Concurrent and Parallel Programming

Concurrent and parallel programming is a paradigm in computer science focused on designing software where multiple computations or processes execute simultaneously or in overlapping time periods. Concurrency deals with the challenge of managing and structuring multiple tasks that are in progress at the same time, often to improve responsiveness or handle multiple external events, and can be implemented even on a single processor core through time-slicing. Parallelism, a specific form of concurrency, leverages multi-core processors or distributed systems to execute multiple tasks simultaneously, with the primary goal of accelerating computation by breaking a large problem into smaller pieces that are solved in parallel. Both approaches introduce complexities related to task synchronization, communication, and managing shared resources to avoid issues like race conditions and deadlocks.

  1. Introduction to Concurrent and Parallel Computing
    1. Overview of Concurrent and Parallel Computing
      1. Historical Context and Evolution
        1. Importance in Modern Computing
        2. Defining Concurrency
          1. Concept of Concurrency
            1. Managing Multiple Tasks Over Time
              1. Illusion of Simultaneity
                1. Time-Slicing
                  1. Cooperative Multitasking
                    1. Preemptive Multitasking
                      1. Use Cases for Concurrency
                        1. User Interface Responsiveness
                          1. Input/Output Handling
                            1. Real-Time Systems
                          2. Defining Parallelism
                            1. Concept of Parallelism
                              1. Simultaneous Execution of Tasks
                                1. Computational Speedup Goals
                                  1. Multiple Processing Unit Requirements
                                    1. Use Cases for Parallelism
                                      1. Scientific Computing
                                        1. Data Processing
                                          1. Graphics Rendering
                                        2. Concurrency vs. Parallelism
                                          1. Key Distinctions
                                            1. Relationship Between Concurrency and Parallelism
                                              1. Concurrent Systems on Single Core
                                                1. Parallel Systems on Multiple Cores
                                                  1. Overlapping Use Cases
                                                    1. Non-Overlapping Use Cases
                                                    2. Motivation for Concurrency and Parallelism
                                                      1. End of Moore's Law for Single-Core Frequency Scaling
                                                        1. Rise of Multi-Core Processors
                                                          1. Rise of Many-Core Processors
                                                            1. Energy Efficiency Considerations
                                                              1. Large-Scale Data Handling
                                                                1. Complex Computation Requirements
                                                                  1. Software Scalability
                                                                    1. Software Responsiveness