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Digital transformation of droplet/aerosol infection risk assessment realized on "Fugaku" for the fight against COVID-19.
Ando, Kazuto; Bale, Rahul; Li, ChungGang; Matsuoka, Satoshi; Onishi, Keiji; Tsubokura, Makoto.
Afiliación
  • Ando K; RIKEN Center for Computational Science, Kobe, Japan.
  • Bale R; RIKEN Center for Computational Science, Kobe, Japan.
  • Li C; Kobe University, Kobe, Japan.
  • Matsuoka S; RIKEN Center for Computational Science, Kobe, Japan.
  • Onishi K; Kobe University, Kobe, Japan.
  • Tsubokura M; RIKEN Center for Computational Science, Kobe, Japan.
Int J High Perform Comput Appl ; 36(5-6): 568-586, 2022 Nov.
Article en En | MEDLINE | ID: mdl-38603243
ABSTRACT
The fastest supercomputer in 2020, Fugaku, has not only achieved digital transformation of epidemiology in allowing end-to-end, detailed quantitative modeling of COVID-19 transmissions for the first time but also transformed the behavior of the entire Japanese public through its detailed analysis of transmission risks in multitudes of societal situations entailing heavy risks. A novel aerosol simulation methodology was synthesized out of a combination of a new CFD methods meeting industrial demands in the solver, CUBE (Jansson et al., 2019), which not only allowed the simulations to scale massively with high resolution required for micrometer virus-containing aerosol particles but also enabled extremely rapid time-to-solution due to its ability to generate the digital twins representing multitudes of societal situations in a matter of minutes, attaining true overall application high performance; such simulations have been running for the past 1.5°years on Fugaku, cumulatively consuming top supercomputer-class resources and the communicated by the media as well as becoming the basis for official public policies.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Int J High Perform Comput Appl Año: 2022 Tipo del documento: Article País de afiliación: Japón

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Int J High Perform Comput Appl Año: 2022 Tipo del documento: Article País de afiliación: Japón