Based on Wojciech Muła's "SIMD-friendly algorithms for substring searching"[0]
The two-way algorithm is Big-O efficient but it needs to preprocess the needle
to find a "criticla factorization" of it. This additional work is significant
for short needles. Additionally it mostly advances needle.len() bytes at a time.
The SIMD-based approach used here on the other hand can advance based on its
vector width, which can exceed the needle length. Except for pathological cases,
but due to being limited to small needles the worst case blowup is also small.
benchmarks taken on a Zen2:
```
16CGU, OLD:
test str::bench_contains_short_short ... bench: 27 ns/iter (+/- 1)
test str::bench_contains_short_long ... bench: 667 ns/iter (+/- 29)
test str::bench_contains_bad_naive ... bench: 131 ns/iter (+/- 2)
test str::bench_contains_bad_simd ... bench: 130 ns/iter (+/- 2)
test str::bench_contains_equal ... bench: 148 ns/iter (+/- 4)
16CGU, NEW:
test str::bench_contains_short_short ... bench: 8 ns/iter (+/- 0)
test str::bench_contains_short_long ... bench: 135 ns/iter (+/- 4)
test str::bench_contains_bad_naive ... bench: 130 ns/iter (+/- 2)
test str::bench_contains_bad_simd ... bench: 292 ns/iter (+/- 1)
test str::bench_contains_equal ... bench: 3 ns/iter (+/- 0)
1CGU, OLD:
test str::bench_contains_short_short ... bench: 30 ns/iter (+/- 0)
test str::bench_contains_short_long ... bench: 713 ns/iter (+/- 17)
test str::bench_contains_bad_naive ... bench: 131 ns/iter (+/- 3)
test str::bench_contains_bad_simd ... bench: 130 ns/iter (+/- 3)
test str::bench_contains_equal ... bench: 148 ns/iter (+/- 6)
1CGU, NEW:
test str::bench_contains_short_short ... bench: 10 ns/iter (+/- 0)
test str::bench_contains_short_long ... bench: 111 ns/iter (+/- 0)
test str::bench_contains_bad_naive ... bench: 135 ns/iter (+/- 3)
test str::bench_contains_bad_simd ... bench: 274 ns/iter (+/- 2)
test str::bench_contains_equal ... bench: 4 ns/iter (+/- 0)
```
[0] http://0x80.pl/articles/simd-strfind.html#sse-avx2
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