Cristina: mARGOt: a dynamic autotuning framework for self-aware approximate computing (2019)
- Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
- Inala, Jeevana Priya; Singh, Rohit; Solar-Lezama, Armando: Synthesis of domain specific CNF encoders for bit-vector solvers (2016)
Further publications can be found at: http://opentuner.org/publications/