Rafael, thanks for the very kind note.
I've not used NumericFuctors so I can't comment on your main question. If it's
of any use, there's an entirely different approach (more of a dirty trick,
really) to inlining functions passed in as arguments. Here's a gist that shows
the trick:
https://gist.github.com/timholy/bdcee95f9b7725214d8b
This is _not_ a good strategy for functions that you wish were themselves
inlined, but for anything that processes large chunks of data this achieves
what you want.
Best wishes,
--Tim
On Thursday, August 14, 2014 11:59:28 AM Rafael Fourquet wrote:
> Hi Julia users,
>
> As this is my first post, I must say I'm extremely impressed by julia, met
> 2 weeks ago. For many months I've meant to clean-up a C++/python lib of
> mine, for public consumption. I couldn't help but procrastinate, as the
> maintenance cost is so high a tax (eg. too clever hacks, big compilation
> times (mainly due to having to instantiate non-lazily an explosion of
> templates to make them accessible from within python), the last version of
> gcc not accepting my code anymore, for probably valid reasons, I guess,
> etc.). So big thanks to all julia developers for destroying my problem and
> freeing me from C++. I gave in after checking by myself the hard-to-believe
> efficiency promise (speed within 120% of C) on the non-toy DES crypto
> algorithm. I'm very grateful for this awesome beautiful, fast, fun,
> language, that I didn't dare to dream of.
>
> The problem of higher-order-functions inlining (via callable types, aka
> functors) is getting a lot of attention, but I will need fast solutions
> soon and don't want to give up the right to use cost-free abstractions
> (offered by C++). So should we hold our breath on built-in functors, are is
> it still worth investigating library solutions?
>
> I found NumericFunctors/NumericFuns (and base/reduce.jl) based on `abstract
> Functor{N}`, are there others built on `Functor{Result, Arg1, ..., ArgN}`?
>
> I wanted to share a tiny trick that occurred to me today, which doesn't
> seem to be widely known, unleashing stateless functors (type constructors
> have a "dual nature"). Please let me know if it relies on undefined
> behavior.
>
> abstract Functor{N}
> typealias UnaryFunctor Functor{1}
> typealias BinaryFunctor Functor{2}
>
> type double <: UnaryFunctor end
> double(x) = 2*x
>
> type plus <: BinaryFunctor end
> plus(x, y) = x+y
> plus(x::String, y::String) = x*y # ;-)
>
> type compose{Out<:Functor, In<:Functor} <: Functor
> compose(x...) = Out(In(x...))
> # compose(x...) = Out(In(x...)...) # more general but awfully slow
> end
>
> doubleplus = compose{double, plus}
>
> # constrained function:
> f{F<:BinaryFunctor}(fun::Type{F}, x) = fun(x, x)
> f(plus, 1)
>
> # etc.
>
> This can't serve as a drop-in replacement for normal functions, as many API
> hardcode `Function` in their type signatures.
> I only barely tested the performances. In the benchmark code at [1],
> statement (1) is 10 times slower than statement (2) on my machine, but if
> `plus` gets replaced by the definition above, it's only about 20% slower.
> And then becomes unsurprisigly faster for `doubleplus`. However, for e.g.
> `reduce(plus, 0, 1:n)` I observed no gain (compared to plus(x,y)=x+y, as
> reduce(+,...) is special-cased and optimized), any ideas why?
>
> Note that the native_code of `doubleplus` "seems" to be more optimized with
> this finer grained definition of compose (which improves only slightly the
> benchmark):
>
> type compose{Out<:Functor, In<:Functor} <: Functor
> if In <: UnaryFunctor
> compose(x) = Out(In(x))
> elseif In <: BinaryFunctor
> compose(x, y) = Out(In(x, y))
> else
> compose(x...) = Out(In(x...))
> end
> end
>
> On a related note, my last question: is there a variation of the following
> definition that would compile?
>
> arity{N, F<:Functor{N}}(::Type{F}) = N
>
> Thanks,
> Rafael
>
> [1] https://github.com/timholy/NumericFunctors.jl, benchmark code below:
> plus(x, y) = x + y
> map_plus(x, y) = map(plus, x, y)
>
> a = rand(1000, 1000)
> b = rand(1000, 1000)
>
> # warming up and get map_plus compiled
> a + b
> map_plus(a, b)
>
> # benchmark
> @time for i in 1 : 10 map_plus(a, b) end # -- statement (1)
> @time for i in 1 : 10 a + b end # -- statement (2)
