Undergrad Documents Self-Taught Journey Into GPU Computing and CUDA Basics

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A self-taught undergraduate programmer recently shared how curiosity about GPU-related online discussions led them to explore general-purpose GPU computing. They discovered that using a GPU for computation is not plug-and-play and depends heavily on the hardware manufacturer's ecosystem. NVIDIA's proprietary CUDA platform, launched in 2006, dominates the space — especially in AI and deep learning — while AMD offers the open-source ROCm framework built around a CUDA-compatible interface called HIP. A tool called HIPIFY allows developers to convert CUDA code for use on AMD hardware, reflecting AMD's strategy of openness to compete with NVIDIA's entrenched ecosystem. The writer describes still being in the early stages, focused on building foundational understanding of CUDA concepts like threads, memory transfer, and parallel execution models.

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