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E-raamat: Modeling and Optimization of Parallel and Distributed Embedded Systems [Wiley Online]

(University of Florida, USA), (University of Florida, USA), (University of Nevada, Reno (UNR), USA)
  • Formaat: 400 pages
  • Sari: IEEE Press
  • Ilmumisaeg: 05-Feb-2016
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1119086388
  • ISBN-13: 9781119086383
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  • Wiley Online
  • Hind: 148,02 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Modeling and Optimization of Parallel and Distributed Embedded SystemsSuurem pilt
  • Formaat: 400 pages
  • Sari: IEEE Press
  • Ilmumisaeg: 05-Feb-2016
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1119086388
  • ISBN-13: 9781119086383
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119086383

This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles.

The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability.

Key features:

  • Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems.  
  • Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems.
  • Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation.
  • Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies.
  • Highlights research challenges and future research directions.

The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research.

Preface xv
Acknowledgment xxi
Part I Overview
1 Introduction
3(26)
1.1 Embedded Systems Applications
6(3)
1.1.1 Cyber-Physical Systems
6(1)
1.1.2 Space
6(1)
1.1.3 Medical
7(1)
1.1.4 Automotive
8(1)
1.2 Characteristics of Embedded Systems Applications
9(2)
1.2.1 Throughput-Intensive
9(1)
1.2.2 Thermal-Constrained
9(1)
1.2.3 Reliability-Constrained
10(1)
1.2.4 Real-Time
10(1)
1.2.5 Parallel and Distributed
10(1)
1.3 Embedded Systems—Hardware and Software
11(4)
1.3.1 Embedded Systems Hardware
11(3)
1.3.2 Embedded Systems Software
14(1)
1.4 Modeling—An Integral Part of the Embedded Systems Design Flow
15(6)
1.4.1 Modeling Objectives
16(2)
1.4.2 Modeling Paradigms
18(2)
1.4.3 Strategies for Integration of Modeling Paradigms
20(1)
1.5 Optimization in Embedded Systems
21(6)
1.5.1 Optimization of Embedded Systems Design Metrics
23(3)
1.5.2 Multiobjective Optimization
26(1)
1.6
Chapter Summary
27(2)
2 Multicore-Based EWSNs—An Example of Parallel and Distributed Embedded Systems
29(22)
2.1 Multicore Embedded Wireless Sensor Network Architecture
31(2)
2.2 Multicore Embedded Sensor Node Architecture
33(3)
2.2.1 Sensing Unit
34(1)
2.2.2 Processing Unit
34(1)
2.2.3 Storage Unit
34(1)
2.2.4 Communication Unit
35(1)
2.2.5 Power Unit
35(1)
2.2.6 Actuator Unit
35(1)
2.2.7 Location Finding Unit
36(1)
2.3 Compute-Intensive Tasks Motivating the Emergence of MCEWSNs
36(2)
2.3.1 Information Fusion
36(2)
2.3.2 Encryption
38(1)
2.3.3 Network Coding
38(1)
2.3.4 Software-Defined Radio (SDR)
38(1)
2.4 MCEWSN Application Domains
38(5)
2.4.1 Wireless Video Sensor Networks (WVSNs)
39(1)
2.4.2 Wireless Multimedia Sensor Networks (WMSNs)
39(1)
2.4.3 Satellite-Based Wireless Sensor Networks (SBWSN)
40(1)
2.4.4 Space Shuttle Sensor Networks (3SN)
41(1)
2.4.5 Aerial—Terrestrial Hybrid Sensor Networks (ATHSNs)
42(1)
2.4.6 Fault-Tolerant (FT) Sensor Networks
43(1)
2.5 Multicore Embedded Sensor Nodes
43(2)
2.5.1 Instrallode
43(1)
2.5.2 Mars Rover Prototype Mote
43(1)
2.5.3 Satellite-Based Sensor Node (SBSN)
44(1)
2.5.4 Multi-CPU-Based Sensor Node Prototype
44(1)
2.5.5 Smart Camera Mote
44(1)
2.6 Research Challenges and Future Research Directions
45(2)
2.7
Chapter Summary
47(4)
Part II Modeling
3 An Application Metrics Estimation Model for Embedded Wireless Sensor Networks
51(12)
3.1 Application Metrics Estimation Model
52(6)
3.1.1 Lifetime Estimation
53(3)
3.1.2 Throughput Estimation
56(1)
3.1.3 Reliability Estimation
57(1)
3.1.4 Models Validation
57(1)
3.2 Experimental Results
58(3)
3.2.1 Experimental Setup
58(1)
3.2.2 Results
59(2)
3.3
Chapter Summary
61(2)
4 Modeling and Analysis of Fault Detection and Fault Tolerance in Embedded Wireless Sensor Networks
63(44)
4.1 Related Work
67(3)
4.1.1 Fault Detection
67(1)
4.1.2 Fault Tolerance
68(1)
4.1.3 WSN Reliability Modeling
69(1)
4.2 Fault Diagnosis in WSNs
70(4)
4.2.1 Sensor Faults
70(2)
4.2.2 Taxonomy for Fault Diagnosis Techniques
72(2)
4.3 Distributed Fault Detection Algorithms
74(3)
4.3.1 Fault Detection Algorithm 1: The Chen Algorithm
74(2)
4.3.2 Fault Detection Algorithm 2: The Ding Algorithm
76(1)
4.4 Fault-Tolerant Markov Models
77(8)
4.4.1 Fault-Tolerance Parameters
77(2)
4.4.2 Fault-Tolerant Sensor Node Model
79(2)
4.4.3 Fault-Tolerant WSN Cluster Model
81(2)
4.4.4 Fault-Tolerant WSN Model
83(2)
4.5 Simulation of Distributed Fault Detection Algorithms
85(6)
4.5.1 Using ns-2 to Simulate Faulty Sensors
85(1)
4.5.2 Experimental Setup for Simulated Data
86(1)
4.5.3 Experiments Using Real-World Data
86(5)
4.6 Numerical Results
91(10)
4.6.1 Experimental Setup
91(1)
4.6.2 Reliability and MTTF for an NFT and an FT Sensor Node
91(4)
4.6.3 Reliability and MTTF for an NFT and an FT WSN Cluster
95(3)
4.6.4 Reliability and MTTF for an NFT and an FT WSN
98(3)
4.7 Research Challenges and Future Research Directions
101(4)
4.7.1 Accurate Fault Detection
101(1)
4.7.2 Benchmarks for Comparing Fault Detection Algorithms
101(1)
4.7.3 Energy-Efficient Fault Detection and Tolerance
101(1)
4.7.4 Machine-Learning-Inspired Fault Detection
102(1)
4.7.5 FT in Multimedia Sensor Networks
102(1)
4.7.6 Security
102(2)
4.7.7 WSN Design and Tuning for Reliability
104(1)
4.7.8 Novel WSN Architectures
104(1)
4.8
Chapter Summary
105(2)
5 A Queueing Theoretic Approach for Performance Evaluation of Low-Power Multicore-Based Parallel Embedded Systems
107(36)
5.1 Related Work
110(2)
5.2 Queueing Network Modeling of Multicore Embedded Architectures
112(8)
5.2.1 Queueing Network Terminology
112(1)
5.2.2 Modeling Approach
113(6)
5.2.3 Assumptions
119(1)
5.3 Queueing Network Model Validation
120(5)
5.3.1 Theoretical Validation
120(1)
5.3.2 Validation with a Multicore Simulator
120(4)
5.3.3 Speedup
124(1)
5.4 Queueing Theoretic Model Insights
125(14)
5.4.1 Model Setup
125(4)
5.4.2 The Effects of Cache Miss Rates on Performance
129(3)
5.4.3 The Effects of Workloads on Performance
132(3)
5.4.4 Performance per Watt and Performance per Unit Area Computations
135(4)
5.5
Chapter Summary
139(4)
Part III Optimization
6 Optimization Approaches in Distributed Embedded Wireless Sensor Networks
143(16)
6.1 Architecture-Level Optimizations
144(2)
6.2 Sensor Node Component-Level Optimizations
146(3)
6.2.1 Sensing Unit
146(2)
6.2.2 Processing Unit
148(1)
6.2.3 Transceiver Unit
148(1)
6.2.4 Storage Unit
148(1)
6.2.5 Actuator Unit
148(1)
6.2.6 Location Finding Unit
149(1)
6.2.7 Power Unit
149(1)
6.3 Data Link-Level Medium Access Control Optimizations
149(3)
6.3.1 Load Balancing and Throughput Optimizations
149(1)
6.3.2 Power/Energy Optimizations
150(2)
6.4 Network-Level Data Dissemination and Routing Protocol Optimizations
152(3)
6.4.1 Query Dissemination Optimizations
152(2)
6.4.2 Real-Time Constrained Optimizations
154(1)
6.4.3 Network Topology Optimizations
154(1)
6.4.4 Resource-Adaptive Optimizations
154(1)
6.5 Operating System-Level Optimizations
155(1)
6.5.1 Event-Driven Optimizations
155(1)
6.5.2 Dynamic Power Management
155(1)
6.5.3 Fault Tolerance
155(1)
6.6 Dynamic Optimizations
156(1)
6.6.1 Dynamic Voltage and Frequency Scaling
156(1)
6.6.2 Software-Based Dynamic Optimizations
156(1)
6.6.3 Dynamic Network Reprogramming
157(1)
6.7
Chapter Summary
157(2)
7 High-Performance Energy-Efficient Multicore-Based Parallel Embedded Computing
159(32)
7.1 Characteristics of Embedded Systems Applications
163(3)
7.1.1 Throughput-Intensive
163(2)
7.1.2 Thermal-Constrained
165(1)
7.1.3 Reliability-Constrained
165(1)
7.1.4 Real-Time
165(1)
7.1.5 Parallel and Distributed
165(1)
7.2 Architectural Approaches
166(7)
7.2.1 Core Layout
166(2)
7.2.2 Memory Design
168(2)
7.2.3 Interconnection Network
170(2)
7.2.4 Reduction Techniques
172(1)
7.3 Hardware-Assisted Middleware Approaches
173(7)
7.3.1 Dynamic Voltage and Frequency Scaling
174(1)
7.3.2 Advanced Configuration and Power Interface
174(1)
7.3.3 Gating Techniques
175(1)
7.3.4 Threading Techniques
176(1)
7.3.5 Energy Monitoring and Management
177(1)
7.3.6 Dynamic Thermal Management
178(1)
7.3.7 Dependable Techniques
179(1)
7.4 Software Approaches
180(2)
7.4.1 Data Forwarding
180(1)
7.4.2 Load Distribution
180(2)
7.5 High-Performance Energy-Efficient Multicore Processors
182(4)
7.5.1 ARM11 MPCore
183(1)
7.5.2 ARM Cortex A-9 MPCore
184(1)
7.5.3 MPC8572E PowerQUICC III
184(1)
7.5.4 Tilera TILEPro64 and TILE-Gx
184(1)
7.5.5 AMD Opteron Processor
185(1)
7.5.6 Intel Xeon Processor
185(1)
7.5.7 Intel Sandy Bridge Processor
185(1)
7.5.8 Graphics Processing Units
186(1)
7.6 Challenges and Future Research Directions
186(3)
7.7
Chapter Summary
189(2)
8 An MDP-Based Dynamic Optimization Methodology for Embedded Wireless Sensor Networks
191(34)
8.1 Related Work
193(2)
8.2 MDP-Based Tuning Overview
195(5)
8.2.1 MDP-Based Tuning Methodology for Embedded Wireless Sensor Networks
195(2)
8.2.2 MDP Overview with Respect to Embedded Wireless Sensor Networks
197(3)
8.3 Application-Specific Embedded Sensor Node Tuning Formulation as an MDP
200(5)
8.3.1 State Space
200(1)
8.3.2 Decision Epochs and Actions
200(1)
8.3.3 State Dynamics
201(1)
8.3.4 Policy and Performance Criterion
201(1)
8.3.5 Reward Function
202(2)
8.3.6 Optimality Equation
204(1)
8.3.7 Policy Iteration Algorithm
205(1)
8.4 Implementation Guidelines and Complexity
205(2)
8.4.1 Implementation Guidelines
205(1)
8.4.2 Computational Complexity
206(1)
8.4.3 Data Memory Analysis
207(1)
8.5 Model Extensions
207(3)
8.6 Numerical Results
210(13)
8.6.1 Fixed Heuristic Policies for Performance Comparisons
210(1)
8.6.2 MDP Specifications
210(3)
8.6.3 Results for a Security/Defense System Application
213(3)
8.6.4 Results for a Healthcare Application
216(4)
8.6.5 Results for an Ambient Conditions Monitoring Application
220(2)
8.6.6 Sensitivity Analysis
222(1)
8.6.7 Number of Iterations for Convergence
223(1)
8.7
Chapter Summary
223(2)
9 Online Algorithms for Dynamic Optimization of Embedded Wireless Sensor Networks
225(16)
9.1 Related Work
227(1)
9.2 Dynamic Optimization Methodology
228(5)
9.2.1 Methodology Overview
228(1)
9.2.2 State Space
229(1)
9.2.3 Objective Function
229(1)
9.2.4 Online Optimization Algorithms
230(3)
9.3 Experimental Results
233(6)
9.3.1 Experimental Setup
233(2)
9.3.2 Results
235(4)
9.4
Chapter Summary
239(2)
10 A Lightweight Dynamic Optimization Methodology for Embedded Wireless Sensor Networks
241(28)
10.1 Related Work
243(1)
10.2 Dynamic Optimization Methodology
244(4)
10.2.1 Overview
244(2)
10.2.2 State Space
246(1)
10.2.3 Optimization Objection Function
246(2)
10.3 Algorithms for Dynamic Optimization Methodology
248(4)
10.3.1 Initial Tunable Parameter Value Settings and Exploration Order
248(1)
10.3.2 Parameter Arrangement
249(2)
10.3.3 Online Optimization Algorithm
251(1)
10.3.4 Computational Complexity
252(1)
10.4 Experimental Results
252(14)
10.4.1 Experimental Setup
253(2)
10.4.2 Results
255(11)
10.5
Chapter Summary
266(3)
11 Parallelized Benchmark-Driven Performance Evaluation of Symmetric Multiprocessors and Tiled Multicore Architectures for Parallel Embedded Systems
269(18)
11.1 Related Work
271(1)
11.2 Multicore Architectures and Benchmarks
272(3)
11.2.1 Multicore Architectures
272(1)
11.2.2 Benchmark Applications and Kernels
273(2)
11.3 Parallel Computing Device Metrics
275(2)
11.4 Results
277(8)
11.4.1 Quantitative Comparison of SMPs and TMAs
277(1)
11.4.2 Benchmark-Driven Results for SMPs
278(2)
11.4.3 Benchmark-Driven Results for TMAs
280(2)
11.4.4 Comparison of SMPs and TMAs
282(3)
11.5
Chapter Summary
285(2)
12 High-Performance Optimizations on Tiled Manycore Embedded Systems: A Matrix Multiplication Case Study
287(56)
12.1 Related Work
290(3)
12.1.1 Performance Analysis and Optimization
290(1)
12.1.2 Parallelized MM Algorithms
290(1)
12.1.3 Cache Blocking
291(1)
12.1.4 Tiled Manycore Architectures
292(1)
12.2 Tiled Manycore Architecture (TMA) Overview
293(8)
12.2.1 Intel's TeraFLOPS Research Chip
294(2)
12.2.2 IBM's Cyclops-64 (C64)
296(1)
12.2.3 Tilera's TILEPro64
297(3)
12.2.4 Tilera's TILE64
300(1)
12.3 Parallel Computing Metrics and Matrix Multiplication (MM) Case Study
301(2)
12.3.1 Parallel Computing Metrics for TMAs
301(1)
12.3.2 Matrix Multiplication (MM) Case Study
302(1)
12.4 Matrix Multiplication Algorithms' Code Snippets for Tilera's TILEPro64
303(11)
12.4.1 Serial Non-blocked Matrix Multiplication Algorithm
303(1)
12.4.2 Serial Blocked Matrix Multiplication Algorithm
304(3)
12.4.3 Parallel Blocked Matrix Multiplication Algorithm
307(2)
12.4.4 Parallel Blocked Cannon's Algorithm for Matrix Multiplication
309(5)
12.5 Performance Optimization on a Manycore Architecture
314(9)
12.5.1 Performance Optimization on a Single Tile
314(1)
12.5.2 Parallel Performance Optimizations
315(4)
12.5.3 Compiler-Based Optimizations
319(4)
12.6 Results
323(16)
12.6.1 Data Allocation, Data Decomposition, Data Layout, and Communication
324(3)
12.6.2 Performance Optimizations on a Single Tile
327(5)
12.6.3 Parallel Performance Optimizations
332(7)
12.7
Chapter Summary
339(4)
13 Conclusions
343(6)
References 349(20)
Index 369
Arslan Munir, University of Nevada, Reno (UNR), USA Arslan Munir is currently an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the UNR. Before then he was a postdoctoral research associate in the Electrical and Computer Engineering (ECE) department at Rice University (Houston, Texas) between May 2012 and June 2014. He received his M.A.Sc. in ECE from the University of British Columbia (Vancouver, Canada) in 2007 and his Ph.D. in ECE from the University of Florida (Gainesville, Florida) USA in 2012. Between 2007 and 2008, he worked as a software development engineer at Mentor Graphics in the Embedded Systems Division. His current research interests include embedded and cyber-physical systems, computer architecture, parallel computing, fault-tolerance, and computer security.

Ann Gordon-Ross, University of Florida, USA Ann Gordon-Ross is currently an Associate Professor of Electrical and Computer Engineering at the University of Florida and is a member of the NSF Center for High Performance Reconfigurable Computing (CHREC) at the University of Florida. She is also the faculty advisor for the Women in Electrical and Computer Engineering (WECE) and the Phi Sigma Rho National Society for Women in Engineering and Engineering Technology. Her research interests include embedded systems, computer architecture, low-power design, reconfigurable computing, dynamic optimizations, hardware design, real-time systems, and multi-core platforms.

Sanjay Ranka, University of Florida, USA Sanjay Ranka researches energy efficient computing, high performance computing, data mining and informatics at the University of Florida's Department of Computer Science. He has coauthored two books, 75 journal articles and 125 refereed conference articles. He is a fellow of the IEEE and AAAS, and a member of IFIP Committee on System Modeling and Optimization.
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