Automate-CUDA

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.