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Folding@home — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Folding@home
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-05T23:26:31.420Z
SHA-256 of the archived text: 40cf8580c21f0f9cc78fa1fba37701032797fe197ce28d2860c10b96238df776
Stable URL of this page: https://metraipolis.com/documento/dc_8b2ffee09313

How to cite

Folding@home — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Folding@home (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_8b2ffee09313 (Metraipolis archive dc_8b2ffee09313, sha256 40cf8580c21f0f9cc78fa1fba37701032797fe197ce28d2860c10b96238df776)

BibTeX

@misc{dc8b2ffee09313,
  title = {Folding@home — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Folding@home},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_8b2ffee09313; sha256 40cf8580c21f0f9cc78fa1fba37701032797fe197ce28d2860c10b96238df776},
  year = {2026}
}

Archived text

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

Folding@home (FAH or F@h) is a distributed computing project aimed to help scientists develop new therapeutics for a variety of diseases by the means of simulating protein dynamics. This includes the process of protein folding and the movements of proteins, and is reliant on simulations run on volunteers' personal computers. Folding@home is currently based at the University of Pennsylvania and led by Greg Bowman, a former student of Vijay Pande. The project utilizes graphics processing units (GPUs), and central processing units (CPUs) including ARM processors like those on the Raspberry Pi for distributed computing and scientific research. The project uses statistical simulation methodology that is a paradigm shift from traditional computing methods. As part of the client–server model network architecture, the volunteered machines each receive pieces of a simulation (work units), complete them, and return them to the project's database servers, where the units are compiled into an overall simulation. Volunteers can track their contributions on the Folding@home website, which makes volunteers' participation competitive and encourages long-term involvement. Originally, it was primarily aimed at protein structure prediction. Since late 2025, the development team considered it "largely solved" by techniques such as AlphaFold, and had consequently changed project focus to protein dynamics prediction. Folding@home is one of the world's fastest computing systems. With heightened interest in the project as a result of the COVID-19 pandemic, the system achieved a speed of approximately 1.22 exaflops by late March 2020 and reached 2.43 exaflops by April 12, 2020, making it the world's first exaflop computing system. This level of performance from its large-scale computing network has allowed researchers to run computationally costly atomic-level simulations of protein folding thousands of times longer than formerly achieved. Since its launch on October 1, 2000, Folding@home has been involved in the production of 226 scientific research papers. Results from the project's simulations agree well with experiments.

Tema degli esploratori: calcolo e infrastrutture (deposito sistematico; ricerca «volunteer computing»).
Fonte: Wikipedia in inglese, voce «Folding@home» — https://en.wikipedia.org/wiki/Folding@home
Wikidata Q386283: «progetto di calcolo distribuito che simula il ripiegamento delle proteine» (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q386283
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Folding@home?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 1377880489, consultata il 2026-10-05T23:08:10.470Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: 9ffdea52188e2fd81ff0e7bf2e491affcb3250b931298aa319cf34da96d4e22c