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General-purpose computing on graphics processing units — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-05T22:51:05.667Z
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General-purpose computing on graphics processing units — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_72fd04478ced (Metraipolis archive dc_72fd04478ced, sha256 749185e6d66c8d11b0bb442d2ee55f49c3fc877250b356f4d3a0058ad2e339e3)

BibTeX

@misc{dc72fd04478ced,
  title = {General-purpose computing on graphics processing units — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_72fd04478ced; sha256 749185e6d66c8d11b0bb442d2ee55f49c3fc877250b356f4d3a0058ad2e339e3},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing. Essentially, a GPGPU pipeline is a kind of parallel processing between one or more GPUs and CPUs, with special accelerated instructions for processing image or other graphic forms of data. While GPUs operate at lower frequencies, they typically have many times the number of processing elements. Thus, GPUs can process far more pictures and other graphical data per second than a traditional CPU. Migrating data into parallel form and then using the GPU to process it can (theoretically) create a large speedup. GPGPU pipelines were developed at the beginning of the 21st century for graphics processing (e.g. for better shaders). From the history of supercomputing it is well known that scientific computing drives the largest concentrations of Computing power in history, listed in the TOP500: the majority today utilize GPUs. The best-known GPGPUs are Nvidia Tesla that are used for Nvidia DGX, alongside AMD Instinct and Intel Gaudi.

Tema degli esploratori: calcolo e infrastrutture (deposito sistematico; categoria «GPGPU»).
Fonte: Wikipedia in inglese, voce «General-purpose computing on graphics processing units» — https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units
Wikidata Q971505 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q971505
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1377843309, consultata il 2026-10-05T22:46:04.792Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: fafcadb1a7a666f430aa2b2275b84a8f1fca2155d7d37521e82694ac2939f8e6