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Welcome to the QMUL HPC blog

  • 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:

  • in rse
  • 10 min read

Unification of Memory on the Grace Hopper Nodes

The delivery of new GPUs for research is continuing, most notable is the new Isambard-AI cluster at Bristol. As new cutting-edge GPUs are released, software engineers are tasked with being made aware of the new architectures and features these new GPUs offer.

The new Grace-Hopper GH200 nodes, as announced in a previous blog post, consist of a 72-core NVIDIA Grace CPU and an H100 Tensor Core GPU. One of the key innovations is the NVIDIA NVLink Chip-2-Chip (C2C) and unified memory, which allows fast and seamless automation of transferring data from CPU to GPU. It also allows the GPU to be oversubscribed, allowing it to handle data much larger than it can host, potentially tackling out-of-GPU memory problems. This allows software engineers to focus on implementing algorithms without having to think too much about memory management.

This blog post will demonstrate manual GPU memory management and introduce managed and unified memory with simple examples to illustrate its benefits. We'll try and keep this to an introductory level but the blog does assume basic knowledge of C++, CUDA and compiling with nvcc.

  • in rse
  • 7 min read

A Slight Case of Overthreading

We still encounter jobs on the HPC cluster that try to use all the cores on the node on which they're running, regardless of how many cores they requested, leading to node alarms. Sometimes, jobs try to use exactly twice or one-and-a-half the allocated cores, or even that number squared. This was a little perplexing at first. In your enthusiasm to parallelize your code, make sure someone else hasn't already done so.