The Green Computing Book

The Green Computing Book: Tackling Energy Efficiency at Large Scale

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Features

  • Focuses on low-power, high-performance designs for large-scale computing systems and components, such as network on a chip
  • Offers innovative software alternatives to the internationally renowned Green Destiny supercomputer
  • Presents automated methods for power-performance adaptation in run-time systems
  • Proposes a new metric—the energy-reliability product—for simultaneously evaluating energy and reliability in storage systems
  • Discusses the critical areas of energy management and virtualization in cloud computing
  • Explains how to control power across the hardware, firmware, operating system, and application levels
  • Explores trends in server costs, energy use, and performance at high-density computing facilities

Summary

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.

Table of Contents

Low-Power, Massively Parallel, Energy-Efficient Supercomputers The Blue Gene Team

Compiler-Driven Energy Efficiency Mahmut Kandemir and Shekhar Srikantaiah

An Adaptive Run-Time System for Improving Energy Efficiency Chung-Hsing Hsu, Wu-chun Feng, and Stephen W. Poole

Energy-Efficient Multithreading through Run-Time Adaptation

Exploring Trade-Offs between Energy Savings and Reliability in Storage Systems Ali R. Butt, Puranjoy Bhattacharjee, Guanying Wang, and Chris Gniady

Cross-Layer Power Management Zhikui Wang and Parthasarathy Ranganathan

Energy-Efficient Virtualized Systems Ripal Nathuji and Karsten Schwan

Demand Response for Computing Centers Jeffery S. Chase

Implications of Recent Trends in Performance, Costs, and Energy Use for Servers Jonathan G. Koomey, Christian Belady, Michael Patterson, Anthony Santos, and Klaus-Dieter Lange

Editor Bio(s)

Wu-chun Feng is a professor in the Department of Computer Science and Department of Electrical & Computer Engineering at Virginia Polytechnic Institute and State University, where he is the director of the Synergy Laboratory. He is also the principal investigator of mpiBlast and Supercomputing in Small Spaces, founder of MyVICE, and co-founder of the Green500 list. His research interests lie broadly at the synergistic intersection of computer architecture, systems software and middleware, and applications software.

 
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