There are different types of molecular dynamics that non-ex people run, and different modes of the same code may be more or less latency sensitive (e.g. two "st

There are different types of molecular dynamics that non-ex people run, and different modes of the same code may be more or less latency sensitive (e.g. two "standard" Gromacs benchmark cases I'd have to look up). While I'm unconvinced of the overall merits of exascale, it's clearly not true that you can generally do all the large-scale, high-resolution, or whatever, calculations in an embarrassingly parallel way. I haven't kept up with the public applications for CORAL et al, but there are definitely enough for petascale and above. My bitter experience, particularly with "big data" people, is that they just won't be told by those with long relevant research computing experience in HPC and similar. It's hardly that HPC people don't know what systems can do, particularly if they live and breathe things like distributed linear algebra. I despaired, and largely gave up, when someone strode into a chair From Industry and assured us that the university didn't do Big Data, notwithstanding LHC, astronomy, sequencing, synchrotron work, etc., and was then going to build a Big Data Cluster from a few knackered PCs (and in a basement!) to better the HPC systems. Then I had MPI explained to me.