What is a cluster?

Overview

Teaching: 0 min
Exercises: 0 min

Questions

• What is a High Performance Computer (HPC)?

• What are the components of a HPC?

• What is a scheduler, and why do we need one?

Objectives

• Explain what a HPC is at a high level.

• Explain what a node and scheduler are in a HPC.

• Determine whether or not HPC is the right tool for your research.

What is a HPC?

The acronym HPC refers to a High Performance Computer - but what does that really mean? We will spend a little bit of time introducing some core language.

• Local workstation in this course will refer to the computer in front of you, whether that’s a desktop, laptop, tablet, or other device.
• Supercomputer refers to one particular type of high performance computer, which is composed of advanced processors. There was a time in computing when vector processing was being developed, making these processors much faster than their predecessors. CRAY-1 was the first supercomputer implementing these processors. This meant they could take a single task and complete it much faster than the average computer.
• Cluster also refers to a type of high performance computer. Over time, advanced processors have become available to all computers, so modern HPCs tend to be clusters rather than supercomputers. This means they’re composed of many smaller computers rather than advanced computers. They are powerful for solving problems that can be split into parts and distributed across the many computers that make up the cluster.

The HPC we assume you have access to in this course, MASSIVE M3, is a cluster. What this means in practice, is that not all problems are solved faster simply by running them on a HPC cluster - you have to think about how to adapt your problem. Don’t worry if you’re not sure what that looks like yet.

What are the components of a cluster?

Nodes

As explained above, a cluster is a collection of “computers”. A single computer or group of resources in a cluster is referred to as a node. For example, your local laptop might have 4 CPUs, 16GB of RAM, and no GPUs. We can imagine my laptop had identical resources. If we could somehow connect our laptops into one cluster, we could think of each individual laptop as a node.

Partitions

A group of nodes that have the same resources on them, and which can be accessed by the same group of people, is referred to as a partition. Both of our laptops have the same compute resources, and can be accessed by both of us, so they would make up a partition. We can give this partition a name, “no GPU laptops.” If a third person had a laptop with 16 CPUs, 4 GPUs, and 64GB of RAM, their laptop would be a new node, but it would belong to a different partition. We could call this “4 GPU laptops.”

Why do we need partitions?

Can you think of any reasons it might be helpful to have partitions in a cluster? Remember, a partition is just a group of nodes that share some properties.

Solution

Organising the cluster with partitions makes it possible to request particular resources based on partition. It also makes it possible to set certain rules for each partition - for example, you might want a partition that only allows code that runs for half an hour on it. Grouping those nodes into one partition makes it easier to organise the cluster for different purposes.

Shared filesystem

If I tried to connect our laptops like in our previous example it would be difficult for both of us to use them. On my laptop I would only have access to my files, and on your laptop you would only have access to your files. On a HPC however, we have a shared filesystem, meaning each node, regardless of partition, has access to the same files.

Sharing is caring on a HPC

Perhaps the idea of just connecting our laptops together has already gotten you asking questions about the challenges of a shared computing system. However, there are some good motivations for using a shared HPC, and some tools designed to make sharing much easier than it would be if we just connected out computers together.

One motivation for using a HPC is that shared hardware also means a shared pool of money for buying that hardware. Computers are expensive - if I only need a GPU sometimes, or only need large amounts of storage occassionally, it makes sense to split the cost of those resources across a group of people, and share the among them too.

Of course, when sharing a resource among many people, it’s important to make sure everyone gets fair access to those resources. For this reason, when using a HPC you will request access to the resources you need, and then a scheduler will decide who gets to use the resources next. This ensures if we all want to use the same resources at the same time, there’s a way to decide who gets to use it first, as the scheduler will create a queue. You can think of the scheduler playing a game of Tetris, where it’s trying to fit all the resource requests together in the best way possible. On M3, our scheduler is called SLURM.

How do we make scheduling fair?

Imagine you are the scheduler for a HPC and you have to decide how to order the resource requests. What things will help you make your decision? How can you make access to the resources fair?

Solution

There are lots of answers to this question. One thing to consider might be if everyone paid the same amount for the hardware. Another might be to make sure everyone gets access to the same amount of time using the HPC. Another thing to consider is the type of hardware being requested - if someone asks for 200GB RAM, is it worth the same as someone asking for 20GB RAM, or 500 GB of RAM? It’s hard work being a scheduler!

Key Points

• Most HPCs are not supercomputers, but clusters composed of many computers.

• Each computer in the HPC is a group of resources, called a node. Nodes with the same flavour of resources and access requirements are called partitions.

• A HPC is shared with other people, so it has a scheduler to ensure everyone has fair access.