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Welcome to the QMUL HPC blog

  • in rse
  • 22 min read

Marimo: The Antidote to Jupyter Notebooks

Don't use a notebook to do an HPC job-script's job, unless your notebook is also a job-script. Marimo, an alternative to Jupyter, enables this, and also helps mitigate some other common criticisms of notebooks. Through the magic of web-assembly, it even allows your Python code to live rent-free in someone else's web browser. Using Lyapunov fractals as a somewhat psychedelic example, we might as well experiment with multiprocessing and shared memory along the way.

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  • 21 min read

Modernising Rcpp and C++ Code With std::span

The programming language C++ is still widely used today, especially for high-performance computing. Out-of-date practices from the 80s and 90s should still work now because the language is designed to be highly backwards compatible. As C++ evolved, there were numerous efforts to modernise the language and update programming practices with new features to improve the C++ experience, such as memory-safety features.

  • in news
  • 4 min read

Queue scheduler announcement

Updated March 2026

We have updated this post with answers to some common questions.

For the lifetime of the Apocrita HPC cluster, the queue scheduler software we have been using to allocate jobs to individual compute nodes has been a variant of Grid Engine. Over the past few years, the company who own this software has changed hands a few times, and we feel that development has stagnated in terms of features, while bugs have not been resolved to our satisfaction.

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  • 3 min read

Virtual vs physical memory

Memory usage can be broadly simplified into two values: Virtual memory (VMEM) which a program believes it has, and Physical memory (also known as "Resident Set Size" or RSS for short), which is the actual amount of memory it uses.

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  • 25 min read

Poisson-Icing 🐟❄️ - Gibbs Sampling with a GPU using CuPy

Hybrid programming allows you to program the majority of your software in your favourite language but performance-critical parts in a faster language. With the Python package CuPy, you can program CPU code in Python and custom GPU kernel functions in CUDA. Thus, we can design our software with a familiar Python interface but run faster GPU code under the hood.

CuPy also has GPU versions of existing NumPy functions which may help transition your CPU code to the GPU without modifying your code too much. This may also help structure your GPU code with familiar NumPy functions, making it readable to many Python users.

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  • 19 min read

Python GPU Programming with Numba and CuPy

In a previous blog, we looked at using Numba to speed up Python code by using a just-in-time (JIT) compiler and multiple cores. The speed-up is remarkable with small changes to the existing code.

In this blog post, we will continue exploring the Numba ecosystem and implement the Gauss map on the GPU, gaining further speed up while still writing Python code. We will also look at CuPy which is another way to write and run GPU code. Instead of being Pythonic, it allows hybrid programming where the GPU code is written in CUDA but executed in Python.

One key advantage of hybrid programming is writing software in your preferred language while optimising performance-critical sections in a faster language - the best of both worlds!

  • in news
  • 2 min read

Rocky 9 benefits

The majority of the cluster has now been upgraded to Rocky 9 and the remaining CentOS 7 nodes will be updated in due course. There may be some users that are still hesitant to move over, but there are a few reasons why you should.