GPU Programming: When, Why and How?
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.
- Why GPUs?
- The GPU hardware and software ecosystem
- What problems fit to GPU?
- GPU programming concepts
- Introduction to GPU programming models
- High-level language support
- Directive-based models
- Multiple GPU programming with MPI
- Non-portable kernel-based models
- Portable kernel-based models
- Preparing code for GPU porting
- GPU programming example: stencil computation
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:
Links to additional resources and tutorials can be found in the lesson episodes.
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