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Because the GPU has access to every draw operation, it can analyze data in these forms quickly, whereas a CPU must poll every pixel or data element much more slowly, as the speed of access between a CPU and its larger pool of random-access memory (or in an even worse case, a hard drive) is slower than GPUs and video cards, which typically ...
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.
In computing, external memory algorithms or out-of-core algorithms are algorithms that are designed to process data that are too large to fit into a computer's main memory at once. Such algorithms must be optimized to efficiently fetch and access data stored in slow bulk memory ( auxiliary memory ) such as hard drives or tape drives , or when ...
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]
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.
GDDR5X SDRAM on an NVIDIA GeForce GTX 1080 Ti graphics card. Video random-access memory (VRAM) is dedicated computer memory used to store the pixels and other graphics data as a framebuffer to be rendered on a computer monitor. [1] It often uses a different technology than other computer memory, in order to be read quickly for display on a screen.
When it was first introduced, the name was an acronym for Compute Unified Device Architecture, [3] but Nvidia later dropped the common use of the acronym and now rarely expands it. [4] CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [5]
Ampere is the codename for a graphics processing unit (GPU) microarchitecture developed by Nvidia as the successor to both the Volta and Turing architectures. It was officially announced on May 14, 2020 and is named after French mathematician and physicist André-Marie Ampère.