Python Concurrency and Parallel Programming

Python concurrency and parallel programming encompasses techniques for executing multiple tasks simultaneously to improve application performance and responsiveness. Concurrency, often achieved through modules like `threading` and `asyncio`, involves managing multiple tasks within a single process, creating the illusion of simultaneous execution and is ideal for I/O-bound operations where the program waits for external resources. In contrast, parallelism, primarily handled by the `multiprocessing` module, distributes tasks across multiple CPU cores to achieve true simultaneous execution, making it suitable for CPU-bound computations. This distinction is crucial in Python due to the Global Interpreter Lock (GIL), which restricts a single Python process from running threads on multiple cores at once, thereby making `multiprocessing` the standard solution for true parallelism.

1. Fundamentals of Concurrency and Parallelism
   1. Defining Concurrency vs. Parallelism
      1. Concurrency Concepts
         1. Task switching
         2. Interleaving execution
         3. Cooperative multitasking
         4. Preemptive multitasking
      2. Parallelism Concepts
         1. True parallel execution
         2. Multi-core systems
         3. Multi-processor systems
         4. Data parallelism
         5. Task parallelism
   2. Task Classification
      1. I/O-Bound Tasks
         1. Characteristics and identification
         2. Network operations
            1. HTTP requests
            2. Socket communication
            3. API calls
         3. File system operations
            1. File reading
            2. File writing
            3. Directory operations
         4. Database operations
            1. Query execution
            2. Data insertion
            3. Transaction processing
      2. CPU-Bound Tasks
         1. Characteristics and identification
         2. Mathematical computations
            1. Numerical algorithms
            2. Statistical calculations
            3. Cryptographic operations
         3. Data processing
            1. Data transformation
            2. Aggregation operations
            3. Sorting and filtering
         4. Media processing
            1. Image manipulation
            2. Video encoding
            3. Audio processing
   3. The Global Interpreter Lock (GIL)
      1. GIL Overview
         1. Purpose and design rationale
         2. CPython implementation details
         3. Historical context
      2. GIL Impact on Threading
         1. Thread scheduling mechanisms
         2. Performance implications
         3. Bytecode execution control
      3. Task-Specific GIL Effects
         1. CPU-bound task limitations
         2. I/O-bound task advantages
         3. GIL release during I/O operations
      4. Memory Management and GIL
         1. Reference counting protection
         2. Thread safety guarantees
         3. Garbage collection coordination