[Online] Fundamentals of Accelerated Computing with Modern CUDA C++

27 Oct 2026, 09:00 → 29 Oct 2026, 13:00 Europe/Berlin

Online

Fundamentals of Accelerated Computing with Modern CUDA C++

Schedule & Format

• Date: 2026, October 27-29
• Times:
  • Oct 27: 9:00 - 13:00 CE(S)T
  • Oct 28: 9:00 - 13:00 CE(S)T
  • Oct 29: 9:00 - 13:00 CE(S)T
• Format: Three half-days
• Location: Online
• Language: English

Registered participants will receive the video conferencing link via email on the day before the course.

Instructor

• Dr. Sebastian Kuckuk, NHR@FAU, certified NVIDIA DLI Ambassador

This course is organized by Erlangen National High Performance Computing Center (NHR@FAU) in collaboration with NVIDIA Deep Learning Institute (DLI).

Course Description

This course teaches GPU acceleration of C++ applications using CUDA, with an emphasis on modern C++ idioms rather than low-level GPU APIs. Starting from library-provided parallel algorithms that execute transparently on the GPU, it progresses through custom CUDA kernels, thread hierarchies, shared memory, and concurrent streams - covering the full range from high-level abstractions to fine-grained GPU control. No prior CUDA or GPU programming experience is required.

Further information about this tutorial can be found on the NVIDIA DLI course page.

Prerequisites

Knowledge

• C++ programming experience, including lambda expressions and standard library algorithms

Technical

• A free NVIDIA developer account
• A local installation of NVIDIA Nsight Systems is recommended

Course Structure

• GPU programming fundamentals: writing and launching CUDA-accelerated C++ code; applying parallel algorithms on the GPU
• Concurrency and profiling: CUDA streams, asynchronous data transfers, and code analysis with NVIDIA Nsight Systems
• Custom kernel development: thread hierarchies, shared memory, and cooperative parallel algorithms

Learning Outcomes

After completing this course, you will be able to:

• Accelerate C++ applications by writing, compiling, and running GPU code with CUDA
• Apply parallel algorithms to GPU workloads without writing custom kernels
• Manage CPU-GPU data movement and optimize memory access patterns
• Write custom CUDA kernels and manage thread hierarchies and shared memory
• Overlap computation with data transfers using concurrent CUDA streams
• Profile GPU code and identify performance bottlenecks with NVIDIA Nsight Systems

Registration, Wait List and Withdrawal Policy

Registration

Please register at the bottom of this page. Registration is open until a few days before the course starts, or until the course is fully booked.

Prices and Eligibility

This course is open and free of charge for participants affiliated with academic institutions in European Union (EU) member states and Horizon 2020-associated countries.

Wait List

If the course reaches its maximum capacity, you can request to join the wait list by sending an email to nhr-training@fau.de. Please include your name and university affiliation in the message.

Withdrawal Policy

Please only register if you are committed to attending the course. No-shows will be blacklisted and excluded from future events.

If you need to withdraw your registration, please either cancel it directly through the registration system or send an email to nhr-training@fau.de.

Additional Courses

You can find an up-to-date list of all courses offered by NHR@FAU at https://hpc.fau.de/teaching/tutorials-and-courses/.