Estimating resource budgets to ensure autotuning efficiency
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Article in Periodical |
| Magazine / Source | PARALLEL COMPUTING |
| MU Faculty or unit | |
| Citation | |
| web | URL |
| Doi | http://dx.doi.org/10.1016/j.parco.2025.103126 |
| Keywords | Autotuning; Dynamic autotuning; Stopping condition; Tuning budget estimation; Regression models |
| Description | Many state-of-the-art HPC applications rely on autotuning to maintain peak performance. Autotuning allows a program to be re-optimized for new hardware, settings, or input — even during execution. However, the approach has an inherent problem that has yet to be properly addressed: since the autotuning process itself requires computational resources, it is also subject to optimization. In other words, while autotuning aims to decrease a program’s run time by improving its efficiency, it also introduces additional overhead that can extend the overall run time. To achieve optimal performance, both the application and the autotuning process should be optimized together, treating them as a single optimization criterion. This framing allows us to determine a reasonable tuning budget to avoid both undertuning, where insufficient autotuning leads to suboptimal performance, and overtuning, where excessive autotuning imposes overhead that outweighs the benefits of program optimization. In this paper, we explore the tuning budget optimization problem in detail, highlighting its interesting properties and implications, which have largely been overlooked in the literature. Additionally, we present several viable solutions for tuning budget optimization and evaluate their efficiency across a range of commonly used HPC kernels. |
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