Show HN: UNet diffusion model in pure CUDA
The GitHub content details optimizing a UNet diffusion model in C++/CUDA to match PyTorch's performance. It covers custom convolution kernels, forward pass improvements, backward pass challenges, and future optimization plans.
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The GitHub content discusses the optimization journey of a UNet diffusion model training in C++/CUDA to match PyTorch's performance. It covers the implementation of various kernels, including custom convolution kernels for enhanced performance. The forward pass is highlighted as time-consuming, with optimizations focusing on residual blocks and convolutions. Challenges in the backward pass involve convolution computations and memory loads for gradient calculations. Future plans include further optimizing convolution kernels, addressing backward pass challenges, and enhancing other kernels like attention. The project draws inspiration from Karpathy's work and Boehm's blog on CUDA kernel optimization. The content offers a comprehensive view of the project's evolution, emphasizing performance enhancements from initial versions to the latest optimizations.
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