GPU Programming Workshop

Overview

In this 4-days online workshop you will learn how to accelerate your applications with OpenACC, CUDA C++ and CUDA Python on NVIDIA GPUs.

The lectures are interleaved with many hands-on sessions using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the cloud.

The workshop is co-organised by Leibniz Supercomputing Centre (LRZ), Erlangen National High Performance Computing Center (NHR@FAU) and NVIDIA Deep Learning Institute (DLI). NVIDIA DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning.

Day 1 (OpenACC) is offered outside of the NVIDIA DLI programme.

All instructors are NVIDIA certified University Ambassadors.

Agenda

1st day: Fundamentals of Accelerated Computing with OpenACC

Learn the basics of OpenACC, a high-level programming language for programming on GPUs. This lecture is for anyone with some C/C++ of Fortran experience who is interested in accelerating the performance of their applications beyond the limits of CPU-only programming. In this lecture, you’ll learn:

• How to profile and optimise your CPU-only applications to identify hot spots for acceleration
• How to use OpenACC directives to GPU accelerate your codebase
• How to optimise data movement between the CPU and GPU accelerator

Upon completion, you'll be ready to use OpenACC to GPU accelerate CPU-only applications.

 

2nd day: Fundamentals of Accelerated Computing with Modern CUDA C++

This lecture provides a comprehensive introduction to general-purpose GPU programming with CUDA. You'll learn how to write, compile, and run GPU-accelerated code, leverage CUDA core libraries to harness the power of massive parallelism provided by modern GPU accelerators, optimize memory migration between CPU and GPU, and implement your own algorithms. At the end of the lecture, you'll have access to additional resources to create your own GPU-accelerated applications.

 

3rd day: Fundamentals of Accelerated Computing with CUDA Python

This lecture explores how to use Numba — the just-in-time, type-specialising 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.

 

4th day: Q&A Session

Unfortunately, the DLI course "Accelerating CUDA C++ Applications with Multiple GPUs" originally scheduled for Day 4, has been suspended by NVIDIA.
Instead, we will offer a Q&A session on GPU programming with CUDA C++, CUDA Python, and OpenACC.

Important information

After you are accepted, please create an account under https://learn.nvidia.com/join

Ensure your laptop / PC will run smoothly by going to http://websocketstest.com/
Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80).
If there are issues with WebSockets, try updating your browser.

NVIDIA Deep Learning Institute

The NVIDIA Deep Learning Institute delivers hands-on training for developers, data scientists, and engineers. The program is designed to help you get started with training, optimising, and deploying neural networks to solve real-world problems across diverse industries such as self-driving cars, healthcare, online services, and robotics.

Prerequisites

Day 1:

• Basic C/C++ or Fortran competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations.
• No previous knowledge of GPU programming is assumed.

Day 2: 

• Basic C++ competency, including familiarity with lambda expressions, loops, conditional statements, functions, standard algorithms and containers.
• No previous knowledge of CUDA programming is assumed.

Day 3:

• Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations.
• NumPy competency, including the use of ndarrays and ufuncs.
• No previous knowledge of CUDA programming is required.

Hands-On

The lectures are interleaved with many hands-on sessions using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the cloud.

Language

English

Lecturer

Dr. Momme Allalen (LRZ), Dr. Sebastian Kuckuk (NHR@FAU), Dr. Volker Weinberg (LRZ)

All instructors are NVIDIA certified University Ambassadors.

Prices and Eligibility

The course is open and free of charge for academic participants from the Member States of the European Union (EU) and Associated Countries to the Horizon 2020 programme.

Registration

Please register with your official e-mail address to prove your affiliation.

Withdrawal Policy

See Withdrawal

Legal Notices

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Online Course	GPU Programming Workshop
Number	hdli1w25
Available places	2
Date	27.10.2025 – 30.10.2025
Price	EUR 0.00
Location	ONLINE
Room
Registration deadline	20.10.2025 23:59
E-mail	[email protected]