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

Wednesday 12 Mar 2025, 09:00 → 17:30 Europe/Berlin

Date and Time

The course will be held online on March 12 from 9:00 a.m. to 5:30 p.m. (CET).

Registered participants will receive the BBB participation link via email the day before the course begins.

 

This course is part one of the three-event series, "From Zero to Multi-Node GPU Programming". Please register individually for each day you wish to attend:

• Part 1: Fundamentals of Accelerated Computing with CUDA C/C++ (March 12)
• Part 2: Accelerating CUDA C++ Applications with Multiple GPUs (March 19)
• Part 3: Scaling CUDA C++ Applications to Multiple Nodes (March 26)

 

Prerequisites

A free NVIDIA developer account is required to access the course material. Please register before the training at https://learn.nvidia.com/join.

Participants should additionally meet the following requirements:

• 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, participants will have a solid understanding of the fundamental tools and techniques for GPU-accelerating C/C++ applications with CUDA. Participants will be able to:

• Write code that can be executed by a GPU accelerator
• Identify and express data and instruction-level parallelism in C/C++ applications using CUDA
• Utilize CUDA-managed memory and optimize memory migration through asynchronous prefetching
• Use command-line and visual profilers to guide optimization efforts
• Leverage concurrent streams to achieve instruction-level parallelism
• Write GPU-accelerated CUDA C/C++ applications or refactor existing CPU-only applications using a profile-driven approach

 

Course Structure

Accelerating Applications with CUDA C/C++

• Writing, compiling, and running GPU code
• Controlling the parallel thread hierarchy
• Allocating and freeing memory for the GPU

Managing Accelerated Application Memory with CUDA C/C++

• Profiling CUDA code with the command-line profiler
• Details on unified memory
• Optimizing unified memory management

Asynchronous Streaming and Visual Profiling for Accelerated Applications with CUDA C/C++

• Profiling CUDA code with NVIDIA Nsight Systems
• Using concurrent CUDA streams

 

Certification

Upon successfully completing the course assessments, participants will receive an NVIDIA DLI Certificate, recognizing their subject matter expertise and supporting their professional career growth.

 

Language

The course will be conducted in English.

 

Instructors

Markus Velten, Dr. Sebastian Kuckuk, both certified NVIDIA DLI Ambassadors.

The course is co-organised by NHR@FAU, NHR@TUD and the NVIDIA Deep Learning Institute (DLI).

 

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.

 

Withdrawal Policy

We kindly ask participants to register only if they 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 sebastian.kuckuk@fau.de .

 

Wait List

If the course reaches its maximum capacity, you can request to join the waitlist by sending an email to sebastian.kuckuk@fau.de . Please include your name and university affiliation in the message.

 

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/ and by NHR@TUD at https://tu-dresden.de/zih/hochleistungsrechnen/nhr-training .