Graphics processing units

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Information for Authority record
Name (Hebrew)
מעבדים גרפי
Name (Latin)
Graphics processing units
Name (Arabic)
وحدات معالجة الرسوميات
Other forms of name
GPUs (Graphics processing units)
Graphics engines
Graphics processors
Visual processing units
See Also From tracing topical name
Computer graphics
MARC
MARC
Other Identifiers
Wikidata: Q183484
Library of congress: sh2009010908
Sources of Information
  • Work cat.: Moore, C. A recurrent neural network implementation using the graphics processing unit, 2009.
  • Wikipedia, accessed Dec. 16, 2009:(A graphics processing unit or GPU, also occasionally called visual processing unit or VPU, is a specialized processor that offloads 3D graphics rendering from the microprocessor. It is used in embedded systems, mobile phones, personal computers, workstations, and game consoles. Modern GPUs are very efficient at manipulating computer graphics, and their highly parallel structure makes them more effective than general-purpose CPUs for a range of complex algorithms. In a personal computer, a GPU can be present on a video card, or it can be on the motherboard)
  • McGraw-Hill dictionary of scientific and technical terms, 2003:(Graphics processor - see graphics engine. Graphics engine - a specialized processor that carries out graphics processing independently of the main central processing unit. Also known as graphics processor)
  • GPU gems : programming techniques, tips, and tricks for real-time graphics, 2004.
  • Weiskopf, Daniel. GPU-based interactive visualization techniques, 2007.
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Wikipedia description:

A graphics processing unit (GPU) is a specialized electronic circuit designed for digital image processing and to accelerate computer graphics, being present either as a discrete video card or embedded on motherboards, mobile phones, personal computers, workstations, and game consoles. GPUs were later found to be useful for non-graphic calculations involving embarrassingly parallel problems due to their parallel structure. The ability of GPUs to rapidly perform vast numbers of calculations has led to their adoption in diverse fields including artificial intelligence (AI) where they excel at handling data-intensive and computationally demanding tasks. Other non-graphical uses include the training of neural networks and cryptocurrency mining.

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