If you’re coming from Fortran to Julia for heavy number crunching, one performance aspect you might notice is just caused by Julia using the LLVM compiler, which doesn’t generate fused multiply-add (FMA) instructions by default. Thus, you need to opt-in explicitly. Luckily, Julia’s code introspection and manipulation tools make it relatively easy to do, using for example @fastmath from Base or @muladd from MuladdMacro. I have described that in a blog post on optimizing our hyperbolic PDE framework Trixi.jl.
Nevertheless, I definitely like using Julia (since v0.3 many years ago…) in my daily work as scientist working in numerical analysis and applied mathematics. We describe some of the nice aspects in our JuliaCon talk on Trixi.jl. Our team has a strong background in C, C++, and Fortran. Most of us have used an HPC Fortran code before. In our talk, Michael describes why we started to work on a new Julia code (Trixi.jl) and what we like about it. We still have a lot to do, in particular on the HPC side, but we’re pretty happy at the moment. Having said this, the Fortran code is of course still in use and will probably stay alive for quite some time.
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