why, when and how? documentation

Graphical processing units (GPUs) are the workhorse of many high performance
computing (HPC) systems around the world. The number of GPU-enabled supercomputers
on the Top500 has been steadily increasing in recent years
and this development is expected to continue. In the near future, the majority of HPC
computing power available to researchers and engineers is likely to be provided by GPUs
or other types of accelerators. Programming GPUs and other accelerators is thus crucial
to developers of software run on HPC systems.

However, the landscape of GPU hardware, software and programming environments is complicated.
Multiple vendors compete in the high-end GPU market, with each vendor providing its own software
stack and development toolkits, and even beyond that, there is a proliferation of tools,
languages and frameworks that can be used to write code for GPUs.
It can thus be difficult for individual developers and project owners to know how to
navigate across this landscape and select the most appropriate GPU programming framework for their
projects based on the requirements of a given project and technical requirements of any
existing code.

This material is meant to help both software developers and decision makers navigate the
GPU programming landscape and make more informed decisions on which languages or frameworks
to learn and use for their projects. Specifically, you will:

  • Understand why and when to use GPUs.

  • Become comfortable with key concepts in GPU programming.

  • Acquire a comprehensive overview of different software frameworks, what levels they operate at, and which to use when.

  • Learn the fundamentals in at least one framework to a level which will enable you to quickly become a productive GPU programmer.


Familiarity with one or more programming languages like C/C++, Fortran, Python or
Julia is recommended.

Who is the course for?

This material is most relevant to researchers and engineers who already develop software
which runs on CPUs in workstations or supercomputers, but also to decision makers or
project managers who don’t write code but make strategic decisions in software projects,
whether it’s in academia, industry or the public sector.

About the course

This training material is the result of a multilateral effort by GPU programming experts from:

See also

Links to additional resources and tutorials can be found in the lesson episodes.


Several sections in this lesson have been adapted from the following sources created by
ENCCS and CSC, which are
all distributed under a
Creative Commons Attribution license (CC-BY-4.0):

The lesson file structure and browsing layout is inspired by and derived from
work by CodeRefinery licensed under the MIT license. We have copied and adapted
most of their license text.

Instructional Material

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Creative Commons Attribution license (CC-BY-4.0).
The following is a human-readable summary of (and not a substitute for) the
full legal text of the CC-BY-4.0 license.
You are free to:

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  • No additional restrictions – You may not apply legal terms or
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With the understanding that:

  • You do not have to comply with the license for elements of the material in
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Except where otherwise noted, the example programs and other software provided
with this repository are made available under the OSI-approved
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