arXiv:2601.15727v3 Announce Type: replace Abstract: The performance of modern AI systems is fundamentally constrained by the quality of their underlying GPU kernels, which translate high-level algorithmic semantics into low-level hardware operations. Achieving near-optimal kernels requires expert-level understanding of hardware architectures and programming models, making kernel engineering a critical but notoriously time-consuming and non-scalable process. Recent advances in large language models and LLM-based agents have opened new possibilities for automating kernel generation and optimizat
Advances in large language models and LLM-based agents have reached a point where their capabilities can be applied to complex code generation and optimization, making automated kernel generation feasible.
Automating kernel generation could significantly reduce the time and expertise required for developing high-performance AI systems, accelerating research and deployment across various industries.
The bottleneck of expert-level hardware understanding for GPU kernel optimization may be alleviated, enabling faster iteration and broader access to high-performance computing capabilities.
- · AI developers
- · GPU manufacturers
- · Cloud computing providers
- · LLM developers
- · Manual kernel optimization specialists
- · Companies without access to advanced AI tools
Increased efficiency and performance gains for AI workloads due to optimized GPU kernels.
Reduced development costs and faster time-to-market for AI-powered products and services.
Democratization of high-performance AI development, potentially leading to a new wave of innovation and specialized AI applications.
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Read at arXiv cs.LG