Consider the following code:
function e()
els = Any[[1], 10.0]
return rand(els)
end
function f()
a = e()
g(a)
end
function g(a)
return invoke(*, Tuple{supertype(typeof(a)),Float64}, a, 10.0)
end
The idea is: g is a very generic fallback (in the original code generated by a macro for dozens of functions), e is a type-unstable computation whose result can only be inferred as Any, and f combines both.
Since f is type-unstable, I don’t expect great performance but the the measured time is far worse than what I would expect:
julia> using BenchmarkTools
julia> @btime f()
260.207 μs (351 allocations: 19.44 KiB)
1-element Vector{Float64}:
10.0
It can be demonstrated that Julia decided to inline g, which was a very bad decision. After marking it as @noinline the performance is reasonable:
julia> @btime f()
72.831 ns (6 allocations: 244 bytes)
1-element Vector{Float64}:
10.0
So, about 3600x faster (I’m using Julia 1.8.1).
Now my question is: is there any way to fix this performance issue other than marking g as @noinline? It is perfectly fine to inline g into type-stable functions, it can even improve performance a little in some cases.
.