[secabeen]

Generally, scientists aren't blocked while they are waiting on a computational queue. The results of a computation are needed eventually, but there is lots of other work that can be done that doesn't depend on a specific calculation.

[jefftk]

It's good to learn how not to be blocked on long-running calculations.

On the other hand, if transitioning to a bursty cloud model means you can do your full run in hours instead of weeks, that has real impact on how many iterations you can do and often does appreciably affect velocity.

[secabeen]

It can, if you have the technical ability to write code that can leverage the scale-out that most bursty-cloud solutions entail. Coding for clustering can be pretty challenging, and I would generally recommend a user target a single large system with job that takes a week over trying to adapt that job to a clustered solution of 100 smaller systems that can complete it in 8 hours.

[Fomite]

This is a big part of it. In my lab, I have a lot of grad students who are computational scientists, not computer scientists. The time it will take them to optimize code far exceeds a quick-and-dirty job array on Slurm and then going back to working on the introduction of the paper, or catching up on the literature, or any one of a dozen other things.

[esalman]

Grad student here, I can attest to that.