In this tutorial we'll be showing you how to visualise HEALPix results using Jupyter Notebook in our OnDemand appliance on the Apocrita HPC cluster. We'll start with installing the required Python packages before demonstrating how to run the Healpy tutorial.
Information about running other components of HEALPix not covered in this tutorial can be found on our docs site.
Hello! I am Sherman and I have just joined the RSE team at Queen Mary. Glad to meet you all!
My background is in computational statistics and machine learning. I have completed projects in rainfall prediction, defect detection for 3D printing and Markov chains using Monte Carlo. These projects involved collaboration with various scientists, such as meteorologists, engineers and statisticians.
An understanding of file permissions is important to the success of computational jobs, and the security of your files.
The default settings are suitable for some, but not every use-case: without sufficient awareness, your files may be visible to people who should not be able to access them, and vice-versa.
Since the last module update in December 2019, we have:
Over the past year, researchers from QMUL's William Harvey Research Institute (WHRI) have engaged on a collaborative code review club. Through this collaborative effort the group aims to peer review the computational components of their research and provide code quality assurance to all involved researchers. Additionally, the Research Software Engineering group of ITS Research has been assisting the group with knowledge transfer and by participating in the review process.
Research Software London is a community to support the use and development of research software in London and the South East. Since 2019, RSLondon has run a number of Software Carpentry workshops to teach introductory computing skills to researchers. ITSR have been involved in these efforts, providing instructors and helpers at each of these workshops.
When it comes to picking a distribution, Python programmers are spoilt for choice. We're going to compare two of the most popular (CPython and Anaconda) and one that promises big performance improvements with relatively little hassle (Intel Distribution for Python).
In this tutorial we'll be showing you how to run a TensorFlow job using the GPU nodes on the Apocrita HPC cluster. We will expand upon the essentials provided on the QMUL HPC docs site, and provide more explanation of the process. We'll start with software installation before demonstrating a simple task and a more complex real-world example that you can adapt for your own jobs, along with tips on how to check if the GPU is being used.
Jigsaw puzzles proved wildly popular during lockdown, but they weren't all done on the dining room table on rainy afternoons. The puzzle faced by researchers from the School of English and Drama (SED), lead by Dr Richard Coulton and in collaboration with the Natural History Museum, was to piece together a set of beautiful botanical watercolours brought back from China by the East India Company surgeon James Cuninghame. Cuninghame purchased these works, by an unknown local artist, in Xiamen in 1699. Sometime in the first half of the eighteenth century, perhaps because of their large size, these watercolours were cut up and glued into what you ungenerously, call a scrap book. The British Library has lovingly digitised this book in a series of publicly-available high resolution images funded by Oak Spring Garden Foundation, who also sponsored the current project.
On Apocrita we can use OpenMP to execute code on GPU devices. This post looks at how to compile such programs and submit them to run on the GPU nodes. The post assumes that you have code, already developed and tested, which is ready for deployment, and that you have been granted access to the GPU nodes.