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Arxiv
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CUDA-LLM: LLMs Can Write Efficient CUDA Kernels
- Large Language Models (LLMs) are being utilized for efficient CUDA kernel generation for GPUs.
- The challenge lies in creating deeply hardware-specific, performance-critical code for massively parallel GPUs.
- A novel framework called Feature Search and Reinforcement (FSR) is introduced for CUDA program optimization.
- FSR optimizes compilation, functional correctness, and runtime performance of CUDA programs.
- The framework is validated through extensive test cases and actual GPU kernel execution latency measurements.
- LLMs using FSR can generate syntactically and semantically correct CUDA code while refining it for efficiency.
- Evaluation of FSR on various CUDA kernels shows correctness rates and significantly improved execution speeds.
- Automatically generated kernels outperform human-written code by up to 179 times in execution speeds.
- The results indicate the potential of combining LLMs with performance reinforcement for GPU programming.
- LLMs empowered with FSR can streamline GPU programming for architecture-aware and performance-sensitive applications.
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