Xoroshiro128Plus slows down relative to Base MT with increasing sample size

From what I've read, Xoroshiro is often suggested as the fastest RNG with decent random properties. Using the implementation here, I do see it beating Base's MT for small samples, but getting relatively slower as the sample size passes ~100:

```
julia> r0 = Random.default_rng();

julia> r1 = RandomNumbers.Xorshifts.Xoroshiro128Plus(1);

julia> @btime rand($r0);
2.458 ns (0 allocations: 0 bytes)

julia> @btime rand($r1);
1.925 ns (0 allocations: 0 bytes)

julia> @btime rand($r0, 8);
41.771 ns (1 allocation: 144 bytes)

julia> @btime rand($r1, 8);
34.454 ns (1 allocation: 144 bytes)

julia> @btime rand($r0, 128);
171.495 ns (1 allocation: 1.14 KiB)

julia> @btime rand($r1, 128);
180.105 ns (1 allocation: 1.14 KiB)

julia> @btime rand($r0, 10^7);
6.606 ms (2 allocations: 76.29 MiB)

julia> @btime rand($r1, 10^7);
25.872 ms (2 allocations: 76.29 MiB)
```

Is this to be expected? Perhaps this information could be added to the docs.

```
julia> versioninfo()
Julia Version 1.5.0-DEV.803
Commit 8ab87d2205 (2020-05-03 08:07 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake)
Environment:
JULIA_NUM_THREADS = 8
```

Reproduce the Julia REPL benchmarks with @btime for the default RNG and Xoroshiro128Plus across the sample sizes shown, using the reported version information as context. Determine whether the increasing slowdown is expected, then document the result if appropriate; done means the performance behavior is explained or an actionable defect is identified.

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