perf: add optimized BinaryViewArray comparison kernels

[orlp]

This should speed up our string comparisons quite a bit, especially for exact equality, and doubly so for exact equality with a constant small string.

[ritchie46]

Yeah buddy!

[orlp]

A sample benchmark for testing equality with a constant string. Input is 1000x mobydick.txt (200 million words):

import polars as pl
from timeit import timeit

with open("mobydick.txt") as f:
    words = f.read().split()

df = pl.DataFrame({"words": words * 1000})
print(df)

print(timeit(lambda: df.select((pl.col.words == "the").sum()), number=10))
print(timeit(lambda: df.select((pl.col.words == "and").sum()), number=10))
print(timeit(lambda: df.select((pl.col.words == "Captain Ahab").sum()), number=10))

print(timeit(lambda: df.select((pl.col.words < "the").sum()), number=10))
print(timeit(lambda: df.select((pl.col.words < "and").sum()), number=10))
print(timeit(lambda: df.select((pl.col.words < "Captain Ahab").sum()), number=10))

pairs = pl.DataFrame({
    "b": df["words"].slice(0, len(df) - 1),
    "a": df["words"].slice(1, len(df) - 1),
})

print(timeit(lambda: pairs.select((pl.col.a == pl.col.b).sum()), number=10))

Polars 0.20.5:

5.933478082995862
5.084461957914755
1.9035077500157058
8.33037916594185
7.153790874872357
5.330617999890819
3.9881368749774992

This PR:

2.0321385839488357
1.5042283749207854
1.5023376671597362
2.973445334006101
2.181169332936406
1.5706394580192864
1.2495929999276996