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Welcome to the QMUL HPC blog

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.

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.

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.

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:

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.

High Performance Computing for the Wolfson Institute Population Health

If you go to run every morning, or drive to work on weekdays, you should know that every journey is unique. For me, every High Performance Computing (HPC) workshop I deliver has its own personality. The audience, the material tailored to each audience, the interactions and questions, and of course, the energy of the community. Last Thursday September 26, an HPC workshop for the Wolfson Institute of Population Health was held from 2:00 p.m. to 5:00 p.m. The seminar includes, as usual, presentations, coffee break, quiz and treats, and the photographs to make it memorable.