CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs
cs.LG
Submitted: 2026-05-08
Updated: 2026-05-08
Code: https://github.com/NVIDIA/compute-eval
Terminology
Sources
- Kevin: Multi-Turn RL for Generating CUDA Kernels
- AVO: Agentic Variation Operators for Autonomous Evolutionary Search
- CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
- FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
- Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations
- KernelBench: Can LLMs Write Efficient GPU Kernels?
- FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
- FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling
- CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization
- CUDABench: Benchmarking LLMs for Text-to-CUDA Generation
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