Ryzen dot product performance

First of all, brilliant work on this package. Extremely impressive.

Since you mention in the readme that you were curious about 1 `fma` Ryzen chips, here's data from a Ryzen 2700X running Julia 1.4.0-rc2.0 on Windows 10 x64 and the latest release (v0.6.21) of the package:

```
# make sure we get enough samples
julia> BenchmarkTools.DEFAULT_PARAMETERS.samples = 1e12

julia> a = rand(256); b = rand(256);

julia> @btime mydot($a,$b)
33.098 ns (0 allocations: 0 bytes)
55.14639163783218

julia> @btime mydotavx($a,$b)
33.802 ns (0 allocations: 0 bytes)
55.14639163783219

julia> @btime myselfdot($a)
22.066 ns (0 allocations: 0 bytes)
79.1990129346761

julia> @btime myselfdotavx($a)
22.868 ns (0 allocations: 0 bytes)
79.1990129346761

julia> a = rand(255); b = rand(255);

julia> @btime mydot($a,$b)
43.749 ns (0 allocations: 0 bytes)
65.04807877890542

julia> @btime mydotavx($a,$b)
39.274 ns (0 allocations: 0 bytes)
65.04807877890542

julia> @btime myselfdot($a)
38.608 ns (0 allocations: 0 bytes)
80.52310652021393

julia> @btime myselfdotavx($a)
32.863 ns (0 allocations: 0 bytes)
80.52310652021396
```

So, the single load versions are always significantly faster, although the total throughput is always quite low compared to the numbers in your readme, which I assume came from a quad channel Skylake-X system or a comparable Xeon.

Let me know if you'd like any further info, whether the LLVM IR / native assembly, details of the hardware involved or for me to run any further benchmarks!

Comienza reproduciendo los benchmarks mydot, mydotavx, myselfdot y myselfdotavx reportados en el Ryzen 2700X indicado, usando Julia 1.4.0-rc2.0 y LoopVectorization.jl v0.6.21. Compara entradas de 255 y 256 elementos con las cifras de rendimiento de la README; se considera terminado cuando se explica el comportamiento del Ryzen o se identifica un problema de rendimiento accionable.

Escrito por el modelo de indexación a partir del texto del issue.