goerz

Don’t people google their newly coined acronyms? GRAPE is already Gradient-Ascent-Pulse-Engineering, which is arguably “machine learning” (optimal control)

Even better: Julia (although Fortran is pretty good!)

“Last week” is well within the 2-week return window.

I’m not sure I would consider the scientific computing that Julia targets a “tiny” niche. It’s worth remembering that Python is 30 years older than Julia, and Julia has only been in a really usable state for 4 or 5 years. You can’t expect it to displace Numpy/Scipy/etc overnight. Especially if you include machine learning, where there’s just huge momentum with large corporations having massively invested in Python frameworks for at least 10 years. Subjectively, I’m seeing quite a lot of growth in Julia. It’s certainly a much stronger language than the Python/C++ combination that currently has the biggest market share in that area.

Also, there’s nothing wrong with niches. Julia is undoubtedly less of a general-purpose language like Python, but it very much shines in its domain.

True, but C and Fortran have very different semantics that make them suitable for very different fields. In particular, C does not have great semantics for linear algebra. Also, C has many footguns due to its use of pointers, while Fortran has virtually none.

You still need to learn the tooling, but the tooling is actually pretty similar for Fortran/C/C++ (same compiler/linker toolchain). Libraries aren't that much of a thing in Fortran, but to the extent that they are, this again is similar to C/C++.

C++ and Rust don’t really have good semantics for scientific computing (linear algebra). They’re great for many other domains like “system’s programming”, where Fortran doesn’t compete, of course. The only language that’s similarly (in fact, more) expressive as Fortran for scientific computing today, as well as holding up for performance, is Julia

How has it “not panned out”? Julia has been in a useable state only for a few years, and is still rapidly developing. Prior to Julia 1.0, I would have said Fortran (which I have 15 years experience with) was the most productive language for scientific computing. Today, Julia is, by a wide margin. In fact, our benchmarks show Julia matching or beating Fortran’s performance, to say nothing of the mich increased flexibility.

Is this affiliated with Juno the iOS Jupyter client?

Not if the air that you need to breathe has a wet bulb temperature >35C