Introduction to GPU programming

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 module 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.

Prerequisites

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

Episodes

• Why GPUs?
• The GPU hardware and software ecosystem
• What problems fit to GPU?
• GPU programming concepts
• Introduction to GPU programming models
• Quiz

Reference

• Instructor guide
• Reference for learners

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:

• Understand when and why to use GPUs

• Grasp core GPU programming concepts

• Navigate the GPU software ecosystem

• Evaluate and choose appropriate tools

Credit

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