Why not a K8s cluster? Much more appropriate for modern software.

Beowulf cluster. Beowulf. Now that's a name I've not heard in a long time. A long time.

The main issue I would see impeding this is power draw and heat. Unless you are rigged up to run this many machines (including appropriate UPSes) you may run into blowing fuses and needing almost as much power for air conditioning as you need to run the cluster.

If you had like 50 SoCs maybe, because the power draw and heat might be manageable. Something like a RaspberryPi or OrangePi.

Yes. I'm actually doing so right now at work, and run multiple Beowulf clusters for a research institution. You don't need or want this.

In a real cluster you would use software like Slurm or PBS to submit jobs to the cluster and have them execute on your compute nodes as resources are available to keep utilisation high.

It makes no sense for the home environment unless you're trying to run some serious computations and if you have a need to do that for work or study then you probably have access to a real HPC.

It might be interesting and fun, but not particularly useful. Maybe a fun HCI setup would be more appropriate to enable you to scale VMS across hosts and get some redundancy.

Docker or Kubernetes work well on a cluster. Before containers this was a lot more work to set up, but these days you just need to image them all, put them on the network, and then use some kind of container orchestration to send them containers/pods.

It really depends on what sort of workload you want to run. Most programs have no concept of horizontal scaling like that, and those that do usually deal with it by just running an instance on each machine.

That said, if you want to run lots of different workloads at the same time, you might want to have a look at something like Kubernetes. I'm not sure what you'd want to run in a homelab that would use even 10 machines, but it could be fun to find out.

The only practical purpose of such a cluster is educational purposes. Clusters as a whole are only useful for academics and researchers, so once you get into clusters using old hardware you're essentially only building the cluster for the sake of the challenge.

There are aseveral options, although some may be defunct.

Last time I looked into this, openMosix was the most interesting, affordable, general-purpose option. It turned several computers into one big virtual computer. I ran a very small, 3-node cluster for a time. The upside was that you could run almost anything on it - unlike most HPC solutions, it didn't require bespoke languages, libraries, or targetted solutions. The downside was performance; it turns out that to really take adventage of HPC, you really need to program for it. OpenMosix looks defunct now.

OpenPMIx looks to have taken up the torch from OpenMosix. It looks active; I have no specific knowledge about it.

tldp.org has some good required reading before you invest in this, in particular discussing the elephant in the room, networking latency. The short version is that, no matter how slow your computers, the bottleneck will still be the network. Unless you're willing to invest a lot into fiber and expensive, fast switches, it's probably not worth it.

slurm crosses the line into modern cluster job management, like you might find in a cloud provider like AWS, which is tye direction the non-supercomputer industry took when commodity MPI turned out to be not feasible. Warewolf is another version, sort of one foot in distributed container management and lightweight MPI. Both are pretty involved, more Beowulf than OpenMosix.

tldr, it's probably not worth it if you're looking for a cheap Beowulf cluster, because such a thing doesn't exist in any practical sense. Cost, and physics, get in the way. If you want to set up a data center, or some job farm like AWS or GCS, that's another matter. But it's a far cry from MPI.

I did a whole ago just to see what it was all about. Then got rid of the setup a week later. It's a cool project but I needed the other boxes. If you need a huge amount of parallel operations (and want to self host) it's a decent option.

Look into BOINC. It's a free open source software for distributed computing ("map-reduce"-type problems). Runs on all platforms, handles computation at the petaflop scale. Large Hadron Collider (CERN) uses it to distribute computational work to volunteers. It's also a way you can contribute your computer's spare capacity to cancer research. !boinc@sopuli.xyz

Kubernetes / K8s / K3s.

If your trying to do compute / simulations on it, it depends on your workload... OpenMPI... ClusterKnoppix / LinuxPMI ...

K3s is great for lower power nodes in a cluster. Self hosting services for home is great use for such a cluster IMHO

A friend and I created one years ago when we were at university made with 6 machines. We were running MATLAB simulations that would take over a day to complete on i3/i5 CPUs. Fortunately MATLAB and the simulation add-on package had been programmed to parallelize jobs, which reduced the simulation time down to just a few hours. This was done in a Windows environment with dual core HP machines with every RAM slot filled.

I can't imagine homelab workloads benefitting from such a set up unless something like video/3D rendering can utilise it.

Someone with more expertise can correct me if I am wrong, but the last I heard about this, I heard that cluster computing was obsoleted by modern IaaS and cloud computing technology.

For example, the Xen project provides Unikernels as part of their Xen Cloud product. The unikernel is (as I understand it) basically a tiny guest operating system that statically links to a programming language runtime or virtual machine. So the Xen guest boots up a single executable program composed of the programming language runtime environment (like the Java virtual machine) statically linked to the unikernel, and then runs whatever high-level programming language that the virtual machine supports, like Java, C#, Python, Erlang, what have you.

The reason for this is if you skip running Linux altogether, even a tiny Linux build like Alpine, and just boot directly into the virtual machine process, this tends to be a lot more memory efficient, and so you can fit more processes into the memory of a single physical compute node. Microsoft Azure does something similar (I think).

To use it, basically you write a program a service in a programming language that runs on a VM and build it to run on a Xen unikernel. When you run the server, Xen allocates the computing resources for it and launches the executable program directly on the VM without an operating system, so the VM is, in effect, the operating system.

I tried migrating my personal services to Docker Swarm a while back. I have a Raspberry Pi as a 24/7 machine but some services could use a bit more power so I thought I'd try Swarm. The idea being that additional machines which are on sometimes could pick up some of the load.

Two weeks later I gave up and rolled everything back to running specific services or instances on specific machines. Making sure the right data is available on all machines all the time, plus the networking between dependencies and in some cases specifying which service should prefer which machine was far too complex and messy.

That said, if you want to learn Docker Swarm or Kubernetes and distributed filesystems, I can't think of a better way.

I'm not familiar with the concept. Would Folding@Home be considered a Beowulf Cluster?

"Beowulf cluster" sounds like what one would call a totally rad type of nebula grouping. 🤙