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I get this argument, but in my experience, PhD students are still using Python and the HPC centers I work with still don’t have Julia on their radar.
by marmaduke 6y ago
I get this argument, but in my experience, PhD students are still using Python and the HPC centers I work with still don’t have Julia on their radar.
- dnautics 6y agoIndeed. I deployed julia on 40,000 cores at TACC, but they wouldn't take an upgrade ticket so I had to run it in a container (not docker, obvi). On the other hand Julia probably has a very strong long game, given that the Federal reserve bank uses it.
- ViralBShah 6y agoWhat's the application? Would love to read more if there is a paper. The Julia Lab folks at MIT are collecting big parallel applications for pushing parallelism in Julia further.
- dnautics 6y agoThere was no paper, sorry, we talk about some of the research here https://www.youtube.com/watch?v=aP0Y1uAA-2Y https://www.youtube.com/watch?v=aP0Y1uAA-2Y big julia demo 40 minutes in. But the video doesn't feature the results I talk about (that happened a few months after the video) the specific results are somewhere in a DARPA presentation (but I don't have those slides, and the data are like 5 laptops back for me). The parallelism was, well, Gustafson parallelization, I pushed a randomly-generated 10k x 10k matrix solve to each of the nodes with a custom soft floating point to measure the relative numerical accuracy. Each node did all of one run per core, it was done very fast, but it would have taken months on a pc. As it was a gustafson scaling at its finest, there was zero cross-node communication during the computation. The containerization I used was singularity. I don't really love them much anymore, for various non-technical reasons, but it got the job done.