Numba - High-Performance Python with JIT

Numba makes Python code go fast. It works by decorating your functions with decorators that tell Numba to compile them. It is particularly effective for code that involves heavy numerical loops and NumPy array manipulations.

When to Use

• When NumPy's built-in vectorization isn't enough for your specific algorithm.
• You have complex nested loops that are slow in standard Python.
• You need to write custom "ufuncs" (universal functions) that operate element-wise on arrays.
• High-performance physical simulations (Monte Carlo, N-body, Grid-based solvers).
• Accelerating code for execution on NVIDIA GPUs (CUDA).
• Creating parallelized code that utilizes all CPU cores without the overhead of multiprocessing.

Reference Documentation

Official docs: https://numba.pydata.org/numba-doc/latest/index.html
User Guide: https://numba.pydata.org/numba-doc/latest/user/index.html
Search patterns: @njit, @vectorize, prange, cuda.jit, numba.typed