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Energy Efficient Distributed Computing Systems [electronic resource].

By: Contributor(s): Material type: Computer fileComputer filePublisher number: 9780470908754Series: Wiley Series on Parallel and Distributed ComputingPublication details: Hoboken : John Wiley & Sons, 2012.ISBN:
  • 9781118341988
Subject(s): Genre/Form: Additional physical formats: Print version:: Energy Efficient Distributed Computing SystemsDDC classification:
  • 004.36
LOC classification:
  • TK5105.5 .Z66 2012
Online resources:
Contents:
ENERGY-EFFICIENT DISTRIBUTED COMPUTING SYSTEMS; CONTENTS; PREFACE; ACKNOWLEDGMENTS; CONTRIBUTORS; 1 POWER ALLOCATION AND TASK SCHEDULING ON MULTIPROCESSOR COMPUTERS WITH ENERGY AND TIME CONSTRAINTS; 1.1 Introduction; 1.1.1 Energy Consumption; 1.1.2 Power Reduction; 1.1.3 Dynamic Power Management; 1.1.4 Task Scheduling with Energy and Time Constraints; 1.1.5 Chapter Outline; 1.2 Preliminaries; 1.2.1 Power Consumption Model; 1.2.2 Problem Definitions; 1.2.3 Task Models; 1.2.4 Processor Models; 1.2.5 Scheduling Models; 1.2.6 Problem Decomposition; 1.2.7 Types of Algorithms; 1.3 Problem Analysis
1.3.1 Schedule Length Minimization1.3.1.1 Uniprocessor computers; 1.3.1.2 Multiprocessor computers; 1.3.2 Energy Consumption Minimization; 1.3.2.1 Uniprocessor computers; 1.3.2.2 Multiprocessor computers; 1.3.3 Strong NP-Hardness; 1.3.4 Lower Bounds; 1.3.5 Energy-Delay Trade-off; 1.4 Pre-Power-Determination Algorithms; 1.4.1 Overview; 1.4.2 Performance Measures; 1.4.3 Equal-Time Algorithms and Analysis; 1.4.3.1 Schedule length minimization; 1.4.3.2 Energy consumption minimization; 1.4.4 Equal-Energy Algorithms and Analysis; 1.4.4.1 Schedule length minimization
1.4.4.2 Energy consumption minimization1.4.5 Equal-Speed Algorithms and Analysis; 1.4.5.1 Schedule length minimization; 1.4.5.2 Energy consumption minimization; 1.4.6 Numerical Data; 1.4.7 Simulation Results; 1.5 Post-Power-Determination Algorithms; 1.5.1 Overview; 1.5.2 Analysis of List Scheduling Algorithms; 1.5.2.1 Analysis of algorithm LS; 1.5.2.2 Analysis of algorithm LRF; 1.5.3 Application to Schedule Length Minimization; 1.5.4 Application to Energy Consumption Minimization; 1.5.5 Numerical Data; 1.5.6 Simulation Results; 1.6 Summary and Further Research; References
2 POWER-AWARE HIGH PERFORMANCE COMPUTING2.1 Introduction; 2.2 Background; 2.2.1 Current Hardware Technology and Power Consumption; 2.2.1.1 Processor power; 2.2.1.2 Memory subsystem power; 2.2.2 Performance; 2.2.3 Energy Efficiency; 2.3 Related Work; 2.3.1 Power Profiling; 2.3.1.1 Simulator-based power estimation; 2.3.1.2 Direct measurements; 2.3.1.3 Event-based estimation; 2.3.2 Performance Scalability on Power-Aware Systems; 2.3.3 Adaptive Power Allocation for Energy-Efficient Computing; 2.4 PowerPack: Fine-Grain Energy Profiling of HPC Applications
2.4.1 Design and Implementation of PowerPack2.4.1.1 Overview; 2.4.1.2 Fine-grain systematic power measurement; 2.4.1.3 Automatic power profiling and code synchronization; 2.4.2 Power Profiles of HPC Applications and Systems; 2.4.2.1 Power distribution over components; 2.4.2.2 Power dynamics of applications; 2.4.2.3 Power bounds on HPC systems; 2.4.2.4 Power versus dynamic voltage and frequency scaling; 2.5 Power-Aware Speedup Model; 2.5.1 Power-Aware Speedup; 2.5.1.1 Sequential execution time for a single workload T1(w, f ); 2.5.1.2 Sequential execution time for an ON-chip/OFF-chip workload
2.5.1.3 Parallel execution time on N processors for an ON-/OFF-chip workload with DOP = i
Summary: The energy consumption issue in distributed computing systems raises various monetary, environmental and system performance concerns. Electricity consumption in the US doubled from 2000 to 2005. From a financial and environmental standpoint, reducing the consumption of electricity is important, yet these reforms must not lead to performance degradation of the computing systems. These contradicting constraints create a suite of complex problems that need to be resolved in order to lead to 'greener' distributed computing systems. This book brings together a group of outstanding researchers th
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Enhanced descriptions from Syndetics:

Description based upon print version of record.

ENERGY-EFFICIENT DISTRIBUTED COMPUTING SYSTEMS; CONTENTS; PREFACE; ACKNOWLEDGMENTS; CONTRIBUTORS; 1 POWER ALLOCATION AND TASK SCHEDULING ON MULTIPROCESSOR COMPUTERS WITH ENERGY AND TIME CONSTRAINTS; 1.1 Introduction; 1.1.1 Energy Consumption; 1.1.2 Power Reduction; 1.1.3 Dynamic Power Management; 1.1.4 Task Scheduling with Energy and Time Constraints; 1.1.5 Chapter Outline; 1.2 Preliminaries; 1.2.1 Power Consumption Model; 1.2.2 Problem Definitions; 1.2.3 Task Models; 1.2.4 Processor Models; 1.2.5 Scheduling Models; 1.2.6 Problem Decomposition; 1.2.7 Types of Algorithms; 1.3 Problem Analysis

1.3.1 Schedule Length Minimization1.3.1.1 Uniprocessor computers; 1.3.1.2 Multiprocessor computers; 1.3.2 Energy Consumption Minimization; 1.3.2.1 Uniprocessor computers; 1.3.2.2 Multiprocessor computers; 1.3.3 Strong NP-Hardness; 1.3.4 Lower Bounds; 1.3.5 Energy-Delay Trade-off; 1.4 Pre-Power-Determination Algorithms; 1.4.1 Overview; 1.4.2 Performance Measures; 1.4.3 Equal-Time Algorithms and Analysis; 1.4.3.1 Schedule length minimization; 1.4.3.2 Energy consumption minimization; 1.4.4 Equal-Energy Algorithms and Analysis; 1.4.4.1 Schedule length minimization

1.4.4.2 Energy consumption minimization1.4.5 Equal-Speed Algorithms and Analysis; 1.4.5.1 Schedule length minimization; 1.4.5.2 Energy consumption minimization; 1.4.6 Numerical Data; 1.4.7 Simulation Results; 1.5 Post-Power-Determination Algorithms; 1.5.1 Overview; 1.5.2 Analysis of List Scheduling Algorithms; 1.5.2.1 Analysis of algorithm LS; 1.5.2.2 Analysis of algorithm LRF; 1.5.3 Application to Schedule Length Minimization; 1.5.4 Application to Energy Consumption Minimization; 1.5.5 Numerical Data; 1.5.6 Simulation Results; 1.6 Summary and Further Research; References

2 POWER-AWARE HIGH PERFORMANCE COMPUTING2.1 Introduction; 2.2 Background; 2.2.1 Current Hardware Technology and Power Consumption; 2.2.1.1 Processor power; 2.2.1.2 Memory subsystem power; 2.2.2 Performance; 2.2.3 Energy Efficiency; 2.3 Related Work; 2.3.1 Power Profiling; 2.3.1.1 Simulator-based power estimation; 2.3.1.2 Direct measurements; 2.3.1.3 Event-based estimation; 2.3.2 Performance Scalability on Power-Aware Systems; 2.3.3 Adaptive Power Allocation for Energy-Efficient Computing; 2.4 PowerPack: Fine-Grain Energy Profiling of HPC Applications

2.4.1 Design and Implementation of PowerPack2.4.1.1 Overview; 2.4.1.2 Fine-grain systematic power measurement; 2.4.1.3 Automatic power profiling and code synchronization; 2.4.2 Power Profiles of HPC Applications and Systems; 2.4.2.1 Power distribution over components; 2.4.2.2 Power dynamics of applications; 2.4.2.3 Power bounds on HPC systems; 2.4.2.4 Power versus dynamic voltage and frequency scaling; 2.5 Power-Aware Speedup Model; 2.5.1 Power-Aware Speedup; 2.5.1.1 Sequential execution time for a single workload T1(w, f ); 2.5.1.2 Sequential execution time for an ON-chip/OFF-chip workload

2.5.1.3 Parallel execution time on N processors for an ON-/OFF-chip workload with DOP = i

The energy consumption issue in distributed computing systems raises various monetary, environmental and system performance concerns. Electricity consumption in the US doubled from 2000 to 2005. From a financial and environmental standpoint, reducing the consumption of electricity is important, yet these reforms must not lead to performance degradation of the computing systems. These contradicting constraints create a suite of complex problems that need to be resolved in order to lead to 'greener' distributed computing systems. This book brings together a group of outstanding researchers th