Efficient Construction of Large Search Spaces for Auto-Tuning
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916980116684800 |
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| author | Willemsen, Floris-Jan van Nieuwpoort, Rob V. van Werkhoven, Ben |
| author_facet | Willemsen, Floris-Jan van Nieuwpoort, Rob V. van Werkhoven, Ben |
| contents | Automatic performance tuning, or auto-tuning, accelerates high-performance codes by exploring vast spaces of code variants. However, due to the large number of possible combinations and complex constraints, constructing these search spaces can be a major bottleneck. Real-world applications have been encountered where the search space construction takes minutes to hours or even days. Current state-of-the-art techniques for search space construction, such as chain-of-trees, lack a formal foundation and only perform adequately on a specific subset of search spaces.
We show that search space construction for constraint-based auto-tuning can be reformulated as a Constraint Satisfaction Problem (CSP). Building on this insight with a CSP solver, we develop a runtime parser that translates user-defined constraint functions into solver-optimal expressions, optimize the solver to exploit common structures in auto-tuning constraints, and integrate these and other advances in open-source tools. These contributions substantially improve performance and accessibility while preserving flexibility.
We evaluate our approach using a diverse set of benchmarks, demonstrating that our optimized solver reduces construction time by four orders of magnitude versus brute-force enumeration, three orders of magnitude versus an unoptimized CSP solver, and one to two orders of magnitude versus leading auto-tuning frameworks built on chain-of-trees. We thus eliminate a critical scalability barrier for auto-tuning and provide a drop-in solution that enables the exploration of previously unattainable problem scales in auto-tuning and related domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26253 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Construction of Large Search Spaces for Auto-Tuning Willemsen, Floris-Jan van Nieuwpoort, Rob V. van Werkhoven, Ben Distributed, Parallel, and Cluster Computing Performance C.4; I.m; I.2.8 Automatic performance tuning, or auto-tuning, accelerates high-performance codes by exploring vast spaces of code variants. However, due to the large number of possible combinations and complex constraints, constructing these search spaces can be a major bottleneck. Real-world applications have been encountered where the search space construction takes minutes to hours or even days. Current state-of-the-art techniques for search space construction, such as chain-of-trees, lack a formal foundation and only perform adequately on a specific subset of search spaces. We show that search space construction for constraint-based auto-tuning can be reformulated as a Constraint Satisfaction Problem (CSP). Building on this insight with a CSP solver, we develop a runtime parser that translates user-defined constraint functions into solver-optimal expressions, optimize the solver to exploit common structures in auto-tuning constraints, and integrate these and other advances in open-source tools. These contributions substantially improve performance and accessibility while preserving flexibility. We evaluate our approach using a diverse set of benchmarks, demonstrating that our optimized solver reduces construction time by four orders of magnitude versus brute-force enumeration, three orders of magnitude versus an unoptimized CSP solver, and one to two orders of magnitude versus leading auto-tuning frameworks built on chain-of-trees. We thus eliminate a critical scalability barrier for auto-tuning and provide a drop-in solution that enables the exploration of previously unattainable problem scales in auto-tuning and related domains. |
| title | Efficient Construction of Large Search Spaces for Auto-Tuning |
| topic | Distributed, Parallel, and Cluster Computing Performance C.4; I.m; I.2.8 |
| url | https://arxiv.org/abs/2509.26253 |