Access to a compute cluster without a university affiliation?

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Discussion Overview

The discussion centers on the availability of compute clusters for researchers without university affiliations, specifically for running Density Functional Theory (DFT) simulations. Participants explore various options for accessing computational resources, including public facilities, cloud services, and alternative computing setups.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant inquires about public access compute clusters for DFT simulations, referencing Brookhaven Lab's facilities as a potential model.
  • Another suggests contacting the UT Austin TACC center, noting potential funding requirements and the possibility of sponsorship from the participant's former department.
  • A participant mentions their current limitations with personal computing resources, specifically RAM constraints for simulations involving amorphous materials.
  • Concerns are raised about the challenges of accessing XSEDE resources without NSF funding.
  • Cloud computing options such as AWS, Google Cloud, and Azure are proposed as alternatives, with one participant providing cost estimates for AWS services.
  • Discussion includes the idea of building a supercomputer using Raspberry Pi boards, although one participant argues that this may not be efficient compared to using cloud GPU services.
  • Another participant suggests running a CUDA version of Quantum Espresso for better efficiency, indicating a preference for GPU computing.

Areas of Agreement / Disagreement

Participants express a range of views on the best options for accessing computational resources, with no consensus on a single solution. Some advocate for cloud services, while others consider alternative setups like Raspberry Pi or specific software optimizations.

Contextual Notes

Participants express varying assumptions about funding availability, the efficiency of different computing methods, and the specific requirements of their simulations, which may affect their recommendations.

Who May Find This Useful

Researchers seeking computational resources for DFT simulations, particularly those without university affiliations, and individuals interested in cloud computing or alternative computing setups.

crashcat
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TL;DR
I want access to a compute cluster same as most universities offer, where you schedule jobs through slurm etc., but I am not at a university this summer or fall.
I am temporarily an orphaned researcher with no university affiliation. Is there any public access compute cluster I can run some DFT on? I know that, for instance, Brookhaven Lab allows free access to its state of the art micro/nanofab facilities if you write and get your proposal accepted. Is there something similar from a university or national lab for computing resources?
 
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Thanks I'll check them out. I have been running it on my personal computer and got like half the results I need, but I don't have enough RAM for the number of atoms in some of the simulations. The high number of atoms is to deal with it being amorphous material. I was looking and maybe I can run a version that only takes 50 GB, but right now it's like 80 GB.
 
XSEDE is an uphill battle if you aren't already NSF funded.

The obvious answer is AWS, Google Cloud, Azure, etc. If you don't want to pay for them, recognize that nobody does - that's why there is all the competition/restrictions on university clusters.
 
I never thought about using AWS. Their pricing is actually reasonable. For a 16-core Xeon, 128 GiB RAM and the discount for letting them run the job whenever there is free capacity, it's 0.62 USD per hour. So I'm estimating $200 a month for a couple of months to run the code I need, or could be much less if their Xeon runs a lot faster than my OC i5 from 2016...

Plus it's scalable for the size of the job I'm running. I'm going to look more into this over the weekend.
 
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The Pi's are so slow, I think it would be cheaper and faster to run the CUDA version of Quantum Espresso, which I haven't tried but some guy vouched for it.
 
crashcat said:
The Pi's are so slow, I think it would be cheaper and faster to run the CUDA version of Quantum Espresso, which I haven't tried but some guy vouched for it.
Yes, you should be running this on a GPU for efficiency, cloud GPU is also a thing e.g. FluidStack.
 

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