| That's the Sentinel system. I worked on it when I was at Cray, and we did some covid stuff[1][2] with a researcher at UAH. We accelerated a docking code using some cool tech I created (in Perl, so there!) and some mods my teammates did to the queuing system. The work won some award at SC20[3] (fka Supercomputing conference). I had considered submitting for the Gordon Bell prize, which had been specifically requesting covid work, though I thought the stuff we had done wasn't terribly sexy. We were getting ~250-500x better performance than single CPU runs. Looking back over these, I gotta chuckle, as this (press releases) is pretty much the only time I'm called "Dr.". :D Back to the OPs points, they are right. In most cases, cloud doesn't make sense for traditional HPC workloads. There are some special cases where it does, those tend to be large ephemeral analysis pipelines, as in bioinformatics and related fields. But for hardcore distributed (mostly MPI) code, running for a long time on a set of nodes interconnected with low latency networks, dedicated local nodes are the better economic deal. During my stint at Cray, I was trying (quite hard) to get supercomputers, real classical ones, into cloud providers, or become a supercomputing cloud provider ourselves. The Met Office system is in Azure, is a Cray Shasta, but that was more of a special case. I couldn't get enough support for this. Such is life. I've moved on. Still doing HPC, but more throughput maximized. [1] https://www.uah.edu/science/departments/math/news/14954-uah-... [2] A whole marketing writeup was done here https://www.hpe.com/us/en/newsroom/journey-to-accelerate-dru... . I tried very hard to correct the errors in the writeups. Sadly I wasn't successful. [3] https://baudry-lab.uah.edu/news#h.121c63ayp0k0 |