Mojo Benchmarking

Comparative benchmarking of Mojo and Python across various data structures and algorithms.

Benchmarking Results

This section provides the performance results of various algorithms across both Python and Mojo in a tabular format.

Algorithm	Python Performance (1000 tests)	Mojo Performance (1000 tests)
Insertion Sort	Average: 0.09246 seconds Total: 92.46207 seconds	Average: Pending Total: Pending
Bubble Sort	Average: 0.17668 seconds Total: 176.68430 seconds	Average: Pending Total: Pending

To replicate, run

python python/runner.py insertion_sort

To-Do List

Data Organization Methods:

• Hashing
• Trees
• Queues
• Lists
• Priority Queues

Streaming Algorithms:

• Computing statistics on data

Sorting and Searching:

• Insertion Sort
• Bubble Sort
• TBD Sorting algorithms
• TBD Search algorithms

Graph Algorithms:

• Basic graph models
• Search algorithms
• Shortest paths algorithms
• Matching algorithms

Programming:

• Dynamic Programming
• Linear Programming
• Convex Programming

Numerical Algorithms:

• Floating point arithmetic
• Stability of numerical algorithms
• Eigenvalues
• Singular values
• Principal Component Analysis (PCA)
• Gradient Descent
• Stochastic Gradient Descent
• Block Coordinate Descent
• Conjugate Gradient
• Newton Methods
• Quasi-Newton Methods

Large Scale Applications:

• Signal Processing
• Collaborative Filtering
• Recommendation Systems

Collaboration

Collaboration is welcome, and your contribution is valuable.

To get started:

1. Fork the repository.
2. Pick a task from the to-do list above.
3. Make your changes.
4. Submit a pull request.