Sorry if this has been asked before. I have functions that take vectors of functions as arguments, similarly to the following: function g2(fs::Vector{Function}, x::Float64)::Float64 a::Float64 = 0.0 for f in fs a += f(x) end a end Let’s say we define julia> f1(x…

FunctionWrappers.jl could perhaps be of interest. Note that your function is type stable (because of the type assert) but the result of f is not.

You could replace fs::Vector{Function} with fs::Tuple{Vararg{Function} and pass a tuple of functions rather than an array. That will allow the compiler to know the exact types of the functions. Unfortunately, the return types do not seem to be inferred in that case: the fact that there is a loop ove…

Try “lispy tuple recursion,” @inline g2(fs::Tuple{Function, Vararg{Function}}, x::Float64) = _g2(0.0, fs, x) # _gs is a "private" method that processes the first call, then discards that function and recursively calls itself @inline _g2(a, fs::Tuple{Function, Vararg{Function}}, x::Float64) = _g2(a…

[image] kristoffer.carlsson: How does compile time scale with the number of functions O(N^2) is there a threshold where this does not work Set by MAX_TUPLETYPE_LEN. Currently 15, but Jameson has shown that recent improvements to inference.jl may allow that to be changed to >1000.

This seems like a pretty common pattern in high performance julia. I wonder if there’s a way to get it into a macro.

I’ve got an unregistered package Unrolled.jl with generated functions that achieve the same thing, but without the quadratic compile-time, and for arbitrary-long tuples: julia> using Unrolled, BenchmarkTools julia> g2(fs::Tuple, x::Float64) = unrolled_reduce(+, 0.0, unrolled_map(f->f(x)…

Type stability when arguments are functions

General Usage

kristoffer.carlsson July 8, 2017, 11:51am 5

How does compile time scale with the number of functions and is there a threshold where this does not work? Would try it myself but I don’t have access to a computer for a while.

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Type stability when arguments are functions

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