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HPC@Green IT [electronic resource] : Green High Performance Computing Methods / by Ralf Gruber, Vincent Keller.

By: Gruber, Ralf [author.].
Contributor(s): Keller, Vincent [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Description: XIV, 215p. 170 illus., 85 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642017896.Subject(s): Computer science | Computer network architectures | Computer system performance | Software engineering | Operating systems (Computers) | Computer Science | Software Engineering/Programming and Operating Systems | Programming Techniques | System Performance and Evaluation | Computer Systems Organization and Communication Networks | Operating SystemsDDC classification: 005.1 Online resources: Click here to access online
Contents:
Historical highlights -- Parameterization -- Models -- Core optimization -- Node optimization -- Cluster optimization -- Grid-level Brokering to save energy -- Recommendations.
In: Springer eBooksSummary: The authors present methods to reduce computer energy consumption by a better use of resources and by maximizing the efficiencies of applications. The processor frequency is adjusted to the needs of the running job, leading to a power drop in servers and PCs, and increasing battery life time of laptops. It is shown how computer resources can be optimally adapted to application needs, reducing job run time. The job-related data is stored and reused to help computer managers to stop old machines and to choose new ones better adapted to the application community.
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Historical highlights -- Parameterization -- Models -- Core optimization -- Node optimization -- Cluster optimization -- Grid-level Brokering to save energy -- Recommendations.

The authors present methods to reduce computer energy consumption by a better use of resources and by maximizing the efficiencies of applications. The processor frequency is adjusted to the needs of the running job, leading to a power drop in servers and PCs, and increasing battery life time of laptops. It is shown how computer resources can be optimally adapted to application needs, reducing job run time. The job-related data is stored and reused to help computer managers to stop old machines and to choose new ones better adapted to the application community.

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