The performance comparison in Fig. 7 is quite limited.
Python with Numpy and JIT, as well as Matlab, R and FORTRAN could also be included with suitable tasks. The code should be optimized in each case.
Here's a broader set of benchmarks, including to R, Matlab and Fortran:
https://julialang.org/benchmarks/
AFAIK these have been optimized for the other languages, but your opinion may differ if you look at the implementations. Code for these is available here:
The Fortran implementation that I see on GitHub, is in no way the most optimized implementation that one could have, or would do in any serious heavy numerical computation. Yet it is amazing to see how well Fortran performs given all the lack of optimizations in the benchmark code. Also, performance highly depends on the compiler and compiler optimization flags and there is no mention of the optimization flags used for the Fortran compiler. and remember optimizations do not end with "-O3" flag in Fortran (and C). A Julia benchmark written by Julia developers showing Julia outperforms any other language does not seem to be the best way of assessing languages' performance. Did you have experts in each of the other languages to implement the respective codes?
Yeah, I mean, Julia outperforming Fortran?
Come on!
https://github.com/dyu/ffi-overhead
This is one reason why you could see Julia outperforming Fortran in some cases where the FFI speed matters. But Fortran does have easier aliasing analysis (because you can't alias) so that helps there, but other than that most of the compiler passes are pretty much the same.