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Developing high-performance CUDA kernels requires a deep understanding of GPU architecture, efficient memory management, and careful code tuning to fully exploit the hardware. This guide offers a step-by-step approach to writing CUDA kernels that are both correct and highly performant.

This guide provides step-by-step instructions for setting up a clean installation of Ubuntu on Windows Subsystem for Linux 2 (WSL2), followed by installing CUDA, cuDNN, and PyTorch for machine learning development.