High-Performance Computing of Self-Gravity for Small Solar System Bodies.
To study the evolution of small solar system bodies like asteroids and comets, a fast method is needed to compute the self-gravity of systems composed of millions of particles. A proposed fully parallel shared-memory algorithm for dense, self-gravitating agglomerates scales efficiently with the numb...
| Publicado en: | Computer (00189162) Vol. 47; no. 9; pp. 34 - 40 |
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| Autores principales: | , , |
| Formato: | Artículo |
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IEEE
Sep2014
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=98483219&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 98483219 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: Sep2014 vid: 47 iid: 9 pid: 13605 pub: IEEE artinfo: ui: 98483219 10.1109/MC.2014.249 ppf: 34 ppct: 6 formats: tig: atl: High-Performance Computing of Self-Gravity for Small Solar System Bodies. aug: au: Frascarelli, Daniel Nesmachnow, Sergio Tancredi, Gonzalo affil: Universidad de la República su: Small solar system bodies Algorithm research Scientific computing Gravity Nuclear particle research sug: subj: Small solar system bodies Algorithm research Scientific computing Gravity Nuclear particle research keyword: Algorithm design and analysis Astronomy Computational efficiency Computational modeling high-performance computing Informatics Instruction sets Message systems numerical simulation Parallel algorithms Planets scientific computing self-gravity small solar system bodies Synchronization ab: To study the evolution of small solar system bodies like asteroids and comets, a fast method is needed to compute the self-gravity of systems composed of millions of particles. A proposed fully parallel shared-memory algorithm for dense, self-gravitating agglomerates scales efficiently with the number of particles as well as the number of computational resources. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
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