Oct 29, 2024 09:00 AM to Nov 01, 2024 01:00 PM
(Europe/Berlin / UTC100)



Contact Name

Contact Phone

+49 2461/61-1825

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GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GPUs offers high application performance by offloading compute-intensive portions of the code to the GPU. The course will cover basic aspects of GPU architectures and programming. Focus is on the usage of the directive-based OpenACC programming model which allows for portable application development. Examples of increasing complexity will be used to demonstrate optimization and tuning of scientific applications.

Topics covered will include:

  • Introduction to GPU/Parallel computing
  • Programming model OpenACC
  • Interoperability of OpenACC with GPU libraries (like cuBLAS and cuFFT) and CUDA
  • Multi-GPU Programming with MPI and OpenACC
  • Tools for debugging and profiling
  • Performance optimization

The course consists of lectures and interactive hands-on sessions in C or Fortran (the attendee’s choice).

Contents level

in hours

in %

Beginner's contents:


0 %

Intermediate contents:


50 %

Advanced contents:


50 %

Community-targeted contents:


0 %

If you are interested in GPU Programming with OpenACC, please also have a look at the respective modules of the CUDA Course at the beginning of the year:
GPU Programming with CUDA - Basics
GPU Programming - Advanced Topics


Some knowledge about Linux, e.g. make, command line editor, Linux shell (see for instance this overview), some experience in C

Target group:

Scientists who want to use GPU systems with OpenACC