I'm a big fan of Julia. It does live up to its speed claims. I've implemented the board game Go in Python, Rust, and Julia and Julia is definitely closer to Rust in speed. Same algorithms were used for all implementations.

Julia's time to first plot still has some problems. The Plots library can build animations, but the time to first animation on my computer is like 10 minutes, and the time to second animation is another 10 minutes. Probably just a bug, I haven't found any other case that takes so long.

I've also mentioned before that a reinforcement learning algorithm I ported from Python/PyTorch to Flux was faster, not because of training times, but because of all the other stuff (and RL has more "other stuff" than supervised learning) that goes on outside the core training loop is so much faster.

> I'm a big fan of Julia. It does live up to its speed claims. I've implemented the board game Go in Python, Rust, and Julia and Julia is definitely closer to Rust in speed. Same algorithms were used for all implementations.

"Closer to Rust than to Python" is a wide range. Almost any non-scripting language (e.g. Java, OCaml, Haskell, Dylan...) would qualify, and that's normally not enough to give them a reputation as "fast".

I’ve implemented some graph traversing algos in Julia and 100x faster than Python. Really in the same ballpark of C++, but much simpler code.

> 100x faster than Python. Really in the same ballpark of C++, but much simpler code.

One could say the same for any of the languages I listed (maybe not "much simpler code" in the case of Java).

If you want a harder guarantee, well written numerical code will be as fast as any other language. If you find a counter example, let us know (it's probably something that should be fixed).

For string processing and other gc heavy code, Julia has further to go. Julia's gc is pretty basic, and needs a lot of love. That said, for dataframes like workloads, Julia usually manages to hand with the best (data.table), and it's rarely much slower.

My assumption is that there's nothing outstanding or remarkable about Julia's performance. Comparing to Python/Perl/Ruby/TCL is a good way to make any language seem fast, and comparing to C++ is a good way to make any language seem expressive. I'd be far more interested to hear about comparisons with alternatives that might actually be competitive - non-scripting languages with a reputation for being expressive, such as ML-like functional languages, or recent general-purpose languages like Swift/Kotlin/C#.

If you want performance benchmarks vs Fortran, https://benchmarks.sciml.ai/html/MultiLanguage/wrapper_packa... has benchmarks with Julia out-performing highly optimized Fortran DiffEq solvers, and https://github.com/JuliaLinearAlgebra/Octavian.jl shows that pure Julia BLAS implementations can compete with MKL and openBLAS, which are among the most heavily optimized pieces of code ever written. Furthermore, Julia has been used on some of the world's fastest super-computers (in the performance critical bits), which as far as I know isn't true of Swift/Kotlin/C#.

Expressiveness is hard to judge objectively, but in my opinion at least, Multiple Dispatch is a massive win for writing composable, re-usable code, and there really isn't anything that compares on that front to Julia.

That seems to be very Julia-specific comparisons, which I'm sure will be oriented towards the use cases Julia has been designed for. I'm more interested in "neutral" benchmarks and more general-purpose computing areas.

> Furthermore, Julia has been used on some of the world's fastest super-computers (in the performance critical bits), which as far as I know isn't true of Swift/Kotlin/C#.

That's more a reflection of culture than performance though. Back when I worked with a bunch of data scientists they would happily run Python or R on our Spark cluster, consuming oodles of resources to do not very much, but balked at writing Scala, even though their code would have run much faster.