Sakana AI, a Japanese artificial intelligence research company, has reported on the development of an AI CUDA Engineer framework for agentic artificial intelligence improvement in CUDA kernel.
CUDA is a low-level software closely tied to NVIDIA hardware, commonly used for running CUDA kernels written to run on graphic chips. Typically, developers write code using high-level frameworks like TensorFlow or PyTorch and then compile it into CUDA code.
The AI CUDA Engineer reads PyTorch code and converts it into CUDA, gradually refining the CUDA code format through a mix of different techniques until the most efficient CUDA code is achieved. In the worst-case scenario, the code may perform equally to regular compilation, but in the best-case scenario, it could be up to 85 times more efficient. Sakana AI suggests that AI CUDA Engineers have the potential to improve code speed by 10-100 times, although the performance table does not specify any cases reaching the 100-fold level.
Source: Sakana AI
TLDR: Sakana AI presents advancements in AI CUDA Engineer framework for improved performance in CUDA kernel development.
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