Hardware Technologies for High-Performance Data-Intensive Computing.
The article offers an investigation into hardware platforms suitable for data-intensive systems. It evaluates the benefits of two coprocessor architectures: graphics processors and reconfigurable hardware. It describes the Large Synoptic Survey Telescope (LSST) and the image-processing benchmark, th...
| Publicado en: | Computer (00189162) Vol. 41; no. 4; pp. 60 - 69 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
| Publicado: |
IEEE
Apr2008
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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=31912724&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 31912724 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: Apr2008 vid: 41 iid: 4 pid: 13605 pub: IEEE artinfo: ui: 31912724 10.1109/MC.2008.125 ppf: 60 ppct: 9 formats: tig: atl: Hardware Technologies for High-Performance Data-Intensive Computing. aug: au: Gokhale, Maya Cohen, Jonathan Yoo, Andy Miller, W. Marcus Jacob, Arpith Ulmer, Craig Pearce, Roger affil: Lawrence Livermore National Laboratory Washington University in St. Louis Sandia National Laboratories Texas A&M University su: Computer input-output equipment Coprocessors Microprocessors Computer architecture Image processing Lanczos method Computer hardware description languages sug: subj: Computer input-output equipment Coprocessors Microprocessors Computer architecture Image processing Lanczos method Computer hardware description languages ab: The article offers an investigation into hardware platforms suitable for data-intensive systems. It evaluates the benefits of two coprocessor architectures: graphics processors and reconfigurable hardware. It describes the Large Synoptic Survey Telescope (LSST) and the image-processing benchmark, the Lanczos resampling filter. To assess the potential for accelerating LSST image-processing tasks, the authors' used the Lanczos filter. A key step in processing large document streams is language classification. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2008 holdings: @attributes: islocal: N |
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