{"ID":2862812,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26300","arxiv_id":"2509.26300","title":"Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning","abstract":"Automatic performance tuning (auto-tuning) is widely used to optimize performance-critical applications across many scientific domains by finding the best program variant among many choices. Efficient optimization algorithms are crucial for navigating the vast and complex search spaces in auto-tuning. As is well known in the context of machine learning and similar fields, hyperparameters critically shape optimization algorithm efficiency. Yet for auto-tuning frameworks, these hyperparameters are almost never tuned, and their potential performance impact has not been studied. We present a novel method for general hyperparameter tuning of optimization algorithms for auto-tuning, thus \"tuning the tuner\". In particular, we propose a robust statistical method for evaluating hyperparameter performance across search spaces, publish a FAIR data set and software for reproducibility, and present a simulation mode that replays previously recorded tuning data, lowering the costs of hyperparameter tuning by two orders of magnitude. We show that even limited hyperparameter tuning can improve auto-tuner performance by 94.8% on average, and establish that the hyperparameters themselves can be optimized efficiently with meta-strategies (with an average improvement of 204.7%), demonstrating the often overlooked hyperparameter tuning as a powerful technique for advancing auto-tuning research and practice.","short_abstract":"Automatic performance tuning (auto-tuning) is widely used to optimize performance-critical applications across many scientific domains by finding the best program variant among many choices. Efficient optimization algorithms are crucial for navigating the vast and complex search spaces in auto-tuning. As is well known...","url_abs":"https://arxiv.org/abs/2509.26300","url_pdf":"https://arxiv.org/pdf/2509.26300v1","authors":"[\"Floris-Jan Willemsen\",\"Rob V. van Nieuwpoort\",\"Ben van Werkhoven\"]","published":"2025-09-30T14:14:01Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.DC\",\"cs.PF\"]","methods":"[]","has_code":false}