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Welcome to the QMUL HPC blog

  • in rse
  • 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.

  • in rse
  • 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.

  • in rse
  • 3 min read

R on Rocky 9

With the major operating system upgrade from Centos 7 to Rocky 9, we want to ensure that using R, RStudio, and Open OnDemand (OOD) is as seamless as possible. This post will include new tips for a better experience, as well as a reiteration of the important or frequently forgotten old tips.

  • in tutorial
  • 8 min read

Managing Python environments

There are some important things Apocrita Python users need to know, such as how to manage and curate your personal environments, as well as how to tackle some common problems during the process.

  • in news
  • 4 min read

The next era for Apocrita is here

For much of the year we have been working on a major project to upgrade Apocrita to a new operating system, (Rocky Linux 9, hereafter known as Rocky 9). As part of the project, we have deployed a new package building tool to help us recompile all of the research applications to work on the new system.

The majority of the cluster has now been upgraded to Rocky 9. The remaining CentOS 7 nodes will be updated in due course.

  • in rse
  • 8 min read

A PyTorch DDP Case Study With ImageNet

In this blog post, we will play about with neural networks, on a dataset called ImageNet, to give some intuition on how these neural networks work. We will train them on Apocrita with DistributedDataParallel and show benchmarks to give you a guide on how many GPUs to use. This is a follow on from a previous blog post where we explained how to use DistributedDataParallel to speed up your neural network training with multiple GPUs.

  • in news
  • 4 min read

High Performance Computing (HPC) events from late 2024

2024 has been productive year in the outreach and education of HPC to different schools at Queen Mary University of London. We have formed alliances with different managers and PIs from various schools within the University who understand the value that HPC can add to their scientific research. We are pleased to share our latest event in 2024: