Big Data Meets Big Science.
The article focuses on the limitations of massively parallel computing in the analysis of data from next-generation scientific instruments. It states that powerful large-scale scientific instruments, such as the Large Synoptic Survey Telescope which is scheduled to go live in 2020, produce more data...
| Publicado en: | Communications of the ACM Vol. 57; no. 7; pp. 13 - 16 |
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| Formato: | Artículo |
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Association for Computing Machinery
Jul2014
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| 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=96866646&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 96866646 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Jul2014 vid: 57 iid: 7 pid: 68 pub: Association for Computing Machinery artinfo: ui: 96866646 10.1145/2617660 ppf: 13 ppct: 3 formats: tig: atl: Big Data Meets Big Science. aug: au: Wright, Alex su: Parallel computers Scientific apparatus & instruments Science databases Big data Cloud computing Quantum computing Algorithms Moore's law Aaronson, Scott sug: subj: Parallel computers Scientific apparatus & instruments Science databases Big data Cloud computing Quantum computing Algorithms Moore's law Aaronson, Scott ab: The article focuses on the limitations of massively parallel computing in the analysis of data from next-generation scientific instruments. It states that powerful large-scale scientific instruments, such as the Large Synoptic Survey Telescope which is scheduled to go live in 2020, produce more data than the most powerful massively parallel supercomputers can handle. It mentions that scientists are examining new methods of reducing datasets to a manageable size, including cloud-based computing and emerging frameworks like quantum computing, and talks about algorithmic and economic constraints facing large scale data analysis. Massachusetts Institute of Technology professor Scott Aaronson suggests that Moore's Law has effectively broken down. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
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