I do not have deep experience with this, but the word on the street is that Julia can be faster than other compiled languages. I think that's partly because it

I do not have deep experience with this, but the word on the street is that Julia can be faster than other compiled languages. I think that's partly because it can find a good algorithm, based on your data structure; think of loop unrolling, etc. You can inspect the assembly code for anything you're working on, and that can be quite helpful at times; see e.g. https://youtu.be/wU6c8CDRXJE?t=3887 https://youtu.be/wU6c8CDRXJE?t=3887. I think the reason why quite a few high-performance people (I mean in the science community -- I don't know much about other communities) are excited about Julia is simply that well-respected experts are also excited. An example is the Julia implementation of the MIT GCM (general circulation model) for the ocean; for similar projects, see https://github.com/CliMA https://github.com/CliMA. Programmer effort is also a factor in scientific computation. If the system can do some of your work for you, so much the better; see e.g. https://www.youtube.com/watch?v=rZS2LGiurKY https://www.youtube.com/watch?v=rZS2LGiurKY for a lecture that touches upon how the framework of Julia eases the burden of machine-learning tasks. As I say, though, I do not have deep experience with Julia. I've rewritten one of my numerical models in Julia and the speed is about the same as before, but my code is much shorter and easier to understand. I would not burn up 6 months translating a complex code, but nor would I start a 6-month coding project in Fortran anymore.