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AFAIK in scientific computing, the research group is targeting their university's particular cluster and the cluster's admins/operators are there to maintain an

• bee_rider 4y ago
  I think it depends on the group. For example, I have more-or-less free rein a small cluster which belongs to my professor (my group is small enough to just manually coordinate to not annoy each other), then there's a university cluster if I feel like messing around with bsub and waiting in a queue. I'm also under the impression that it isn't super difficult to get a handful of xsede credits when starting a new project. I prefer to debug on my local machine or the group cluster, so I don't have to risk putting pointless buggy runs into the shared queues (which might be costly, depending on policy, but even if they aren't costly I don't want to hog resources for no reason). If I write the code that tunes performance for a particular machine, then I have codepaths that I wrote and can't test locally very easily. Better to use the existing OpenMP and MPI knobs to tune. Sure, you can end up with an evil thread interleaving that only shown up when you have 100 cores, but this should hopefully be rare. That's my perspective at least. I do numerical stuff, so I'm shielded by some of the real nitty-gritty "perfectly synchronized threads" stuff by the fact that my costly operations are usually decent sized linear algebra calls, so it is usually best to give the lowest NUMA level(s) to BLAS, LAPACK, or maybe some sparse solver (which should be reasonably scalable and pretty well tested).