Distributed Strategies for Computational Sprints.
Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe a sprinting architecture in which many, independent chip multiprocessors share a power supply and sprints are constrained by the chips’ thermal limits and the rack’s power limits. More...
| Publicado en: | Communications of the ACM Vol. 62; no. 2; pp. 98 - 107 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Association for Computing Machinery
Feb2019
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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=134383090&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 134383090 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Feb2019 vid: 62 iid: 2 pid: 68 pub: Association for Computing Machinery artinfo: ui: 134383090 10.1145/3299885 ppf: 98 ppct: 9 formats: tig: atl: Distributed Strategies for Computational Sprints. aug: au: Songchun Fan Zahedi, Seyed Majid Lee, Benjamin C. affil: Duke University, California, USA. Duke University, Durham, NC, USA. su: Computational mathematics Computer programming Systems design Technical specifications Systems development sug: subj: Computational mathematics Computer programming Systems design Technical specifications Systems development ab: Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe a sprinting architecture in which many, independent chip multiprocessors share a power supply and sprints are constrained by the chips’ thermal limits and the rack’s power limits. Moreover, we present the computational sprinting game, a multi-agent perspective on managing sprints. Strategic agents decide whether to sprint based on application phases and system conditions. The game produces an equilibrium that improves task throughput for data analytics workloads by 4–6× over prior greedy heuristics and performs within 90% of an upper bound on throughput from a globally optimized policy. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2019 holdings: @attributes: islocal: N |
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