Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search
As high-performance computing and AI workloads become increasingly dependent on GPUs, maintaining high performance across rapidly evolving hardware generations has become a major challenge. Developers often spend months tuning scientific applications to fully exploit new architectures, navigating a complex optimization space that spans algorithm design, source implementation, compiler flags and pass sequences, and kernel launch parameters. Existing approaches can effectively search parts of this space in isolation, such as launch configurations or compiler settings, but optimizing across the full space still requires substantial human expertise and iterative manual effort. In this paper, ...
| Authors | Daniel Nichols, Konstantinos Parasyris, Caetano Melone et al. |
|---|---|
| Primary category | cs.DC |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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