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 that investigate the
different facets of green and energy efficient distributed
computing.



Key features:

* One of the first books of its kind

* Features latest research findings on emerging topics by
well-known scientists

* Valuable research for grad students, postdocs, and
researchers

* Research will greatly feed into other technologies and
application domains

ALBERT Y. ZOMAYA is the Chair Professor of High
Performance Computing & Networking in the School of Information
Technologies, The University of Sydney. He is a Fellow of the IEEE,
the American Association for the Advancement of Science, and the
Institution of Engineering and Technology, and a Distinguished
Engineer of the ACM. He has authored seven books and some 400
articles in technical journals.

YOUNG CHOON LEE, PhD, is with the Centre for Distributed
and High Performance Computing, School of Information Technologies,
The University of Sydney.

Key features:

• One of the first books of its kind
• Features latest research findings on emerging topics by well-known scientists
• Valuable research for grad students, postdocs, and researchers
• Research will greatly feed into other technologies and application domains

ACKNOWLEDGMENTS xxxi

CONTRIBUTORS xxxiii

1 POWER ALLOCATION AND TASK SCHEDULING ON MULTIPROCESSOR COMPUTERS WITH ENERGY AND TIME CONSTRAINTS 1
Keqin Li

1.1 Introduction 1

1.1.1 Energy Consumption 1

1.1.2 Power Reduction 2

1.1.3 Dynamic Power Management 3

1.1.4 Task Scheduling with Energy and Time Constraints 4

1.1.5 Chapter Outline 5

1.2 Preliminaries 5

1.2.1 Power Consumption Model 5

1.2.2 Problem Definitions 6

1.2.3 Task Models 7

1.2.4 Processor Models 8

1.2.5 Scheduling Models 9

1.2.6 Problem Decomposition 9

1.2.7 Types of Algorithms 10

1.3 Problem Analysis 10

1.3.1 Schedule Length Minimization 10

1.3.1.1 Uniprocessor computers 10

1.3.1.2 Multiprocessor computers 11

1.3.2 Energy Consumption Minimization 12

1.3.2.1 Uniprocessor computers 12

1.3.2.2 Multiprocessor computers 13

1.3.3 Strong NP-Hardness 14

1.3.4 Lower Bounds 14

1.3.5 Energy-Delay Trade-off 15

1.4 Pre-Power-Determination Algorithms 16

1.4.1 Overview 16

1.4.2 Performance Measures 17

1.4.3 Equal-Time Algorithms and Analysis 18

1.4.3.1 Schedule length minimization 18

1.4.3.2 Energy consumption minimization 19

1.4.4 Equal-Energy Algorithms and Analysis 19

1.4.4.1 Schedule length minimization 19

1.4.4.2 Energy consumption minimization 21

1.4.5 Equal-Speed Algorithms and Analysis 22

1.4.5.1 Schedule length minimization 22

1.4.5.2 Energy consumption minimization 23

1.4.6 Numerical Data 24

1.4.7 Simulation Results 25

1.5 Post-Power-Determination Algorithms 28

1.5.1 Overview 28

1.5.2 Analysis of List Scheduling Algorithms 29

1.5.2.1 Analysis of algorithm LS 29

1.5.2.2 Analysis of algorithm LRF 30

1.5.3 Application to Schedule Length Minimization 30

1.5.4 Application to Energy Consumption Minimization 31

1.5.5 Numerical Data 32

1.5.6 Simulation Results 32

1.6 Summary and Further Research 33

References 34

2 POWER-AWARE HIGH PERFORMANCE COMPUTING 39
Rong Ge and Kirk W. Cameron

2.1 Introduction 39

2.2 Background 41

2.2.1 Current Hardware Technology and Power Consumption 41

2.2.1.1 Processor power 41

2.2.1.2 Memory subsystem power 42

2.2.2 Performance 43

2.2.3 Energy Efficiency 44

2.3 Related Work 45

2.3.1 Power Profiling 45

2.3.1.1 Simulator-based power estimation 45

2.3.1.2 Direct measurements 46

2.3.1.3 Event-based estimation 46

2.3.2 Performance Scalability on Power-Aware Systems 46

2.3.3 Adaptive Power Allocation for Energy-Efficient Computing 47

2.4 PowerPack: Fine-Grain Energy Profiling of HPC Applications 48

2.4.1 Design and Implementation of PowerPack 48

2.4.1.1 Overview 48

2.4.1.2 Fine-grain systematic power measurement 50

2.4.1.3 Automatic power profiling and code synchronization 51

2.4.2 Power Profiles of HPC Applications and Systems 53

2.4.2.1 Power distribution over components 53

2.4.2.2 Power dynamics of applications 54

2.4.2.3 Power bounds on HPC systems 55

2.4.2.4 Power versus dynamic voltage and frequency scaling 57

2.5 Power-Aware Speedup Model 59

2.5.1 Power-Aware Speedup 59

2.5.1.1 Sequential execution time for a single workload T1(w, f ) 60

2.5.1.2 Sequential execution time for an ON-chip/OFF-chip workload 60

2.5.1.3 Parallel execution time on N processors for an ON-/OFF-chip workload with DOP = i 61

2.5.1.4 Power-aware speedup for DOP and ON-/OFF-chip workloads 62

2.5.2 Model Parametrization and Validation 63

2.5.2.1 Coarse-grain parametrization...