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aBioGuy | 3 years ago

Furthermore, scientific computing often (usually?) involves trainees. It can difficult to train people when small mistakes can have five figure bills.

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Moissanite|3 years ago

This is the biggest un-addressed problem, IMO. Getting more scientific computing done in the cloud is where we are inevitably trending, but no-one yet has a good answer for completely ad-hoc, low value experimentation and skill building in cloud. I see universities needing to maintain clusters to allow PhDs and postdocs to develop their computing skills for a good while yet.

atrettel|3 years ago

I agree that this is a big thing to consider here too. I set up a computing cluster in grad school and it was much less costly to make a mistake there than it would have been in a cloud service. Re-running something only wasted wall time and not any money. That said, money is not the only scarce resource here. Researchers can get allocations at university and government HPC systems, but you then have to be quite careful with your allocation of computing time. I remember keeping track of the number of SUs (core-hours) I was burning quite carefully when I used university clusters, since once it is gone, you might not get any more time.