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Modeling and Optimization of Parallel and Distributed Embedded Systems

Book Description

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.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Preface
    1. About This Book
    2. Highlights
    3. Intended Audience
    4. Organization of the Book
  6. Acknowledgment
  7. Part One: Overview
    1. Chapter 1: Introduction
      1. 1.1 Embedded Systems Applications
      2. 1.2 Characteristics of Embedded Systems Applications
      3. 1.3 Embedded Systems—Hardware and Software
      4. 1.4 Modeling—An Integral Part of the Embedded Systems Design Flow
      5. 1.5 Optimization in Embedded Systems
      6. 1.6 Chapter Summary
    2. Chapter 2: Multicore-Based EWSNs—An Example of Parallel and Distributed Embedded Systems
      1. 2.1 Multicore Embedded Wireless Sensor Network Architecture
      2. 2.2 Multicore Embedded Sensor Node Architecture
      3. 2.3 Compute-Intensive Tasks Motivating the Emergence of MCEWSNs
      4. 2.4 MCEWSN Application Domains
      5. 2.5 Multicore Embedded Sensor Nodes
      6. 2.6 Research Challenges and Future Research Directions
      7. 2.7 Chapter Summary
  8. Part Two: Modeling
    1. Chapter 3: An Application Metrics Estimation Model for Embedded Wireless Sensor Networks
      1. 3.1 Application Metrics Estimation Model
      2. 3.2 Experimental Results
      3. 3.3 Chapter Summary
    2. Chapter 4: Modeling and Analysis of Fault Detection and Fault Tolerance in Embedded Wireless Sensor Networks
      1. 4.1 Related Work
      2. 4.2 Fault Diagnosis in WSNs
      3. 4.3 Distributed Fault Detection Algorithms
      4. 4.4 Fault-Tolerant Markov Models
      5. 4.5 Simulation of Distributed Fault Detection Algorithms
      6. 4.6 Numerical Results
      7. 4.7 Research Challenges and Future Research Directions
      8. 4.8 Chapter Summary
    3. Chapter 5: A Queueing Theoretic Approach for Performance Evaluation of Low-Power Multicore-Based Parallel Embedded Systems
      1. 5.1 Related Work
      2. 5.2 Queueing Network Modeling of Multicore Embedded Architectures
      3. 5.3 Queueing Network Model Validation
      4. 5.4 Queueing Theoretic Model Insights
      5. 5.5 Chapter Summary
  9. Part Three: Optimization
    1. Chapter 6: Optimization Approaches in Distributed Embedded Wireless Sensor Networks
      1. 6.1 Architecture-Level Optimizations
      2. 6.2 Sensor Node Component-Level Optimizations
      3. 6.3 Data Link-Level Medium Access Control Optimizations
      4. 6.4 Network-Level Data Dissemination and Routing Protocol Optimizations
      5. 6.5 Operating System-Level Optimizations
      6. 6.6 Dynamic Optimizations
      7. 6.7 Chapter Summary
    2. Chapter 7: High-Performance Energy-Efficient Multicore-Based Parallel Embedded Computing
      1. 7.1 Characteristics of Embedded Systems Applications
      2. 7.2 Architectural Approaches
      3. 7.3 Hardware-Assisted Middleware Approaches
      4. 7.4 Software Approaches
      5. 7.5 High-Performance Energy-Efficient Multicore Processors
      6. 7.6 Challenges and Future Research Directions
      7. 7.7 Chapter Summary
    3. Chapter 8: An MDP-Based Dynamic Optimization Methodology for Embedded Wireless Sensor Networks
      1. 8.1 Related Work
      2. 8.2 MDP-Based Tuning Overview
      3. 8.3 Application-Specific Embedded Sensor Node Tuning Formulation as an MDP
      4. 8.4 Implementation Guidelines and Complexity
      5. 8.5 Model Extensions
      6. 8.6 Numerical Results
      7. 8.7 Chapter Summary
    4. Chapter 9: Online Algorithms for Dynamic Optimization of Embedded Wireless Sensor Networks
      1. 9.1 Related Work
      2. 9.2 Dynamic Optimization Methodology
      3. 9.3 Experimental Results
      4. 9.4 Chapter Summary
    5. Chapter 10: A Lightweight Dynamic Optimization Methodology for Embedded Wireless Sensor Networks
      1. 10.1 Related Work
      2. 10.2 Dynamic Optimization Methodology
      3. 10.3 Algorithms for Dynamic Optimization Methodology
      4. 10.4 Experimental Results
      5. 10.5 Chapter Summary
    6. Chapter 11: Parallelized Benchmark-Driven Performance Evaluation of Symmetric Multiprocessors and Tiled Multicore Architectures for Parallel Embedded Systems
      1. 11.1 Related Work
      2. 11.2 Multicore Architectures and Benchmarks
      3. 11.3 Parallel Computing Device Metrics
      4. 11.4 Results
      5. 11.5 Chapter Summary
    7. Chapter 12: High-Performance Optimizations on Tiled Manycore Embedded Systems: A Matrix Multiplication Case Study
      1. 12.1 Related Work
      2. 12.2 Tiled Manycore Architecture (TMA) Overview
      3. 12.3 Parallel Computing Metrics and Matrix Multiplication (MM) Case Study
      4. 12.4 Matrix Multiplication Algorithms' Code Snippets for Tilera's TILEPro64
      5. 12.5 Performance Optimization on a Manycore Architecture
      6. 12.6 Results
      7. 12.7 Chapter Summary
    8. Chapter 13: Conclusions
  10. References
    1. Index
  11. End User License Agreement