A university near me must be going through a hardware refresh, because they’ve recently been auctioning off a bunch of ~5 year old desktops at extremely low prices. The only problem is that you can’t buy just one or two. All the auction lots are batches of 10-30 units.

It got me wondering if I could buy a bunch of machines and set them up as a distributed computing cluster, sort of a poor man’s version of the way modern supercomputers are built. A little research revealed that this is far from a new idea. The first ever really successful distributed computing cluster (called Beowulf) was built by a team at NASA in 1994 using off the shelf PCs instead of the expensive custom hardware being used by other super computing projects at the time. It was also a watershed moment for Linux, then only a few yeas old, which was used to run Beowulf.

Unfortunately, a cluster like this seems less practical for a homelab than I had hoped. I initially imagined that there would be some kind of abstraction layer allowing any application to run across all computers on the cluster in the same way that it might scale to consume as many threads and cores as are available on a CPU. After some more research I’ve concluded that this is not the case. The only programs that can really take advantage of distributed computing seem to be ones specifically designed for it. Most of these fall broadly into two categories: expensive enterprise software licensed to large companies, and bespoke programs written by academics for their own research.

So I’m curious what everyone else thinks about this. Have any of you built or admind a Beowulf cluster? Are there any useful applications that would make it worth building for the average user?

  • plenipotentprotogod@lemmy.worldOP
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    11 months ago

    This actually came up in my research. Folding@Home is considered a “grid computer” According to Wikipedia:

    Grid computing is distinguished from … cluster computing in that grid computers have each node set to perform a different task/application. Grid computers also tend to be more heterogeneous and geographically dispersed (thus not physically coupled) than cluster computers.

    The primary performance disadvantage is that the various processors and local storage areas do not have high-speed connections. This arrangement is thus well-suited to applications in which multiple parallel computations can take place independently, without the need to communicate intermediate results between processors.