In this session we will covers some of the most popular and effective GPU accelerated libraries that give high performance without the requirement of writing your own custom GPU code. We will cover CUDA-X which has libraries for math, image/video processing, deep learning, and GPU tailored partner libraries. On top of CUDA-X we will cover RAPIDS which will target data science and data analytics workloads. We will conclude the session with interactive coverage of NVIDIAs profiling tools. These tools give us the opportunity to pinpoint bottlenecks in our workloads and ensure we are using the GPU to its full potential. By the end of the workshop, you'll have the skills to utilize existing GPU accelerated libraries and optimize your own codes NVIDIA GPUs!

Learning Objectives:

Introduce RAPIDS and CUDA-X for drop-in GPU-accelerated libraries
Introduce Nsight Systems and Compute for profiling and optimizing code 
 
Tools, Libraries, and Frameworks Used:

RAPIDS
CUDA-X
Nsight Systems
Nsight Compute