python_multiprocessing_visualization

This repository contains a command-line tool that visualizes performance differences across various concurrency and parallelism approaches in Python. It compares concurrent vs. parallel execution and evaluates different libraries used in each approach.

📖 Read the detailed Medium article about this project

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💡 Supported Implementations

Upon running the CLI tool, you can select from the following implementations:

1. Threaded
2. Sequential
3. ThreadPool
4. Asyncio
5. Multiprocessing
6. Multiprocessing Pool
7. Multiprocessing ProcessPoolExecutor
8. Generate Bar Graph

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🛠 Usage

After choosing the implementation, you’ll be prompted to:

• Enter the prime number you want to test
• Choose how many times to run this test (to simulate more or less CPU intensity)
• Provide the pool size, if applicable
• Indicate whether to simulate non-blocking I/O-bound behavior (adds artificial delays to mimic I/O waits)

After the simulation completes, you can generate a bar graph comparing the execution times across implementations.

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📚 Key Learnings

1. ⚠️ Threaded, Sequential, and Asyncio implementations perform similarly on CPU-bound tasks because they are all limited by Python’s Global Interpreter Lock (GIL).

2. 🚀 Multiprocessing significantly outperforms other methods for CPU-bound tasks. It uses separate processes, each with its own Python interpreter and memory space, allowing true parallel execution on multiple cores.

3. ⚡ For non-blocking I/O-bound tasks, Asyncio can be more efficient than multiprocessing. While multiprocessing waits for I/O in each process, Asyncio can switch between coroutines during non-blocking I/O wait times, enabling better resource utilization.