Numba vs NumPy vs cuPy in Python: A Comparison

CuPy vs. Numpy vs. Numba

TDS Editors Rinu Gour Amit Shekhar

Hello Folks, Today we will learn Numba vs NumPy vs cuPy in Python: A Comparison.

Python is a versatile language, but when it comes to performance, especially in numerical computations, it can sometimes fall short. This is where libraries like Numba, Numpy, and Cython come into play. Each of these libraries offers unique advantages and optimizations. In this blog post, we’ll compare Numba, Numpy, and Cython, focusing on their syntax and time complexity.

The Eternal Debate!

Numba, Cupy, and Numpy are three popular libraries used for numerical computation in Python. While they share some similarities, each has its strengths and weaknesses, Here’s a brief comparison:

1. NumPy (Numerical Python)

NumPy is the fundamental package for scientific computing in Python. It provides support for arrays, matrics, and many mathematical functions.

Example 1:

import numpy as np

# create a 2D array
arr = np.array([[1,2,3],[4,5,6]])

# perform matrix multiplication
result = np.matmul(arr,arr.T)

print(result)

Here, we see a simple eg:

import numpy as np…