Investigate (& report?) performances on JIT runtimes

[masklinn]

• test runtime on pypy is not great, this might be because of pure-python pyyaml being slow, -m ua_parser does not work for pypy #206 would probably help rule that out (or in)
• graal was added commented to GHA, but was even slower than pypy (graalpy 24 was just released: https://github.com/oracle/graalpython/releases/tag/graal-24.0.0)

[masklinn]

All benching done with samples/useragents.txt (75158 lines, 20322 unique).

NOTE: "legacy" has a clearing cache of size 200.

parser	cache (n=200)	cpython 3.12	pypy 7.3.17	graalpy 24.1.0
legacy	clearing	29.00s (386us/line)	156.56s (2083us/line)	106.12s (1412us/line)
basic		33.53s (446us/line)	221.14s (2942us/line)	117.09s (1558us/line)
basic	lru	29.19s (388us/line)	220.40s (2933us/line)	72.96s (971us/line)
basic	s3fifo	24.18s (322us/line)	146.93s (1955us/line)	55.65s (740us/line)
basic	sieve	24.37s (324us/line)	127.61s (1698us/line)	55.43s (737us/line)
regex		1.31s (17us/line)	1.47s (20us/line)	7.15s (95us/line)

A few observations:

• the regex engines of pypy and graal are dreary, but graal's showing is much better than expected¹
• pypy really doesn't like it the new impl: on cpython the LRU is sufficient to catch up² and on Graal to gain a 30% edge, but pypy is still 40% behind
• pypy also much prefers sieve, graal and cpython essentially don't care

Footnotes

1. important caveat, somehow Graal manages to work with a ton of concurrency, I assume the GC is concurrent but I don't understand what else it's doing: on my machine, on bench --bases legacy basic regex --cachesizes 200 --caches none lru s3fifo sieve, GraalPy times to 1175.69s user 8.45s system 283% cpu 6:57.58 total while pypy times to 925.28s user 3.32s system 100% cpu 15:27.14 total, so graal uses nearly 30% more CPU total but it nearly fully loads 3 cores, so ends up executing the test suite a bit more than twice as fast (some configurations go even higher e.g. basic with no cache basically runs at 400%) ↩

2. LRU should be better than the clearing cache at 200, but it makes sense that the layered approach of the new API would have some additional overhead, so coming out to a wash on cpython makes sense ↩