The unreasonable effectiveness of the Julia programming language

Re:The unreasonable effectiveness of the Julia programming l... by fantod on Friday October 09, 2020 @08:59AM (#24729439)

Really? My only experience with Julia was porting a simulation from Python and the speedup was incredible. Admittedly, that's an instance of numerical computing, but I would wonder why it was slow for you.

[ Parent | on HN ]

Re:The unreasonable effectiveness of the Julia programming l... by rich_sasha on Friday October 09, 2020 @09:03AM (#24729469)

This is only meta analysis, but some topics come up more frequently when people say how impressed they are with Julia.

Simulations, ODEs, tight for-loops seem to be high on the lists.

“Generic” data science doesn’t, or indeed general, unscientific programming.

Perhaps this is not right, or outdated, but my impression is that Julia is perhaps very well suited to _some_ scientific programming, not necessarily all of it, despite the broad statements.

[ Parent | on HN ]

Re:The unreasonable effectiveness of the Julia programming l... by jakobnissen on Friday October 09, 2020 @09:50AM (#24729881)

I think ODEs and numerical computing gets mentioned because it was the first area where Julia packages outshined all alternatives. It originated among physicists and mathematicians, so it has a "head start" in these areas.

Julia is a general-purpose programming language, despite having roots in scientific programming. Its performance characteristics with its high latency and runtime memory overhead makes it unsuitable for a number of non-scientific applications. But not all. I think Julia would work excellent as a webserver backend, for example.

I mostly use it for DNA sequence processing, which is not really numerical programming, but more string processing. There it shines. I wouldn't want to make a video game in Julia or fly a plane using Julia software.

[ Parent | on HN ]

Re:The unreasonable effectiveness of the Julia programming l... by goatinaboat on Friday October 09, 2020 @09:54AM (#24729916)

Really? My only experience with Julia was porting a simulation from Python and the speedup was incredible.

The bang-for-your-buck of adding Numba to existing Python code is why we passed on Julia. Give it a try.

[ Parent | on HN ]

Re:The unreasonable effectiveness of the Julia programming l... by jakobnissen on Friday October 09, 2020 @09:59AM (#24729959)

The problem with Numba is that it only really works in small, limited usecases. Good for e.g. Numpy arrays, but can it provide these massive speedups with custom classes? Arrays of strings? Sets? Probably not, since both Julia and Numba relies on inferring the data types of all objects, and on them being stored efficiently in memory. The Julia type system allows easy type inference, and all custom structs are stored efficiently in memory. In Python, neither of those are true.

[ Parent | on HN ]

Re:The unreasonable effectiveness of the Julia programming l... by vslira on Friday October 09, 2020 @01:27PM (#24732237)

hijacking this discussion to raise the banner of seamless interoperability between numba (quick numerical code) and pypy (quick algorithmic code + business logic).

My dream python is pypy+mypy+numba. Alas, pypy doesn't play nice with the others "(

[ Parent | on HN ]