https://github.com/hrolive/fundamentals-of-accelerated-computing-with-cuda-python

Explore how to use Numba—the just-in-time, type-specializing Python function compiler—to create and launch CUDA kernels to accelerate Python programs on massively parallel NVIDIA GPUs.
https://github.com/hrolive/fundamentals-of-accelerated-computing-with-cuda-python

accelerated-computing cuda cuda-programming jit numba nvidia python

Last synced: 11 days ago
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Explore how to use Numba—the just-in-time, type-specializing Python function compiler—to create and launch CUDA kernels to accelerate Python programs on massively parallel NVIDIA GPUs.

• Host: GitHub
• URL: https://github.com/hrolive/fundamentals-of-accelerated-computing-with-cuda-python
• Owner: HROlive
• Created: 2024-10-24T13:16:05.000Z (12 days ago)
• Default Branch: main
• Last Pushed: 2024-10-24T13:23:01.000Z (12 days ago)
• Last Synced: 2024-10-25T15:47:58.422Z (11 days ago)
• Topics: accelerated-computing, cuda, cuda-programming, jit, numba, nvidia, python
• Homepage:
• Size: 4.41 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
![Course](images/banner.png)

## Table of Contents

1. [Description](#description)

2. [Information](#information)

3. [Certificate](#certificate)



## Description

This course explores how to use Numba—the just-in-time, type-specializing Python function compiler—to accelerate Python programs to run on massively parallel NVIDIA GPUs. You’ll learn how to: · Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs) · Use Numba to create and launch custom CUDA kernels · Apply key GPU memory management techniques Upon completion, you’ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.



## Information

At the conclusion of the workshop, you’ll have an understanding of the fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba:

> - GPU-accelerate NumPy ufuncs with a few lines of code.

> - Configure code parallelization using the CUDA thread hierarchy.

> - Write custom CUDA device kernels for maximum performance and flexibility.

> - Use memory coalescing and on-device shared memory to increase CUDA kernel bandwidth.

More detailed information and links for the course can be found on the [course website](https://www.nvidia.com/en-in/training/instructor-led-workshops/fundamentals-of-accelerated-computing-with-cuda-python/).



## Certificate

The certificate for the course can be found below:

- ["Fundamentals of Accelerated Computing with CUDA Python" - NVIDIA Deep Learning Institute]() (Issued On: November 2024)