Concurrent Computing Performance

This project compares the performance of different concurrency models in handling HTTP GET and POST requests. The three models examined are:

• Multi-threading
• Multi-processing
• Asynchronous programming using coroutines

Directory Structure

.
├── WebApp
│   └── simple_web_app.py
└── compare.py

Web Application

The web application is built using Flask and provides a simple API with the following endpoints:

• GET /: Returns a welcome message.
• GET /api/data: Returns a response after simulating a delay.
• POST /api/data: Accepts JSON data and returns it after a delay.

Running the Web Application

1. Navigate to the WebApp directory:

   cd WebApp

2. Install Flask if you haven't already:

   pip install Flask

3. Run the web application:

   python simple_web_app.py

   The application will be available at http://127.0.0.1:5000.

Performance Comparison

The compare.py script benchmarks the three concurrency models against the web application.

Ensure the web application is running.

In a separate terminal, navigate to the project root directory:

cd path/to/ConcurrentComputingPerformance

Execute the performance comparison script:

python compare.py

Results

The script will output the time taken for each concurrency model to complete the requests, allowing you to compare their performance directly.

Time taken with threading: 5.050655364990234 seconds

Time taken with multiprocessing: 6.980526447296143 seconds

Time taken with asyncio: 3.007309675216675 seconds