Performance of `gradient1` about interpolations.jl

[cortner]

.... indeed as I'm writing this it occured to me that ForwardDiff will probably get very good performance:

[ Info: And with FOrwardDiff
  14.570 ns (0 allocations: 0 bytes)

[mkitti]

Could you provide the code that generated the benchmark?

[cortner]

trying to reduce it to a short script, the weirdest thing happened:

using Interpolations, ForwardDiff 
rr = range(0.0, stop=5.0, length=10_000)
spl = cubic_spline_interpolation(rr, sin.(rr))
r = rand() * 5
print("Evaluation: "); @btime $spl($r)
print(" gradient1: "); @btime Interpolations.gradient1($spl, $r)
print("      Dual: "); @btime $spl(ForwardDiff.Dual($r, 1))

rr = range(0.0, stop=5.0, length=10_000)
spl = cubic_spline_interpolation(rr, sin.(rr))
r = rand() * 5
@btime $spl($r)
@btime Interpolations.gradient1($spl, $r)
@btime $spl(ForwardDiff.Dual($r, 1))

produces output

Evaluation:   8.717 ns (0 allocations: 0 bytes)
 gradient1:   12.345 ns (0 allocations: 0 bytes)
      Dual:   15.030 ns (0 allocations: 0 bytes)
  8.717 ns (0 allocations: 0 bytes)
  56.682 ns (0 allocations: 0 bytes)
  15.030 ns (0 allocations: 0 bytes)

Most likely a problem with benchmarking and not with Interpolations.jl, please close this if you agree.

[mkitti]

Try running with @benchmark to get more statistics.

[cortner]

with @benchmark the discrepancy does not occur. If you agree, we close this and I file an issue with BenchmarkTools

[mkitti]

That is intriguing. How so?

@btime reports the fastest time. @benchmark obviously provides more statistics. Perhaps more autodiff is occurring on the first run than the second run?

[cortner]

that would not explain it. The weird time is already the second run.

[mkitti]

Is the weird time the faster time?

[cortner]

No - the slow time. The fast time is consistent with what I'm expecting to see.

[cortner]

I cannot reproduce in latest versions.