arXiv:2605.30359v1 Announce Type: cross Abstract: Generating high-performance GPU kernels remains challenging due to the need for both correctness and hardware-aware optimization. While large language models (LLMs) show promise in code generation, they often fail to produce kernels that are both correct and efficient. We propose Kernel Foundry, a diagnosis-driven evolutionary framework for automatic GPU kernel optimization. Our method combines expert-guided, retrieval-augmented initialization with a multi-island evolutionary search, where candidate kernels are iteratively refined using structu
The increasing complexity of GPU architectures and the limitations of general-purpose LLMs for highly optimized code are driving the need for specialized kernel optimization tools.
Improving the efficiency of GPU kernels directly translates to more performant AI models and compute infrastructure, impacting the entire AI development pipeline.
The ability to automatically generate and optimize high-performance GPU kernels could significantly reduce the development time and expertise required for complex AI/ML workloads.
- · AI/ML developers
- · GPU manufacturers
- · Cloud computing providers
- · High-performance computing (HPC) sector
- · Manual kernel optimization specialists
Faster and more efficient AI model training and inference become more accessible.
Reduced operational costs for large-scale AI deployments due to optimized hardware utilization.
Acceleration of AI research and deployment across various industries as compute bottlenecks are alleviated.
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Read at arXiv cs.LG