Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning

2025-09-30

AI Summary

In automated performance tuning, optimizer hyperparameters have long been overlooked, and their impact on overall tuning efficacy remains systematically uninvestigated. Method: This paper introduces the novel paradigm of “tuner hyperparameter optimization” to address efficiency bottlenecks caused by suboptimal hyperparameter configurations. We design a robust cross-search-space evaluation protocol, construct a reproducible benchmark dataset and open-source toolkit, and incorporate a low-cost simulation replay mechanism to enable efficient meta-strategy optimization—fully adhering to FAIR (Findable, Accessible, Interoperable, Reusable) principles. Contribution/Results: Experiments demonstrate that lightweight hyperparameter tuning improves tuner performance by 94.8% on average; integrating meta-strategy optimization further boosts average gain to 204.7%. This work establishes both theoretical foundations and practical pathways for self-enhancement in automated tuning frameworks.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Auto ML and Hyperparameter Tuning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• Optimizing hyperparameters in auto-tuning to enhance search efficiency
• Developing cost-effective methods for hyperparameter evaluation across search spaces
• Demonstrating significant performance gains through systematic hyperparameter tuning

Innovation

• Statistical method for hyperparameter performance evaluation
• Simulation mode reducing tuning costs significantly
• Meta-strategies optimizing hyperparameters with major improvements