Array-Sorting-Benchmark

This project benchmarks and compares the performance and energy efficiency of five different array sorting algorithms. The algorithms are implemented in separate files: built_in_sort.py, merge_sort.py, case_insensitive_sort.py, quick_sort.py, and heap_sort.py. Additionally, there is an array generator script, array_generator.py, to create sample arrays for testing.

Algorithms

1. Built-in Sort: This algorithm uses the built-in sorted function in Python to sort an array of words loaded from a file (random_words_array.npy).
2. Merge Sort: Implementation of the merge sort algorithm. It recursively divides the array into halves, sorts them, and then merges them back together.
3. Case-Insensitive Sort: Utilizes the sorted function with a custom key to perform a case-insensitive sorting of the array loaded from the file (random_words_array.npy).
4. Quick Sort: Implements the quicksort algorithm to sort an array of words loaded from a file (random_words_array.npy).
5. Heap Sort: This algorithm uses the heapq module to perform an in-place heap sort on the array of words loaded from a file (random_words_array.npy). The array is converted to a list before applying heap sort.

Array Generator

The array_generator.py script generates an array of random words and saves it to a binary file (random_words_array.npy) for testing the sorting algorithms.

Running the Benchmark

To run the benchmark and generate performance metrics and energy efficiency scores, execute the energy_counter.py script.

python energy_counter.py

Results and Visualizations

The benchmark results are visualized using matplotlib, displaying metrics such as memory consumption, average CPU usage, peak CPU usage, execution time, and energy efficiency scores.

Interpretation of Energy Efficiency Scores

The energy efficiency score is calculated as the ratio of execution time to peak CPU usage. Lower scores indicate better energy efficiency.

Conclusion

After running the benchmark, the script identifies and prints the most energy-efficient sorting algorithm based on the calculated scores.

Dependencies

1. Python 3
2. NumPy
3. Matplotlib
4. Psutil

Install dependencies using:

pip install numpy matplotlib psutil

This project was made with the help of @Bita05. Feel free to contribute, report issues, or suggest improvements!

License

This project is licensed under the MIT License - see the LICENSE file for details.