# Automate-CUDA

- **Event:** [OpenEnv Hackathon SF](https://cerebralvalley.ai/e/openenv-hackathon-sf)
- **When:** Mar 7 at 9:00 AM – Mar 8 at 12:00 AM (PST)
- **Where:** Shack15, San Francisco, CA
- **Team:** [William Chen](https://cerebralvalley.ai/u/willcreateagi), [Yiying Xie](https://cerebralvalley.ai/u/Irene_xie)
- **GitHub:** https://github.com/OCWC22/A100-CUDA-RL
- **Website:** https://colab.research.google.com/drive/145HxWWaJH0drhmSzfqNGSisM9K6mvQzT?usp=sharing
- **Demo video:** https://youtu.be/Rdz14ox3JhQ
- **Hugging Face:** https://huggingface.co/AutomatedCUDA
- **Gallery:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/76

KernelForge-OpenEnv is an autonomous CUDA kernel optimization system that uses reinforcement learning to train language models to write high-performance GPU kernels for NVIDIA A100s. Built on Meta-PyTorch's OpenEnv framework, it creates a closed-loop pipeline where a model generates CUDA code, compiles and benchmarks it on real hardware, then learns from the results across multi-turn episodes.

Writing optimized CUDA kernels is one of the hardest tasks in GPU programming, requiring deep expertise in memory hierarchies, warp scheduling, and architecture-specific tuning. KernelForge automates this by bootstrapping from expert-curated datasets and progressively training through a 3-stage curriculum using GRPO, with discrete rewards based on whether generated kernels beat PyTorch baselines. It effectively teaches a language model to become a CUDA performance engineer through trial-and-error on real GPUs.

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