Parallel and Distributed Programming Using C 1st Edition by Cameron Hughes, Tracey Hughes ISBN 0321544676 9780321544674

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ISBN 10: 0321544676
ISBN 13: 9780321544674
Author: Cameron Hughes, Tracey Hughes

Parallel and Distributed Programming Using C 1st Edition Table of contents:

Chapter 1: Introduction to Parallel and Distributed Computing

• Understanding Parallel and Distributed Systems
• The Need for Parallel and Distributed Computing
• Key Concepts and Terminology
• Types of Parallelism: Data Parallelism vs. Task Parallelism
• Challenges in Parallel and Distributed Programming

Chapter 2: Overview of the C Programming Language

• A Quick Refresher on C
• Data Types, Functions, and Control Structures in C
• Memory Management in C
• Debugging and Optimization Techniques in C

Chapter 3: Basics of Parallel Programming

• Introduction to Parallel Computing
• Parallel Architectures: Shared Memory vs. Distributed Memory
• Threading and Concurrency
• The Role of Processes and Threads in Parallel Programming
• Basic Parallel Programming Techniques

Chapter 4: Using POSIX Threads (Pthreads)

• Introduction to Pthreads
• Creating and Managing Threads in C
• Synchronization: Mutexes and Semaphores
• Thread Safety and Race Conditions
• Advanced Pthread Features

Chapter 5: Parallel Programming Models

• SPMD (Single Program Multiple Data) Model
• SIMD (Single Instruction Multiple Data) and MIMD (Multiple Instruction Multiple Data)
• Task Parallelism vs. Data Parallelism
• Map-Reduce and Divide and Conquer Models

Chapter 6: Message Passing Interface (MPI)

• Introduction to MPI and Distributed Memory Systems
• Basic MPI Functions: Initialization, Communication, Synchronization
• Point-to-Point Communication and Collective Communication in MPI
• Implementing MPI in C
• Advanced MPI Features: Non-blocking Communication, Derived Data Types

Chapter 7: OpenMP for Shared Memory Programming

• Introduction to OpenMP for Parallelism in Shared Memory Systems
• Basic OpenMP Directives and Syntax
• Parallel Loops and Worksharing Constructs
• Synchronization in OpenMP
• Advanced OpenMP Techniques: Task Parallelism and Dynamic Scheduling

Chapter 8: Parallel Algorithms

• Designing Parallel Algorithms
• Parallelizing Common Algorithms: Sorting, Searching, Matrix Operations
• Performance Considerations in Parallel Algorithms
• Load Balancing and Scheduling
• Dealing with Communication Overheads

Chapter 9: Distributed Computing Concepts

• Fundamentals of Distributed Systems
• Client-Server Architecture in Distributed Systems
• Remote Procedure Calls (RPC)
• Handling Fault Tolerance and Redundancy
• Synchronization and Distributed Coordination

Chapter 10: Distributed Programming Using MPI

• Distributed Memory Systems and MPI
• Implementing Distributed Algorithms with MPI
• Load Balancing in Distributed Systems
• Fault Tolerance and Reliability in MPI
• Performance Tuning in Distributed Systems

Chapter 11: Parallel File Systems and I/O

• Introduction to Parallel I/O Systems
• File Sharing and Synchronization in Parallel Programs
• Using MPI for File Operations
• High-Performance Storage Systems in Parallel Computing

Chapter 12: Debugging and Optimizing Parallel Programs

• Common Bugs in Parallel Programs: Deadlocks, Race Conditions, and Starvation
• Tools for Debugging Parallel Programs: gdb, Valgrind, and MPI Debuggers
• Performance Analysis and Optimization in Parallel Programs
• Memory Profiling and Minimizing Memory Overhead

Chapter 13: Scaling Parallel and Distributed Programs

• Challenges in Scaling Parallel and Distributed Applications
• Performance Bottlenecks in Large-Scale Systems
• Load Balancing and Work Partitioning
• Parallelism in Multi-core and Many-core Systems
• Scaling to Supercomputers and Cloud Computing Environments

Chapter 14: Case Studies and Applications

• Real-World Examples of Parallel and Distributed Systems
• Case Study 1: Parallel Numerical Simulations
• Case Study 2: Distributed Database Systems
• Case Study 3: High-Performance Computing in Science and Engineering
• Case Study 4: Cloud Computing and Large-Scale Distributed Systems

Chapter 15: The Future of Parallel and Distributed Programming

• Emerging Trends in Parallel Computing
• The Role of GPUs and Accelerators in Parallel Programming
• Quantum Computing and its Impact on Parallel Programming
• The Future of Distributed Systems: Edge Computing, IoT, and Beyond

Conclusion

• Key Takeaways from the Book
• Resources for Further Learning
• Final Thoughts on Parallel and Distributed Programming

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