AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis
arXiv:2606.09682v1 Announce Type: new Abstract: AutoMegaKernel (AMK) compiles a HuggingFace Llama-family model into a single persistent cooperative CUDA kernel that runs the whole forward pass in one launch, with no per-model hand-written CUDA. The contribution is the system, not raw speed. A frozen schedule-IR validator statically certifies deadlock-freedom and race-freedom via static graph checks (not a mechanized proof), so an unsafe agent-proposed schedule is rejected before launch: across 7,160 adversarial schedules (6,091 unsafe) it had zero false-accepts and accepted all 360 real loweri
The increasing complexity and scale of AI models like Llama require more efficient execution paradigms, pushing research towards novel kernel synthesis and optimization techniques.
This work introduces a validated system for generating highly optimized, deadlock- and race-free CUDA kernels for large AI models, potentially streamlining deep learning compiler development and improving hardware utilization.
The system reduces the need for manual CUDA optimization and provides static guarantees for kernel safety, shifting the development burden from hand-tuned code to automated, validated synthesis.
- · AI model developers
- · GPU manufacturers
- · Cloud providers
- · Deep learning compiler teams
- · Manual CUDA optimization specialists
Increased efficiency and reliability in deploying large language models on GPU hardware.
Faster iteration cycles for AI researchers and engineers due to automated and validated kernel synthesis.
Lower operational costs for running large AI models, potentially accelerating their widespread adoption and deployment.
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Read at arXiv cs.LG