Comment by plafl | Hacker News Reader

Comment by plafl | Hacker News Reader

I have started to learn Julia recently after some following the news. The addition of a debugger finally decided me to give a try. For the record I have been using Python for scientific programming before numpy existed, also a little Matlab too.

Since my experiments are just some simple implementations of kmeans and bandits epsilon greedy algorithms take what I'm going to say with a grain of salt. Anyway:

I find Julia very interesting. I managed to make kmeans fast with type annotations. If I were to summarize I would say that Julia is a much better cython. I never managed for example to debug cython. Profiling also seems to work in Julia, another thing very hard to do in Python. On the other hand, as in cython, sometimes you don't know if some missing type annotation is slowing your program. It remains to be seen if the "verbosity" of type annotations is going to help or slow its adoption.

>sometimes you don't know if some missing type annotation is slowing your program

The lack of type annotations won't slow down your Julia program, since the compiler will infer them anyway (types are for multiple dispatch, documentation or to assert types, not speed). What will affect it is if the type can be inferred or not. For example, if you have a variable that is sometimes an int, sometimes a float, sometimes a string it will force the compiler to put the checks on runtime, dropping performance to CPython level (although the compiler optimizes small unions, such as Union{Int, Nothing} for a nullable Int). That's what the community calls type-stability, and the first step of profiling a function is usually using the macro @code_warntype to see what the compiler is inferring. See:

https://docs.julialang.org/en/latest/manual/performance-tips...

You need types. It's the secret sauce behind multiple dispatch.

Be sure to read the sibling comment from ddragon; it’s not necessary to explicitly write type annotations, that should never increase performance. It only alters when a particular function can be called. The “secret sauce” in Julia is the aggressive type inference that gets run on non-typed code, determining the types (when possible) of your entire program.

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