Overview

Welcome to Many Ways to GPU Programming

In this course, you will use a number of different approaches to accelerate an example molecular dynamics application on GPU hardware. The aim is to provide you with a practical understanding of the differences between each approach, the trade-offs involved, and even if multiple approaches might be used in a single codebase.

This course is delivered through a combination of presentations and reinforcement activities, like Multiple Choice Questions and hands-on exercises using Jupyter Notebooks.

The estimated time to complete this course is 4.5 hours.

What you will learn:

• Understand the differences between CPUs and GPUs.
• Explore the fundamentals of GPU-accelerated computing.
• Use NVIDIA Nsight Systems to profile and analyse applications.
• Write basic programs in C++, C, or Fortran.
• Apply OpenACC directives within C/C++ and Fortran applications.
• Use OpenMP directives for GPU-enabled programming.
• Develop and run CUDA kernels within C/C++ or Fortran applications.
• Explore approaches for optimising code for GPU architectures.

Prerequisites:

• Knowledge of a language like C, C++, or Fortran.