Anders Damsgaard,
> However, I would *never* consider Julia a viable alternative to C/FORTRAN
> tasks, including numerical simulations and massively parallel deployment
> on HPC systems.
i'm ignorant, but curious. a friend who does high performance computing
is a fan of Julia, and in the past pointed me at
https://www.nextplatform.com/2017/11/28/julia-language-delivers-petascale-hpc-performance/
i'm not a believer in miracles, but the performance here seems fairly
impressive. does this contradict your pessimistic assessment of Julia
(without saying which view is correct)? is it maybe because of
specialized features of Julia (i can imagine Julia's support for SIMD
operations could help a lot with this sort of computing)? or...?
obviously, incompatibility between versions, slow garbage collection,
etc., which you mention below, are not good signs.
cheers, Greg
> While promising on paper, the reality is that the language is
> immature which creates issues with code compability between versions.
> One example is that they flip-flop between variable scope in global
> space, effectively breaking most scripts without warning. Also, the
> julia+python+blas dependencies of installed packages and computational
> overhead quickly become very significant. Furthermore, the garbage
> collection is poor and leads to orders-of-magnitude increase in memory
> footprint over days of running an iterative simulation.
Received on Tue May 11 2021 - 10:15:37 CEST