GPU-Accelerated Computing with Python
October 18, 2022
1:00 PM - 4:00 PM
Description
In this workshop, you'll get hands-on experience accelerating Python codes with NVIDIA GPUs. We will utilize code samples in three main categories to introduce you to Python GPU accelerated computing. First, we will explore drop-in replacements for SciPy and NumPy code through the CuPy library. Next we’ll cover NVIDIA RAPIDS, which provides GPU acceleration for end-to-end data science workloads. Finally we'll cover Numba, which gives you the flexibility to write custom accelerated code without leaving the Python language. We'll finish with an end-to-end example that incorporates all the tools introduced to tackle a geospatial problem. By the end of the workshop, you'll have the skills to start accelerating your own Python codes with NVIDIA GPUs!
Learning Objectives
By participating in this workshop, you’ll learn how to:
- Learn to accelerate existing Python applications on GPU
- Introduce CuPy and RAPIDS for drop-in GPU-accelerated replacements for Numpy and Scikit learn
- Introduce Numba for writing custom GPU accelerated functions
Prerequisites Basic familiarity with Python
- Familiarity with Numpy, and/or Scikit-learn is beneficial
Tools, Libraries, and Frameworks Used
- Numba
- CuPy
- RAPIDS
Course Length: 3 hours
This workshop speaker will be Kristopher Keipert, senior solutions architect at NVIDIA.
Author(s): Kristopher Keipert and Zoe Ryan
Registration
Click here to register. Registrations for this training ends on October 10, 2022.
Date posted
Sep 1, 2022
Date updated
Sep 12, 2022