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PyTorch is a machine learning library based on the Torch library, [4] [5] [6] used for applications such as computer vision and natural language processing, [7] originally developed by Meta AI and now part of the Linux Foundation umbrella.
The main competing factor was the price of hardware and raw performance in 3D computer games, which is greatly affected by the efficient translation of API calls into GPU opcodes. The display driver and the video decoder are inherent parts of the graphics card: hardware designed to assist in the calculations necessary for the decoding of video ...
To improve performance, each program draws to its own independent memory buffer instead of to a slow graphical subsystem. (In Windows Vista, each hardware overlay is more correctly known as a Direct3D surface). Then the system's GPU assembles each of the windows into a single display screen in real time. With modern GPUs capable of advanced 3D ...
General-purpose computing on graphics processing units (GPGPU, or less often GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU).
Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. [3] It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created by the Idiap Research Institute at EPFL. Torch development moved in 2017 to PyTorch, a port of the library to Python. [4] [5] [6]
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The GPU, [3] or graphics processing unit, is the unit that allows the graphics card to function. It performs a large amount of the work given to the card. The majority of video playback on a computer is controlled by the GPU. Once again, a GPU can be either integrated or dedicated.
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