Instructor-Led Workshop: Fundamentals of Accelerated Computing with CUDA C/C++

NHR Workshop (Online)

Montag, 09. März 2026, 9 - 17 Uhr

Speaker: Markus Velten

This workshop teaches the fundamental tools and techniques for accelerating C/C++ applications to run on massively parallel GPUs with CUDA^®. You’ll learn how to write code, configure code parallelization with CUDA, optimize memory migration between the CPU and GPU accelerator, and implement the workflow that you’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable massive performance gains. At the end of the workshop, you’ll have access to additional resources to create new GPU-accelerated applications on your own.

This course is part of the three-day series "From Zero to Multi-Node GPU Programming", which is offered in collaboration with NHR@FAU.

More details

Agenda (in development)

Accelerating Applications with CUDA C/C++
- Learn the essential syntax and concepts to be able to write GPU-enabled C/C++ applications with CUDA:
  - Write, compile, and run GPU code.
  - Control parallel thread hierarchy.
  - Allocate and free memory for the GPU.
Managing Accelerated Application Memory with CUDA C/C++
- Learn the command-line profiler and CUDA-managed memory, focusing on observation-driven application improvements and a deep understanding of managed memory behavior:
  - Profile CUDA code with the command-line profiler.
  - Go deep on unified memory.
  - Optimize unified memory management.
Asynchronous Streaming and Visual Profiling for Accelerated Applications with CUDA C/C++
- Identify opportunities for improved memory management and instruction-level parallelism:
  - Profile CUDA code with NVIDIA Nsight Systems.
  - Use concurrent CUDA streams.

Handouts

The course material (slides) will be made available to the class participants.

HPC-Certification Forum Links

Prerequisites

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

Learning Objectives

At the conclusion of the workshop, you’ll have an understanding of the fundamental tools and techniques for GPU-accelerating C/C++ applications with CUDA and be able to:

Write code to be executed by a GPU accelerator
Expose and express data and instruction-level parallelism in C/C++ applications using CUDA SD1.2.6 CUDA C/C++ Programming Fundamentals
Utilize CUDA-managed memory and optimize memory migration using asynchronous prefetching
Leverage command-line and visual profilers to guide your work PE2.3.7 NVIDIA Nsight Systems
Utilize concurrent streams for instruction-level parallelism
Write GPU-accelerated CUDA C/C++ applications, or refactor existing CPU-only applications, using a profile-driven approach

Hardware Requirements

Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud. A stable, reasonable broadband internet connection is required.

Registration

Link: https://indico.ecap.work/event/182/
The NHR workshop is limited to 60 participants.
You will receive the access data shortly before the event by email to your registered email address.

Further Information

Course language: English
Target group: HPC Dev

If you have any further questions, please contact Markus Velten (markus.velten@tu-dresden.de).