Broadcast performance: fix type stability

[mbauman]

Remove a needless construction of a covariant typevar within a tuple. Tuples already are covariant.

Before:

julia> xset= 10:15
10:15

julia> yset= 12:14
12:14

julia> f(x,y) = sqrt( x^2 + y^2 )
f (generic function with 1 method)

julia> function loop3(xset, yset)
           m = Matrix{Float64}(undef, 3, length(xset)*length(yset))
           i = 1
           @inbounds for x in xset, y in yset
               m[:,i] .= (Float64(x), Float64(y), f(x,y))
               i += 1
           end
           m
       end
loop3 (generic function with 1 method)

julia> using BenchmarkTools

julia> @benchmark loop3($xset, $yset)
BenchmarkTools.Trial:
  memory estimate:  5.59 KiB
  allocs estimate:  109
  --------------
  minimum time:     9.634 μs (0.00% GC)
  median time:      10.504 μs (0.00% GC)
  mean time:        16.472 μs (32.25% GC)
  maximum time:     43.241 ms (99.95% GC)
  --------------
  samples:          10000
  evals/sample:     1

After:

julia> @benchmark loop3($xset, $yset)
BenchmarkTools.Trial:
  memory estimate:  544 bytes
  allocs estimate:  1
  --------------
  minimum time:     186.732 ns (0.00% GC)
  median time:      193.974 ns (0.00% GC)
  mean time:        217.001 ns (6.63% GC)
  maximum time:     69.051 μs (99.42% GC)
  --------------
  samples:          10000
  evals/sample:     616

[Sacha0]

Nice catch! :)

[ararslan]

Worth Nanosoldiering?

[mbauman]

Sure, I think this should show improvements in GoGame and Laplacian.

@nanosoldier runbenchmarks(ALL, vs = ":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here. cc @ararslan

[mbauman]

Looks like I'll need to add these examples to BaseBenchmarks.