On Wed, 15 Mar 2023 12:37:24 GMT, Francesco Nigro <d...@openjdk.org> wrote:
>>> It seems reasonable to keep these two in sync, yes. (`CharacterData.of` >>> could even call into `StringLatin1.canEncode`, unless that's cause for some >>> performance anomaly) >> >> If I update `StringLatin1.canEncode` and call into that from >> `CharacterData.of`, I observe no regression for the Latin1 case, but a >> significant regression for the non-Latin1 case. I have no idea how to >> explain that: >> >> >> Benchmark (codePoint) Mode Cnt Score Error Units >> Characters.isDigit 48 avgt 15 0.675 ± 0.029 ns/op >> Characters.isDigit 1632 avgt 15 2.435 ± 0.032 ns/op > > Can you check what happen adding much more inputs to the dataset including > non-latin chars as well and use `-prof perfnorm` to check what `perf` report > re branches/branch-misses? > > You can use `SplittableRandom` to pre-populate an array of inputs which > sequence is "random" but still allow deterministic benchmarking and feed the > benchmark method by cycling the pre-computed inputs. > In the real world I expect `isDigit` to happen on different input types and > both having C2 with both branches places based on prev inputs distribution > and a confused branch-predictor to allow comparing vs something that looks a > bit nearest to the real world (TBD, I know). > I expect in that case that a single cmp + mask to work better depending on > latin input distribution/occurrence I created a randomized version of `Characters.isDigit` which tests with code points picked at random such that any category (Latin1, negative, different planes, unassiged) are equally probable. Baseline: Benchmark (codePoint) Mode Cnt Score Error Units Characters.isDigitRandom 1632 avgt 15 5.503 ± 0.371 ns/op Current PR: Benchmark (codePoint) Mode Cnt Score Error Units Characters.isDigitRandom 1632 avgt 15 5.393 ± 0.336 ns/op Using StringLatin1.canEncode: Benchmark (codePoint) Mode Cnt Score Error Units Characters.isDigitRandom 1632 avgt 15 5.377 ± 0.322 ns/op Seems the PR still has a small improvement for this scenario. The StringLatin1.canEncode regression disappears. In the real world ASCII/Latin1 seems to dominate most data, so this scenario is perhaps not very realistic. I'm running this on a Mac, so cannot try `-prof perfnorm`. ------------- PR: https://git.openjdk.org/jdk/pull/13040
