Hacker News Reader

Comment by fasterbynight | Hacker News Reader

I don't know about other languages, but in Julia I've heard people often say that loops end up faster than the equivalent vectorized code. So while this is true for Python/Matlab I don't think it is good universal advice.

That said, matrix notation can sometimes be the more readable way of expressing a calculation.

View on HN

I might have used an early beta of Julia circa 2018 or something, but the chorus that it performs like $static_fastlang doesn't match the experience I had.

Do you have code to share and look at? All we can do is point to real-world code and benchmarks. For this specific case, see LoopVectorization.jl results (https://juliasimd.github.io/LoopVectorization.jl/latest/exam...), and the corresponding effects on stiff ODE solver benchmarks against C and Fortran packages (https://benchmarks.sciml.ai/html/StiffODE/Hires.html).

I'd don't know what you mean by "performs like" but it's definitely significantly closer in runtime speed after the first run to compiled languages than interpreted (in particular Python/Matlab).

Anyways, my point was more in response to this from the person I responded to: "my instinct is to look for opportunities to vectorize by rewriting loops into matrix notation on paper and then expressing them as array calculations". In some languages (Julia in particular) that is slower than the equivalent loop based code.

Reply on news.ycombinator.com