The High Performance Computing (HPC) team organised an event to celebrate February's bonus day this year. The goal was to introduce the HPC team members to the research community at QMUL, and to have the opportunity to ask the HPC expert in-person about any issue related to the performance of HPC jobs in Apocrita.
Here is a quick summary of what we covered in the session:
Whilst most Apocrita users will want to use the R module or RStudio via OnDemand for R workflows, it is also possible to use R inside of Anaconda.
In a previous blog, we discussed ways we could use multiprocessing and
mpi4py together to use multiple nodes of GPUs. We will cover some machine
learning principles and two examples of pleasingly parallel machine learning
problems. Also known as embarrassingly parallel problems, I rather call them
pleasingly because there isn't anything embarrassing when you design your
problem to be run in parallel. When doing so, you could launch very similar
functions to each GPU and collate their results when needed.
NVIDIA recently announced the GH200 Grace Hopper Superchip which is a combined CPU+GPU with high memory bandwidth, designed for AI workloads. These will also feature in the forthcoming Isambard AI National supercomputer. We were offered the chance to pick up a couple of these new servers for a very attractive launch price.
The CPU is a 72-core ARM-based Grace processor, which is connected to an H100 GPU via the NVIDIA chip-2-chip interconnect, which delivers 7x the bandwidth of PCIe Gen5, commonly found in our other GPU nodes. This effectively allows the GPU to seamlessly access the system memory. This datasheet contains further details.
Since this new chip offers a lot of potential for accelerating AI workloads, particularly for workloads requiring large amounts of GPU RAM or involving a lot of memory copying between the host and the GPU, we've been running a few tests to see how this compares with the alternatives.
Using multiple GPUs is one option to speed up your code. On Apocrita, we have V100, A100 and H100 GPUs available, with up to 4 GPUs per node. On other compute clusters, JADE2 has 8 V100 GPUs per node and Sulis has 3 A100 GPUs per node. If your problem is pleasingly parallel, you can distribute identical or similar tasks to each GPU on a node, or even on multiple nodes.
We held a 2-hour HPC workshop last Friday, December 15th. We arranged an agenda in coordination with the research student at QMUL, Peter Alexander Lock. It covered the generalities of Linux, accessing Apocrita, submitting jobs, and HPC commands.
Since the last module update in December 2022, we have:
On November 15th, our HPC team organised an event called “Let’s talk about Linux and HPC”, which focused on giving an overview of HPC at QMUL. The conference was open to the public and published on Eventbrite.
Approximately 30 people were in attendance between organisers and online or onsite attendees that came to our 2-hour event in the Engineering building at the Mile End campus. During the conference, attendees shared opinions, thoughts and suggestions for future workshops dealing with Linux, Ubuntu and setting up scripts in more detail. They were able to express themselves with the help of mentimeter.
The High Performance Computing (HPC) team is keen to spread Linux and HPC knowledge at Queen Mary University of London. Keeping in mind our vision to support excellence in research, our valuable efforts have been fruitful this year. Our achievements include:
We have deployed the latest version of Environment Modules (5.3.0) across the cluster on all frontend and compute nodes.