Tests to improve performance in computationally intensive tasks.

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Project description

SpeedestPy

Warning: We use plateform/compilator dependent code, It may not works on your computer ! This package will only be tested on linux(Ubuntu 22.04) for now

This project demonstrates how to integrate external process into Python applications to improve performance in computationally intensive tasks.

For the moment we are only using python, numpy and rust.

  • src/ contains rust sources
  • src_python/ contains python sources

Performance Benchmark

Use scripts/speed_test.py

The following benchmarks compare the execution times of equivalent operations performed using pure Python, NumPy, and Rust with various libraries and techniques:

Add one
array shape: (50000, 10000)
Execution time (Python): 6.225 seconds
Execution time (rust): 0.324 seconds
----------------------------------
Pairwise distances
array shape: (10000, 3)
Execution time (Python): 382.692 seconds
Execution time (Numpy): 2.525 seconds
Execution time (rust): 6.646 seconds
Execution time (rust ndarray): 4.203 seconds
Execution time (rust rayon): 0.838 seconds
Execution time (rust ndarray parralel): 0.495 seconds

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