What Is A Computer Cluster Or Cluster Computing?

“Computing power” and “compute” are becoming key features of the AI boom, both for tech enthusiasts and ordinary people alike. AI companies are building enormous data centres, buying thousands of GPUs and spending billions on infrastructure just to keep up with demand, and we’re all becoming acutely aware of this, as well as the effects this may have on the world and social issues too.

But, from a technical standpoint, one thing not everybody has a secure grip on is understanding how all those computers actually work together. Essentially, that’s where cluster computing comes in.

A computer cluster is essentially a group of connected computers that work together as though they were one system. Instead of asking a single machine to handle an enormous workload, the job can be distributed across multiple computers, or “nodes”, which makes the process quicker and more efficient.

On the surface, it sounds relatively simple, but this idea sits underneath a surprisingly large amount of modern computing.

What Is A Computer Cluster?

Put simply, a computer cluster is a collection of computers that are connected through a network and are configured to work together. Each individual computer is known as a node. Nodes have their own processors, memory, operating systems and other resources, but there’s underlying software that coordinates them so that they can collectively tackle workloads.

You can think of it kind of like a startup. No matter how competent they are, one person can only do so much. But, if you give the same job or task to a group of people with expertise across the board, they’ll be able to divide up the responsibilities and work between them, coordinate the team and ultimately, make the overall task significantly more manageable.

This is pretty much how a computer cluster works. Except in this case, the “employees” are really computers, and the clusters can include anything from three or four computers to hundreds, if not thousands, of nodes.

How Does Cluster Computing Work?

The computers in a cluster communicate over a network which normally uses a high-speed local area network (LAN). All this coordination is aided or facilitated by specialised software, sometimes called middleware, which also helps to distribute workloads between them.

A simple thought experiment can be really helpful to explain this concept. Imagine a company receives a million customer requests in a short period of time. Rather than sending every request to one server, a cluster could distribute those requests across dozens or hundreds of servers. That means that each node is able to handle a specific part of the workload, while the overall system presents itself to users as one service. It’s pretty much about dividing and conquering.

Now, this is particularly useful because computers can work in parallel. Instead of having Computer A being allocated task one, two, three and four, you could rather allocate Computers A, B, C and D with one task each. Once they’ve all been completed, they can be consolidated once again.

If done properly, this can dramatically increase the amount of work a system can handle, decreasing the workload and pressure on the system as a whole.

Why Do Businesses Use Computer Clusters?

The main reasons businesses choose to use computer clusters rather than a single system are to improve performance, scalability and overall reliability.

If tasks are divided up among several nodes, that means that the system is able to complete work far faster than a single computer would’ve been able to do, and at a higher quality. When it comes to things like really important scientific research, simulations, AI, machine learning and data analysis of large data sets, this can be incredibly beneficial.

And that leads to scale. If businesses now have the ability to deal with more tasks and complete them far more quickly than before, that means organisations can add more nods rather than replacing one machine with an increasingly powerful computer (that would also be far more expensive). This is called horizontal scaling. IBM explains that it’s possible to increase the resources of clusters by simply adding notes, thus allowing organisations to adjust their capacity in response to changes in demand.

This is helpful for startups that tend to start out with fairly modest infrastructure and aim to expand their computing capacity as their customer base and workloads grow.

Reliability is another significant selling point for cluster computing. By spreading the load across multiple computers (rather than putting everything on one computer), the failure of a single computer won’t take an entire service offline all at once. Rather, if one computer goes down, while it will still be inconvenient, it won’t crash the whole system all at once. Rather, one of the other nodes can take over in the meantime.

Different Types of Computer Clusters

There are different types of computer clusters, built for different purposes.

High-performance computing (HPC) clusters, on the one hand, are designed to tackle computationally intensive workloads. They’re used for things like scientific simulations, research and more often now than ever before, AI and machine learning.

High-availability clusters focus on keeping services running: if one node fails, another can take over.

Load-balancing clusters distribute incoming workloads between multiple servers, helping prevent one machine from becoming overwhelmed.

It’s also possible for there to be combinations of these approaches, depending on what an organisation is trying to achieve.

Are Cluster Computing and Cloud Computing?

The two terms are often confused, sometimes even erroneously used interchangeably, but they’re not the same. Cluster computing is all about connecting computers together so that they can work together as a coordinated system, while cloud computing is more broad. The latter describes a process in which computing resources like servers, storage and software are provided on demand, normally through the internet.

These two can certainly overlap. For instance, cloud providers can make use of clusters behind the scenes in order to deliver computing services to clients. Meanwhile, organisations can also build and manage their own clusters.

So, they’re not the same thing at all, but essentially, clusters are an important part of the infrastructure that makes modern cloud computing possible.

Cluster Computing Is An Important Part of Modern AI

Training and running advanced AI models can require enormous amounts of computing power. Instead of relying on one super-powerful machine, organisations can connect large numbers of processors and GPUs into clusters capable of handling huge workloads.

That means when you hear about AI companies building massive computing infrastructure, there’s a good chance you’re also hearing about clusters – even if the word itself isn’t being used. The basic idea is surprisingly old (like many terms in the AI world, actually), but its importance is growing as computing workloads become bigger and more demanding.

In other words, cluster computing is essentially about getting computers to stop working alone and start working as a team. And as AI, cloud computing and data-intensive startups continue to grow, that team is only getting bigger.