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

NHR Workshop (Online)

Monday, March 09^th 2026, 9 am - 5 pm

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.

Useful Information

Course language: English
Target group: HPC Dev

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