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NHR@FAU

  1. Home
  2. Teaching & Training
  3. Tutorials & Courses
  4. Choosing GPU Programming Approaches

Choosing GPU Programming Approaches

In page navigation: Teaching & Training
  • Tutorials & Courses
    • Accelerating CUDA C++ Applications with Multiple GPUs
    • C++ for Beginners
    • Choosing GPU Programming Approaches
    • Core-Level Performance Engineering
    • From Zero to Multi-Node GPU Programming
    • Fundamentals of Accelerated Computing with CUDA C/C++
    • Fundamentals of Accelerated Computing with CUDA Python
    • Fundamentals of Accelerated Computing with Modern CUDA C++
    • Fundamentals of Accelerated Computing with OpenACC
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    • Hybrid Programming in HPC - MPI+X
    • Introduction to OpenMP
    • Introduction to the LIKWID Tool Suite
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Choosing GPU Programming Approaches

This course provides an overview of the most common GPU programming approaches, including CUDA/ HIP, SYCL, modern C++, Thrust, OpenACC, OpenMP and Kokkos. It helps participants understand the strengths and weaknesses of each approach, enabling them to make informed decisions about which one to use for their specific applications.

Participants will get the most out of this course if they have already have prior experience in at least one GPU programming approach, but participation without any prior knowledge is also possible.

Prerequisites

Participants should meet the following requirements:

  • Familiarity with (modern) C++ programming

Upcoming Iterations and Additional Courses

You can find dates and registration links for this and other upcoming NHR@FAU courses at https://hpc.fau.de/teaching/tutorials-and-courses/.

Erlangen National High Performance Computing Center (NHR@FAU)
Martensstraße 1
91058 Erlangen
Germany
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