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Comment by oscardssmith | Hacker News Reader

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.

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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.

> 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.

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.

That's fair. That said, the fact that writing a BLAS implementation in a high level language seems like a favorable benchmark does kind of show my point that for numerical computing, Julia is really fast (especially for a high level language).

As a general purpose language, I'd probably categorize Julia as fast, but not crazy fast. Numerical computing is definitely where most of the man hours have gone into development. As a general purpose language, Julia probably ends up at around the same place as C# (not as fast as C++, but still pretty respectable). That said, this is an area where the main thing Julia needs here is more optimization on some of the libraries that already exist, and some new libraries to extend the language's reach further. I don't think there's anything fundamental holding it back from getting closer to C++ performance in general computing. It uses the same compiler (LLVM), and it's pretty clear that you can get Julia to output the same assembly code. It's just a matter of having enough devs pushing ecosystem forward.

For a non-numerical benchmark where Julia does really well, you should also check out https://h2oai.github.io/db-benchmark/. There's a bunch of work to be done to improve DataFrames.jl more, but it's already one of the fastest tools for what it does (despite having only reached version 1.0 a few weeks ago).

Ok, that's a little more interesting - a "neutral" benchmark of several tools, though still very much in a niche use case.

Is there any effort to include Julia in the TechEmpower Benchmarks? If it's trying to be a general-purpose language, that's the first place I'd look.

It's already on there for some of the benchmarks at least. It generally is roughly between 30th and 60th place depending on the benchmark (with outliers in both directions). That said, HTTP.jl is not a library that has had a ton of work in Julia, and it's probably not what you want to write a high performance webserver in at the moment. I don't think this is a fundamental limitation, but just a domain that needs a couple thousand dev hours to make competitive with some of the more mature frameworks. Another part of the problem is that Julia has some fairly well known garbage collection performance problems. GC performance hasn't been a major dev priority as of yet, since the language is much better than most high level languages at eliding allocations or stack allocating variables (so it doesn't make as much garbage for many workflows). That said, for GC heavy tasks (string stuff often is), it is a major problem that is starting to get a bunch of effort put into it. Hopefully in a year or 2, these issues will be solved. It's nothing too fundamental, just that the current GC only has 2 generations, and the heuristics for moving objects between generations are not tuned especially well.

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