Hi All, I have a project doing mainly matrix multiplication and inversion (pinv and inv). No parallel features are used, julia is launch without additionnal workers. Inversed matrix sizes vary from [50 to 800, 10 to 100].
I develop with last stable 3.8 on a standard desktop computer running ubuntu 14.4. Processor is a intel I5 quadcore, 8 Go ram. I want to use the code in production on a "server" with 2 Octocores using Hyper-V for a virtual machine running same os and julia. VM configuration is 16 virtual cores (32 maximum), 64 Go ram. On desktop, main function call use 2 cores/4 at 100%, 3.4 Ghz. a lot of free memory. On server, same call fully use all 16 virtual cores but it takes 5 times more time to run (on average over more then thousands runs) Server processor speed is 2.6 Ghz, a lot of free memory. I would like to understand why it takes all VM ressources to finally be slower. Anyone have any hints about how julia manage machine ressources and how to maximise performance for linear algebra ? Maybe it comes from virtualization parameters, any hints/links ? Thanks in advance, Jojo
