GPU Technologies
TLDR: GPU technologies encompass the hardware innovations and software innovations that enable graphics processing units (GPUs) to perform tasks beyond rendering, including machine learning, data processing, and video encoding. Modern GPU technologies leverage GPU parallel processing capabilities, advanced architectures, and AI-powered features to accelerate performance for gaming, professional workloads, and scientific computing. Innovations like ray tracing, DLSS, and CUDA have redefined the role of GPUs in modern computing.
https://en.wikipedia.org/wiki/Graphics_processing_unit
A major advancement in GPU technologies is the integration of dedicated cores for specific tasks, such as RT Cores for ray tracing and Tensor Cores for AI processing, seen in architectures like NVIDIA RTX and AMD RDNA. These cores enable real-time lighting effects and AI-driven enhancements like Deep Learning Super Sampling (DLSS). Open standards like Vulkan and DirectX further enhance cross-platform support for gaming and professional applications, optimizing performance and visual fidelity.
https://www.nvidia.com/en-us/geforce/technologies/rtx/
Beyond gaming, GPU technologies power workloads in machine learning, data science, and 3D rendering. Frameworks like CUDA and OpenCL enable AI developers to harness the massive parallelism of GPUs for tasks like training neural networks and analyzing large datasets. Innovations in GPU memory architecture, such as Infinity Cache and HBM (High Bandwidth Memory), further enhance GPU efficiency and performance. These technologies have solidified the role of GPUs as essential tools across a wide variety industries.