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A group of researchers in my university (including me), started to study the possibility to move to cloud computing services in order to run our simulations.

The simulation areas include condensed matter physics, particle physics, quantum field theory, soft condensed matter, statistical physics, etc.

We have zero experience with it.

The question is: Are there reliable cloud computing services for matter modeling?

By reliable, I mean that their performance (CPU/GPU use, RAM use, hard drive speed) is similar to a local computer, with lower degradation (as many of them use virtual machines). As the demands are very heterogeneous, at first glance, we are thinking in bare iron system in order to install/configure different software for running the simulations. About the hardware, it is desirable to have both CPUs and GPUs access.

To distinguish this question from these ones:

where access is usually not just granted to anyone who pays for it, perhaps you can suggest commercial services here.

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  • $\begingroup$ The comments on this question have been moved to the HPC chat room. $\endgroup$ – Nike Dattani Apr 4 at 16:33
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Materials Square (MatSQ)

Materials Square (MatSQ) is a web-based simulation platform providing an interface, workflow, and pay-as-you-go cloud server for material simulations. It supports DFT, MD and CALPHAD calculations.

  • Provides a free 3D atomic structure-modeling interface supporting most structure types.

enter image description here

  • Support density functional theory calculations of Electronic Structure, Phonon, reaction path and optical properties using Quantum ESPRESSO.
  • MD simulations include a GUI for LAMMPS and can analyze physical movements of atoms at finite temperature, thermal, optical and mechanical properties.
  • Pricing ranges from pay-per-use of $0.25 per core hour to UNLIMITED plans.

enter image description here

References:

  1. Materials Square Brochure
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Amazon Web Services (AWS) and Google Cloud can be used for the running simulations and I've used both. I applied to a few grant programs a while back where part of the award included AWS or Google Cloud credits. I think the credits were donated to the funding agencies by the respective companies to encourage more use of those services in science and engineering.

Anyway, it was pretty easy to put together an environment to run LAMMPS on both these services. The cores themselves were not that fast, but the environments were well suited for running replica exchange calculations with large numbers of replicas (50 to hundreds). The communication between the "virtual CPUs (vCPUs)" was sufficiently fast that I didn't see any performance degradation between serial runs and replica exchange. That said, exchanges were only attempted every 1000 steps (~3 minutes of real time) in my simulations, so communication would have had to be really slow to see a major impact.

I haven't used them since preparing the those grant applications since the cost seemed somewhat high compared to buying a workstation computer and running it for years.

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    $\begingroup$ Thanks for your 3 answers today: You're on a roll! I would have preferred if we could stick more to this format so people could upvote AWS or Google Cloud separately, and we can see how the community feels AQC/GC compares with MatSQ (for example). In this sense, you would pick AWS or GC and go into more detail, such as how much it costs / how much disk space they offer, etc. But what you have written is still useful knowledge for future users who might want to run LAMMPS on one of those services. $\endgroup$ – Nike Dattani Apr 1 at 20:14
  • $\begingroup$ @NikeDattani Thanks! My experiences with AWS and Google Cloud were fairly similar, so I grouped them together. $\endgroup$ – WaterMolecule Apr 1 at 20:32
  • $\begingroup$ Understood. It just makes it hard for people to upvote for AWS or GC separately: maybe 10 people like AWS and only 5 people like Google Cloud, and we won't be able to learn that from this type of Q/A format. It's just something to maybe keep in mind for future Q/A on this site! $\endgroup$ – Nike Dattani Apr 1 at 20:35
  • $\begingroup$ @NikeDattani but by doing that, you form opinion-based answers, which don't add value. Questions can be closed due to answers given being opinion-based. The same should apply here. $\endgroup$ – spikey_richie Apr 2 at 9:27
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Molecular Quantum Chemistry via WebMO on Google Cloud

It's fairly easy to run Linux-based programs on Google Cloud. The WebMO interface gives a tutorial on installing on GCP including molecular quantum chemistry programs.

Supported tools include open source and "free binary" programs, for example:

  • Psi4
  • Orca
  • GAMESS
  • NWChem
  • Mopac

As mentioned in another answer, costs are higher than a dedicated workstation or cluster, but if you're performing occasional calculations, it might be worthwhile.

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Hetzner

If you know you will continuously need a powerful computer for the next 1-2 years, consider renting a bare-metal server from Hetzner. You can rent a 32 core 2nd generation AMD EPYC server with 256 GB of RAM and a fast SSD for about 200 EUR/month. No traffic fees.

You get remote access to a blank machine, and you are free to install whatever OS and software you need, no virtualization overhead or other restrictions.

There are other configurations to choose from, but the AMD boxes are usually the better value.

PS: The 32 core box I mentioned is seriously powerful when it comes to CPU compute. You can expect Linpack perfomance in excess of 1 TFLOPS, as the 24 core model of the same family can do 1 TFLOPS +/- 10%.

One thing you need to watch out for is the number of memory modules! If you rent a server with less than half of the maximum memory capacity, some of the memory channels will be left unpopulated, which may have a negative impact on performance.

So for the EPYC server I would recommend exactly 256 GB of RAM, or if (for your applications) that is not enough to go with 32 cores, then 512 GB would be the next good option.

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  • $\begingroup$ +1. Welcome to our new community, and thank you for contributing here! We hope to see much more of you in the future!!! $\endgroup$ – Nike Dattani Apr 15 at 21:05

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