GPU Programming

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GPU programming focuses on how software uses the GPU’s parallel hardware. GPUs excel when the same kind of math is applied across many pieces of data at once, which matches the heavy matrix operations common in deep learning. The goal is to organize the computation so the GPU does useful work continuously.

Most developers rely on frameworks like PyTorch or TensorFlow instead of writing low-level kernels. Even then, GPU programming still shows up in performance decisions. How tensors are shaped, how memory is accessed, and how operations are arranged can determine whether the GPU runs efficiently or stalls. When training feels slow despite strong hardware, the cause is often in these software-level choices.

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