Performance is arguably the most crucial attribute that reflects the quality of a configurable
software system. However, given the increasing scale and complexity of modern software …

Machine learning (ML) methods has recently contributed very well in the advancement of the
prediction models used for energy consumption. Such models highly improve the accuracy …

High performance computing applications, runtimes, and platforms are becoming more
configurable to enable applications to obtain better performance. As a result, users are …

As we enter the exascale computing era, efficiently utilizing power and optimizing the
performance of scientific applications under power and energy constraints has become …

Sobol indices are a widespread quantitative measure for variance-based global sensitivity
analysis, but computing and utilizing them remains challenging for high-dimensional …

Predicting the performance of highly configurable software systems is the foundation for
performance testing and quality assurance. To that end, recent work has been relying on …

We develop a ytopt autotuning framework that leverages Bayesian optimization to explore
the parameter space search and compare four different supervised learning methods within …

Tuning application parameters for optimal performance is a challenging combinatorial
problem. Hence, techniques for modeling the functional relationships between various input …

Machine/deep learning models have been widely adopted to predict the configuration
performance of software systems. However, a crucial yet unaddressed challenge is how to …

Ytopt is a Python machine-learning-based autotuning software package developed within
the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search …