> People usually use C++ or Julia. All of the fastest answers are in Julia

That's surprising to me and piques my interest. What sort of pipeline is this that's faster in Julia than C++? Does Julia automatically use something like SIMD or other array magic that C++ doesn't?

I use Rust instead of C++, but I also see my Julia code being faster than my Rust code.

In my view, it's not that Julia itself is faster than Rust - on the contrary, Rust as a language is faster than Julia. However, Julia's prototyping, iteration speed, benchmarking, profiling and observability is better. By the time I would have written the first working Rust version, I would have written it in Julia, profiled it, maybe changed part of the algorithm, and optimised it. Also, Julia makes more heavy use of generics than Rust, which often leads to better code specialization.

There are some ways in which Julia produces better machine code that Rust, but they're usually not decisive, and there are more ways in which Rust produces better machine code than Julia. Also, the performance ceiling for Rust is better because Rust allows you to do more advanced, low level optimisations than Julia.

To be clear, the fastest theoretically possible C++ is probably faster than the fastest theoretically possible Julia. But the fastest C++ that Senior Data Engineer candidates would write in ~2 hours was slower than the fastest Julia (though still pretty fast! The benchmark for this problem was 10ms, and the fastest C++ answer was 3 ms, and the top two Julia answers were 2.3ms and .21ms)

The pipeline was pretty heavily focused on mathematical calculations – something like, given a large set of trading signals, calculate a bunch of stats for those signals. All the best Julia and C++ answers used SIMD.

The main thing is just that Julia has a standard library that works with you rather than working against you. The built in sort will use radix sort where appropriate and a highly optimized quicksort otherwise. You get built in matrices and higher dimensional arrays with optimized BLAS/LaPack configured for you (and CSC+structured sparse matrices). You get complex and rational numbers, and a calling convention (pass by sharing) which is the fast one by default 90% of the time instead of being slow (copying) 90% of the time. You have a built in package manager that doesn't require special configuration, that also lets you install GPU libraries that make it trivial to run generic code on all sorts of accelerators.

Everything you can do in Julia you can do in C++, but lots of projects that would take a week in C++ can be done in an hour in Julia.