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have a few old laptops and some raspberrypi's and was wondering if it would be possible to cluster them together for general tasks/3D modeling?

im not asking if this is practical. im asking if its possible.

Ayden Quinn
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  • Not general purpose computing. If you have enough stuff to do in parellel and fine enough control of the application you might be able to write some custom scripts and outsource some processinf, but I imagine thats outside your definition of general tasks. – davidgo Jun 30 '23 at 04:50
  • @davidgo would there be a way to run different things on each computer and have them render on the host? like i have VSCode run on one node and Firefox on another. (just as an example) – Ayden Quinn Jun 30 '23 at 17:03
  • @AydenQuinn It depends on what software you're clustering and why (performance vs failover). For general tasks, you can always do remote desktop (or equivalent) in a window to another computer and run stuff there. There's all kinds of remoting tools, all the way up to stuff like Citrix which pretty much does run apps "invisibly" on a remote server. For stuff like 3D modeling, each tool has it's own render farming stuff, like Blender's CrowdRender/Flamenco/Blendfarm etc. – Cpt.Whale Jun 30 '23 at 19:48
  • I am not sure I understand your question, and simplifying a bit - On Linux/Unix based systems (and you likely want to use Linux for this kind of thing) you can use SSH with the "-X" switch to log into remote systems and forward X (X=graphical display). You can then run programs on the remote system and the output will appear on your computer. You can also use SSH as a way to remotely run commands on another device, and Linux is, of-course, cross platform, so it can work on both Laptops and Pi's. – davidgo Jun 30 '23 at 21:29

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OfCourse it's possible!

For example see this paper: https://link.springer.com/chapter/10.1007/978-3-030-69984-0_33

Abstract

The Raspberry Pi SBC (single-board computer) is being used for distributed memory parallel computing mainly as a low-cost teaching environment and as a low-energy consumption/green computing platform. In this chapter, we take a heterogeneous approach, where the Raspberry Pi is used along with standard computers. In the heterogeneous environment, computing as well as communication performance has to be taken into account in order to get the best results. In this chapter, we focus on the work on communication performance because it provides one of the best guidelines for successful granularity of parallel computing. We have carried out our experiments with a standard MPI (message passing interface) implementation, as well as using the currently most powerful Raspberry Pi models, in order to analyze the communication performance. We have experimented with classical Send-Receive MPI operations and the so-called one-sided MPI communication operations. Also, we document several details specifically related to the heterogeneous configuration environment that we found necessary for interoperation of MPI.

aqf
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