arXiv:2606.26758v1 Announce Type: new Abstract: High-performance GPU kernels are critical for reducing the exponentially growing computational costs of large language models (LLMs), but their development heavily relies on manual tuning by domain experts. While recent advances in LLM-based approaches show promise for automating kernel generation, they still struggle to achieve both correctness and high performance. This limitation primarily arises from the lack of domain-specific optimization guidance, hindering effective exploration of the optimization space. We propose EGG, an Expert-Guided A
The accelerating computational demands of large language models are pushing the limits of current hardware optimization, necessitating new approaches to efficient kernel generation.
Improving the automation and performance of GPU kernel generation is critical for reducing operational costs and accelerating the development of advanced AI, impacting the economic viability and pace of AI innovation.
The reliance on manual expert tuning for GPU kernels can be significantly reduced, leading to faster development cycles and potentially more efficient AI infrastructure.
- · AI developers
- · Cloud providers
- · GPU manufacturers
- · LLM researchers
- · Manual kernel optimization specialists
Automated, high-performance kernel generation will lower the computational barriers for training and deploying large language models.
Reduced compute costs could accelerate the proliferation and sophistication of AI applications across various industries.
Increased accessibility to advanced AI development might decentralize AI innovation, potentially challenging the dominance of a few large players.
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Read at arXiv cs.AI