Description

State-of-the-Art Approaches to Advance the Large-Scale Green Computing Movement

Edited by one of the founders and lead investigator of the Green500 list, The Green Computing Book: Tackling Energy Efficiency at Large Scale explores seminal research in large-scale green computing. It begins with low-level, hardware-based approaches and then traverses up the software stack with increasingly higher-level, software-based approaches.

In the first chapter, the IBM Blue Gene team illustrates how to improve the energy efficiency of a supercomputer by an order of magnitude without any system performance loss in parallelizable applications. The next few chapters explain how to enhance the energy efficiency of a large-scale computing system via compiler-directed energy optimizations, an adaptive run-time system, and a general prediction performance framework. The book then explores the interactions between energy management and reliability and describes storage system organization that maximizes energy efficiency and reliability. It also addresses the need for coordinated power control across different layers and covers demand response policies in computing centers. The final chapter assesses the impact of servers on data center costs.

The Green Computing Book: Tackling Energy Efficiency at Large Scale

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Hardback by Wu-chun Feng

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State-of-the-Art Approaches to Advance the Large-Scale Green Computing MovementEdited by one of the founders and lead investigator of the Green500... Read more

    Publisher: Taylor & Francis Inc
    Publication Date: 16/06/2014
    ISBN13: 9781439819876, 978-1439819876
    ISBN10: 1439819874

    Number of Pages: 353

    Non Fiction , Computing

    Description

    State-of-the-Art Approaches to Advance the Large-Scale Green Computing Movement

    Edited by one of the founders and lead investigator of the Green500 list, The Green Computing Book: Tackling Energy Efficiency at Large Scale explores seminal research in large-scale green computing. It begins with low-level, hardware-based approaches and then traverses up the software stack with increasingly higher-level, software-based approaches.

    In the first chapter, the IBM Blue Gene team illustrates how to improve the energy efficiency of a supercomputer by an order of magnitude without any system performance loss in parallelizable applications. The next few chapters explain how to enhance the energy efficiency of a large-scale computing system via compiler-directed energy optimizations, an adaptive run-time system, and a general prediction performance framework. The book then explores the interactions between energy management and reliability and describes storage system organization that maximizes energy efficiency and reliability. It also addresses the need for coordinated power control across different layers and covers demand response policies in computing centers. The final chapter assesses the impact of servers on data center costs.

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