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I'm Materials Science student interested in using an RPi cluster for calculations of materials properties.

I'm very new to HPC but what I know about the RPI is that its processor is based on the ARM architecture which was initially intended for control and now we see this architecture on phone processors, but I don't know the limits I can push this type of cluster to, with heavy calculations. I also know that they will function as a means to devide the calculations in threads with the propiate parallel programing so I will not ask if they work as a single virtual machine, I understand it doesn't.

My question is if the RPi cluster performance ratio, while stacking, does keep increasing for heavy math calculations or any physics calculations; or if there is a point where the performance curve gets steady.

If anyone have used a RPi's clusters for Physics, Math or Chemistry calculations, how does it performs?

Thanks in advance.

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If you're interested in learning the basics of how the RPi functions and how to do such tasks, the RPi is a great tool.

That being said, an RPi cluster has no chance in hell at defeating even a moderately recent i5 processor. Your laptop will probably serve you better than an RPi cluster for heavy computations.

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  • Seconding this. RPI is great for a project to run a sensor network and to offload computation directly to a sensor. It can also be supremely useful to connect an older research tool to a network and allow remote data collection. However, it is not a serious computation platform, it has an impressive amount of power, but it is $35 after all . The most reasonable use I see for a true cluster is redundancy for long term installations.
    – crasic
    Commented Dec 23, 2017 at 3:23
  • The main reason for asking was the price. I could stack 50 of them and enhance some of my work, and it would be way cheaper than a mixed CPU/GPU cluster. But if you are saying it is a no go, well I guess I'll have to invest in a more serious one. Thanks for the reply guys.
    – dbarcene
    Commented Dec 23, 2017 at 16:43

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