Directive based models

This course focuses on high-level GPU programming using directive-based approaches, where the goal is to make GPU computing more accessible compared to low-level kernel programming.

Instead of writing GPU kernels manually, students learn how to use compiler directives to parallelize code and offload computations to GPUs. The course emphasizes both ease of development and performance awareness, showing how abstraction can be used effectively without losing efficiency.

Prerequisites

  • Familiarity with one or more programming languages like C/C++ or Fortran is recommended

  • Basic understanding of parallel computing concepts

Episodes

Reference

Learning outcomes

This material is for all researchers and engineers who work with large or small datasets and who want to learn powerful tools and best practices for writing more performant, parallelised, robust and reproducible data analysis pipelines.

By the end of this module, learners should:

  • Incrementally transform sequential programs into GPU-accelerated versions using directives

  • Understanding how directives translate to GPU execution

Credit

Don’t forget to check out additional course materials from XXX. Please contact us if you want to reuse these course materials in your teaching. You can also join the XXX channel to share your experience and get more help from the community.

License

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MIT License

Copyright (c) 2025, EVITA project, Ashwin Vishnu Mohanan, Claudia Blaas-Schenner, Jaison Lewis, Jasper Seehofer, Kevin Lüdemann, Kjartan Thor Wikfeldt, Mary Kate Chessey, Matthias Eulert, Tobias Haas, Victoria Döller, Yinyin Ma, and Yonglei Wang

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