Acceleration Libraries for CUDA

This repository showcases practical examples of how using PyCUDA and Numba, two powerful tools for GPU programming with Python. Whether you’re exploring the flexibility of PyCUDA or the simplicity of Numba’s Python-native syntax, you’ll find detailed examples and explanations to help you harness the computational power of CUDA.

What You’ll Learn

• Efficiently perform parallel computations on GPUs.
• Understand the similarities and differences between PyCUDA and Numba.
• Explore hands-on code examples for adding vectors on the GPU.

Why This Matters

As GPUs become increasingly central to high-performance computing, learning how to optimize basic operations like vector summation is a great way to build a foundation in GPU programming. Both PyCUDA and Numba offer unique advantages, and this repository will help you decide which tool suits your needs.

Pages

• Five different ways to sum vectors in PyCUDA
• Summing vectors in Numba