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📊 Simple Runtime Analyzer

Execution Time Measurement and Visualization with Python:

This project is a basic example designed to demonstrate how to measure execution times in C++, process data, and visualize results using Python. It features a function that generates random sequences, processes the data, and outputs performance metrics to a file, which are then visualized to analyze the relationship between input size and execution time. This approach is useful for comparing the performance of different algorithms, exploring the gap between real and theoretical runtimes, and deepening understanding of computational efficiency analysis.

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📁 File Structure

File / Script	Description
src/measure_times.cpp	Main entry point. Generates random sequences, measures processSequence execution time, and saves results to a .txt file.
src/process_sequence.hpp	Declaration of the processSequence function, which handles sequence processing.
src/process_sequence.cpp	Implementation of processSequence: sorts the sequence, removes duplicates, and calculates cumulative sums.
scripts/plot.py	Python script that reads the metrics file and generates a PNG graph.
Makefile	Automates the compilation of C++ files and executes both the binary and Python script. Includes targets such as all, run, and clean.

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⚙️ Program Workflow

1. Data Generation

   • Vectors of random integers are generated with values between 0 and 10000.
   • Sequence sizes vary from 1,000 to 7,000,000 elements.

2. Sequence Processing

   • Sequences are sorted (std::sort), duplicates are removed (std::unique + erase), and cumulative sums are computed from the second element onward.

3. Time Measurement

   • std::chrono::high_resolution_clock is used to capture timestamps before and after processing.
   • Results are saved in runtime_metrics.txt.

4. Visualization

   • The script plot.py reads the .txt file, extracts size-time pairs, and generates a graph using matplotlib.

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🐍 Python Requirements

Ensure the following dependencies are installed:

• Python 3.x
• matplotlib

You can install matplotlib with:

pip install matplotlib

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🧱 Compilation Requirements

To compile and run the program:

• A modern C++ compiler supporting C++20 (e.g., clang++ or g++)
• Make (to use the Makefile)
• Python 3 with matplotlib installed

✅ Compile and Run

From the root directory of the project, run:

make run

This will compile the program, measure execution times, and generate the graph.

🧹 Clean Generated Files

make clean

📜 This project is licensed under the MIT License © 2025 Diego Osorio (ShineKnightDev).