Living with Machines is a funded project at The Alan Turing Institute (aka the Turing), bringing together academics from different disciplines, to answer research questions such as how did historical newspapers tell the political landscape, how were accidents in factories reported, how did road and settlement names change, how did people change occupations during the industrial revolution...
There are many strategies and tools for improving the performance of Python code, for a comprehensive treatment see High Performance Python by Gorelick and Ozsvald (institutional access is available to QM staff). However, there are some subtleties when using them in an HPC environment. More bluntly, requesting processor cores does not automatically mean your code will use them effectively, and that cannot happen if it doesn't know how many of them there are!
As the complexity of HPC applications increases, the management of memory and threading scopes becomes increasingly important. Tools like Intel Inspector are crucial in this context, to effectively identify and resolve a wide array of memory errors and thread synchronisation issues.
The RSE team in ITS Research has had a busy few years since we started sharing our work in this blog. In this post we look at some highlights of recent activity and what we have to look forward to.
Polytomous variables can be used to model data that has two or more possible outcomes. For example, a survey with multiple-choice questions is polytomous. The R package, poLCA, does statistical clustering of polytomous variables. For example, grouping together survey results that are similar to each other.
Once we've written a program more advanced than our "Hello, world!" example, we're going to make mistakes. In this post, we'll look at how we can use the very compilers we're using to compile our program to pick up on some of these mistakes.
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.
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).