December 02 - 03, 2024
9:30 - 17:00 CET
Instructors: Alessio Sclocco, Johan Hidding, Laurent Soucasse
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The eScience Center offers a range of workshops and training courses, aimed at PhD candidates and other researchers or research software engineers. We organize workshops covering digital skills needed to put reproducible research into practice. These include online collaboration, reproducible code and good programming practices. We also offer more advanced workshops such as GPU Programming, Parallel Programming, Image Processing and Deep Learning.
From deep learning to high-performance computing, Graphics Processing Units (GPUs) are nowadays an important tool for scholars and research software engineers alike. Parallel in nature, they offer incredible computing capabilities that just a few years ago were only available in supercomputers. While using GPUs to accelerate computation becomes easier year after year, obtaining high performance from these devices still requires some knowledge of how they work, and the programming model on which they are based.
In this workshop we will provide the learners with the fundamental knowledge that they need to start their journey into the world of programming GPUs. After a brief introduction to the specificities of GPUs, and how they differ from traditional processors, participants will experience various ways of using them with Python. They will get familiar with libraries such as CuPy and Numba to accelerate Python code, and have first-hand experience in writing small CUDA programs that can run directly on the GPU.
The participant should:
Preferred:
Where: Science Park 402, 1098 XH Amsterdam. Get directions with OpenStreetMap or Google Maps.
When: December 02 - 03, 2024, 9:30 - 17:00 CET.
Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).
Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:
Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.
Workshop files: You will find all slides, notebooks, archived collaborative documents, and other relevant files in the files folder of the workshop website repository after the workshop.
Contact: Please email or training@esciencecenter.nl for more information.
Participants are expected to follow these guidelines:
| 09:30 | Welcome and icebreaker |
| 09:45 | Introduction |
| 10:00 | Convolve an image with a kernel on a GPU using CuPy |
| 10:30 | Coffee break |
| 10:45 | Running CPU/GPU agnostic code using CuPy |
| 11:15 | Coffee break |
| 11:30 | Image processing example with CuPy |
| 12:00 | Lunch break |
| 13:00 | Image processing example with CuPy |
| 14:00 | Coffee break |
| 14:15 | Run your Python code on a GPU using Numba |
| 15:00 | Coffee break |
| 15:15 | Run your Python code on a GPU using Numba |
| 16:15 | Wrap-up |
| 16:30 | END |
| 09:30 | Welcome and icebreaker |
| 09:45 | Introduction to CUDA |
| 10:30 | Coffee break |
| 10:45 | CUDA memories and their use |
| 11:15 | Coffee break |
| 11:30 | CUDA memories and their use |
| 12:00 | Lunch break |
| 13:00 | Data sharing and synchronization |
| 14:00 | Coffee break |
| 14:15 | Data sharing and synchronization |
| 15:00 | Coffee break |
| 15:15 | Concurrent access to the GPU |
| 16:15 | Wrap-up |
| 16:30 | Drinks |
All times in the schedule are in the CET timezone.
To participate in this workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.
We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.
We will teach GPU programming using the Jupyter Notebook, a programming environment that runs in a web browser. For this to work you will need a reasonably up-to-date browser. The current versions of the Chrome, Safari and Firefox browsers are all supported (some older browsers, including Internet Explorer version 9 and below, are not).
In particular, for this workshop we are using the infrastructure of JupyterHub for Education kindly provided by SURF in the framework of the EuroCC project. Google Colab may be used as an alternative.