A configurable benchmarking suite for classic sorting algorithms on large integer arrays. Designed for reproducible, high-precision performance analysis and comparison.

• Controlled Data Generation

  • Generate arrays of arbitrary length (100 → 100 000) or value-range variability (10 → 1 000 000)
  • Geometric spacing of sample sizes (100 samples)
  • Deterministic seeding for full reproducibility

• Flexible Storage

  • Compress & store generated arrays on disk with ZPAQ + pickle
  • Single-output file per sample set (ArrayStorage.pick)
  • Automatic folder creation with timestamp

• Precision Timing & Statistics

  • Adaptive minTime based on clock resolution + relative error threshold
  • Per-sample measurement of mean, median, IQR, outliers
  • Support for asymmetric error bars (e.g. median ± IQR)

• Prebuilt Comparisons

  • QuickSort (2-way & 3-way), CountingSort, RadixSort
  • Plug-in any additional SortingAlgorithm

• Visualization & Export

  • Interactive Plotly plots (linear & log-log scaling) that estimate the mean execution time with a 95 % confidence interval
  • JSON export of raw & aggregated statistics

The final report — including methodology, data analysis, and conclusions — is available at this page:

https://londero-lorenzo.github.io/SortingAlgorithms/

This page includes:

• A readable version of the report as PDF (Word-based)
• Interactive performance plots
• Access to source code and benchmarks

• Variability.onLength → arrays vary by length (constant value range)
• Variability.onNumbers → arrays vary by number of distinct elements (constant length)

• minTime and relative error thresholds are defined in Utils/TimingSettings.py

• All benchmarks now run sequentially; there is no multiprocessing support.
• Only one .pick file is generated per sample set (no chunking).
• Visualization is handled exclusively via Plotly (no Matplotlib).
• Interactive graphs are saved as standalone HTML files, suitable for GitHub Pages.
• Final plots estimate the mean execution time for each data point and include a 95 % confidence interval.
• The interactive benchmark visualization is available at:
  https://londero-lorenzo.github.io/SortingAlgorithms/benchmarks.html

git clone https://github.com/londero-lorenzo/SortingAlgorithms.git cd SortingAlgorithms python3.11 setup.py

1. Generate Data

   python create_sample_arrays.py

   • Generates compressed .pick files for each variability configuration.

2. Run Benchmarks

   (venv) python -m Benchmark.benchmark_runner --input <path_to_arrays> --output <output_folder> --algorithms <algorithm_names>

   • Executes all selected sorting algorithms on the generated arrays.
   • Results are saved as .time files under Benchmark/ExecutionTimes/X_Variability.

3. Visualize Results

   python view_benchmarks.py

   or more specifically:

   (venv) python -m Utils.graph_viewer --file <path_to_time_files> --auto --searchFolder <search_path> --output <output_html>

   • Generates interactive Plotly graphs (HTML) or opens them in a browser.
   • Supports both length variability and number variability modes.

4. View Interactive Graph (on GitHub Pages)

   • Open https://londero-lorenzo.github.io/SortingAlgorithms/benchmarks.html to explore the interactive benchmark visualizations.

• Only relevant files and folders are shown below for clarity.

SortingAlgorithms/ ├─ Array/ │ ├─ ArrayStorage/ │ │ ├─ N_variability/ # Length-based sample sets │ │ └─ M_variability/ # Number-based sample sets │ └─ ArraySettings.py # Variability & storage config │ ├─ Benchmark/ │ ├─ ExecutionTimes/ │ │ ├─ N_variability/ # .time files for length variability │ │ └─ M_variability/ # .time files for number variability │ ├─ Benchmark.ipynb # Notebook for interactive benchmark │ ├─ benchmark_runner.py # Sequential benchmark executor **(TODO)** │ └─ BenchmarkViewer.ipynb # Notebook for result analysis │ ├─ Report/ │ └─ Charts/ │ ├─ N_variability/ # Plotly figures for length variability │ └─ M_variability/ # Plotly figures for number variability │ ├─ Utils/ │ ├─ ArrayGenerator.py # Data generation logic │ ├─ ArraySettings.py # Variability & file naming │ ├─ ArrayStorageCompressor.py # Saving file using ZPAQ + pickle │ ├─ SortingSettings.py # minTime & error thresholds │ └─ graph_viewer.py # Interactive plot generator │ ├─ docs/ │ └─ benchmarks.html # Interactive HTML visualization │ ├─ create_sample_arrays.py # CLI for data generation ├─ setup.py # Installation & dependency setup ├─ start_jupyter.py	# Launching jupyter lab ├─ start_venv.py	# Launching virtual environment ├─ view_benchmarks.py # Launching graphical interaction └─ README.md