Arslan Munir - Modeling and Optimization of Parallel and Distributed Embedded Systems

1. Chapter 2: Multicore-Based EWSNsAn Example of Parallel and Distributed Embedded Systems
2. Chapter 4: Modeling and Analysis of Fault Detection and Fault Tolerance in Embedded Wireless Sensor Networks
3. Chapter 5: A Queueing Theoretic Approach for Performance Evaluation of Low-Power Multicore-Based Parallel Embedded Systems
4. Chapter 6: Optimization Approaches in Distributed Embedded Wireless Sensor Networks
5. Chapter 7: High-Performance Energy-Efficient Multicore-Based Parallel Embedded Computing
6. Chapter 8: An MDP-Based Dynamic Optimization Methodology for Embedded Wireless Sensor Networks
7. Chapter 9: Online Algorithms for Dynamic Optimization of Embedded Wireless Sensor Networks
8. Chapter 10: A Lightweight Dynamic Optimization Methodology for Embedded Wireless Sensor Networks
9. Chapter 11: Parallelized Benchmark-Driven Performance Evaluation of Symmetric Multiprocessors and Tiled Multicore Architectures for Parallel Embedded Systems
10. Chapter 12: High-Performance Optimizations on Tiled Manycore Embedded Systems: A Matrix Multiplication Case Study

1. Chapter 3: An Application Metrics Estimation Model for Embedded Wireless Sensor Networks
2. Chapter 4: Modeling and Analysis of Fault Detection and Fault Tolerance in Embedded Wireless Sensor Networks
3. Chapter 5: A Queueing Theoretic Approach for Performance Evaluation of Low-Power Multicore-Based Parallel Embedded Systems
4. Chapter 6: Optimization Approaches in Distributed Embedded Wireless Sensor Networks
5. Chapter 7: High-Performance Energy-Efficient Multicore-Based Parallel Embedded Computing
6. Chapter 8: An MDP-Based Dynamic Optimization Methodology for Embedded Wireless Sensor Networks
7. Chapter 9: Online Algorithms for Dynamic Optimization of Embedded Wireless Sensor Networks
8. Chapter 10: A Lightweight Dynamic Optimization Methodology for Embedded Wireless Sensor Networks
9. Chapter 11: Parallelized Benchmark-Driven Performance Evaluation of Symmetric Multiprocessors and Tiled Multicore Architectures for Parallel Embedded Systems
10. Chapter 12: High-Performance Optimizations on Tiled Manycore Embedded Systems: A Matrix Multiplication Case Study

Arslan Munir

University of Nevada, Reno, USA

Ann Gordon-Ross

University of Florida, Gainesville, USA

Sanjay Ranka

University of Florida, Gainesville, USA

This edition first published 2016

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To our families

Advancements in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics have led to the proliferation of embedded systems in a plethora of application domains (e.g., industrial and home automation, automotive, space, medical, and defense). To meet the diverse application requirements of these application domains, novel trends have emerged in embedded systems. One such trend is networking single-unit embedded systems to form a multiple-unit embedded system, also referred to as a distributed embedded system. Given the collective computing capabilities of the single-unit embedded systems, the distributed embedded system enables more sophisticated applications of greater value as compared to an isolated single-unit embedded system. An emerging trend is to connect these distributed embedded systems via a wireless network instead of a bulky, wired networking infrastructure. Another emerging trend in embedded systems is to leverage multicore/manycore architectures to meet the continuously increasing performance demands of many application domains (e.g., medical imaging, mobile signal processing). Both single-unit and distributed embedded systems can leverage multicore architectures for attaining high performance and energy efficiency. Since processing is done in parallel with multicore-based embedded systems, these systems are being termed as parallel embedded systems. The burgeoning of multicore architectures in embedded systems induces parallel computing into the embedded domain, which was previously used predominantly in the supercomputing domain only. In some applications, parallel embedded systems are networked together to form parallel and distributed embedded systems. For both parallel and distributed embedded systems, modeling and optimization at various design levels (e.g., verification, simulation, analysis) are of paramount significance. Considering the short time-to-market for many embedded systems, often embedded system designers resort to modeling approaches for the evaluation of design alternatives in terms of performance, power, reliability, and/or scalability.

Embedded computers have advanced well beyond the early days of 8-bit microcontrollers. Contemporary embedded computers are organized into multiprocessors that execute millions of lines of code in real time and at very low power levels. This book targets parallel and distributed embedded systems, which have been enabled by technological advances in silicon technology, MEMS, wireless communications, computer networking, and digital electronics. These parallel and distributed embedded systems have applications in various domains, such as military and defense, medical, automotive, and unmanned autonomous vehicles.