Generated functions for performance auto-tuning

To get the best performance across different inputs, we often have multiple implementations, and choose which one to use based on the input given. For example, for array traversals we may want to unroll loops for small arrays, but not for larger ones. So we’d have an implementation of each, and choose based on some cutoff.

This gets awkward, because the optimal cutoff depends not only on the algorithms, but on the machine running the code. So what we’d really like is a way to tune this, ideally without the end-user needing to worry about it.

I found a way to do this by entirely misusing generated functions. But… it seems to work? Here’s the code, with f and g chosen to actually do some timing, to show that this is possible:

using Static: StaticInt

# Cheaper for small values
function f(x)
    t0 = time_ns()
    t1 = t0 + 50x
    @elapsed while time_ns() < t1

# Cheaper for large values
function g(x)
    t0 = time_ns()
    t1 = t0 + 1000
    @elapsed while time_ns() < t1

# Set initial bounds
(lo, hi) = (0, 100)

# Warm up, to avoid measuring compile time

function bisect(f, g, lo::Int, hi::Int)
    f_lo = f(lo)
    f_hi = f(hi)
    g_lo = g(lo)
    g_hi = g(hi)
    while (hi - lo) > 1
        x = round(Int, (lo + hi) / 2)
        fx = f(x)
        gx = g(x)
        if fx < gx
            (lo, f_lo) = (x, fx)
            (hi, f_hi) = (x, fx)
    return lo

@generated function cutoff(::F, ::G, ::StaticInt{lo}, ::StaticInt{hi}) where {F,G, lo, hi}
    # Recover functions from their types
    f = F.instance
    g = G.instance

    # Find the best value
    x = StaticInt(bisect(f, g, lo, hi))
        # Hard-code the result

And the result:

julia> cutoff(f, g, StaticInt(lo), StaticInt(hi))

julia> @code_warntype cutoff(f, g, StaticInt{lo}(), StaticInt{hi}())
MethodInstance for cutoff(::typeof(f), ::typeof(g), ::StaticInt{0}, ::StaticInt{100})
  from cutoff(::F, ::G, ::StaticInt{lo}, ::StaticInt{hi}) where {F, G, lo, hi} in Main at REPL[9]:1
Static Parameters
  F = typeof(f)
  G = typeof(g)
  lo = 0
  hi = 100
1 ─     return static(19)

Is it plausible to do something like to auto-tune “switch points” for a particular machine? Would this break horribly? Is there a better way to do this?

EDIT: Better to return a ::Val
EDIT2: Better still, Static.StaticInt

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I would do something less fancy, like use a Scratch space to persist the values between sessions, and load/generate the values at __init__ time (or in an explicit tune! function call the user can do) and put them in a const Ref or something like that and just grab the value out of it at runtime. If you really needed it at compile time (eg to avoid dynamic dispatch with static arrays) you could @eval a 0-arg function instead of a Ref (and rely on invalidations to recompile the downstream function calls).

Since generated functions are documented to not define how many times they will run the code-generation code, if tuning could be expensive I think it would be better to have a lot more control over it. But I personally don’t actually know if something worse could happen or not.

Thanks for the ideas @ericphanson . I think I do need it to be compile-time, since the whole point is for high-performance code with no dynamic dispatch. I don’t understand your "@eval a 0-arg function" suggestion. Do you mean this function would find the cutoff? I’d think this would lead to overhead each time it’s called, but I’m probably missing the point.

No, I meant treating it like a Ref. So I was thinking of something like

function tune!()
    # find cutoff `c`
    @eval get_cutoff() = $c

function choose_implementation(v)
    if length(v) < get_cutoff()

But I don’t actually know $c works here this way, maybe something more is needed (maybe a let block to not capture the reference to the variable c but just the value?).

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julia> function tune!(f, g, lo::Int, hi::Int)
           c = bisect(f, g, lo, hi)
           @eval get_cutoff() = StaticInt{$c}()
tune! (generic function with 1 method)

julia> tune!(f, g, 0, 100)
get_cutoff (generic function with 1 method)

julia> get_cutoff()

julia> @code_lowered get_cutoff()
1 ─ %1 = Core.apply_type(Main.StaticInt, 19)
│   %2 = (%1)()
└──      return %2
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Oh, but there’s a wrinkle… To use this in generated functions, it will have to be defined before they are.

Ah, hm. What if instead of using tune!, you just do the bisection at top-level in a let block and close over c in making get_cutoff()?

    c = …
    @eval get_cutoff() = StaticInt{$c}()

Then you can’t redefine it later but generated functions wouldn’t like that anyway.

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That might do it!

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