HashEvals is a Rust program that stress-tests hash functions for avalanche quality, integral collisions, and distribution skew across variable-length n-grams. The suite draws inspiration from the SMHasher family of benchmarks while keeping a lean codebase that is easy to extend with new hash primitives.
What It Tests?
- Avalanche behavior – flips every input bit and measures how many output bits change, tracking worst-case bias and per-bit variance. Ideally, flipping any input bit changes each output bit with 50% probability.
- Integer collisions – hashes random integers with only last
nbytes populated and watches for birthday-paradox collisions (forn ≤ 8). Representative of open-addressing hash tables with integer keys. - Distribution probe – runs large bucketed Chi² checks to highlight skewed output distributions. Representative of constructing bucketed hash tables or load balancers.
Each test operates over continuous random buffers generated with ChaCha20Rng so results are deterministic under the same input --seed.
Hashes returning 32-bit values (e.g. Crc32, RabinKarp32) still participate in the avalanche and distribution checks.
Function | Avg.Bias | Worst.Bias | Integral ⨳ | Chi² | Throughput
-------------+------------+------------+------------+----------+---------------
Blake3 | 0.15142 % | 3.75977 % | 42.347 % | 2021.527 | 582.1 MiB/s
SeaHash | 0.17826 % | 4.44336 % | 42.055 % | 2012.333 | 3525.7 MiB/s
SipHash | 0.19405 % | 4.88281 % | 41.682 % | 2010.550 | 2734.0 MiB/s
FoldHash | 0.19693 % | 4.88281 % | 42.379 % | 2022.245 | 10712.6 MiB/s
FarmHash | 0.19945 % | 5.07812 % | 42.112 % | 1985.036 | 6123.6 MiB/s
xxHash3 | 0.20226 % | 5.07812 % | 42.096 % | 2006.815 | 8122.3 MiB/s
gxHash | 0.21399 % | 5.41992 % | 41.964 % | 1988.415 | 1020.3 MiB/s
StringZilla | 0.21524 % | 5.51758 % | 41.932 % | 1996.037 | 10994.1 MiB/s
MurMur3 | 0.21968 % | 5.56641 % | 42.200 % | 1993.416 | 3914.6 MiB/s
aHash | 0.24295 % | 6.20117 % | 42.094 % | 1988.110 | 11371.3 MiB/s
FxHash | 1.86375 % | 6.92404 % | 42.130 % | 2022.595 | 11154.7 MiB/s
Crc32 | 15.87577 % | 37.50000 % | 42.828 % | 1315.053 | 2811.4 MiB/s
RabinKarp32 | 50.00000 % | 50.00000 % | 38.630 % | 1318.352 | 310.4 MiB/s Configuration:
Hash functions: StringZilla, SipHash, aHash, xxHash3, gxHash, Crc32, MurMur3, FarmHash, Blake3, FxHash, FoldHash, SeaHash, RabinKarp32
N-gram sizes: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 60, 100, 200, 300, 400, 500] bytes
Samples per size: 10'000'000 n-grams
Random seed: 42
Total avalanche tests: 15'040'000'000 bit flips per hash function
Optimization: For N > 8, randomly sample 64 unique bit positions to avoid quadratic complexity
Function | Avg.Bias | Worst.Bias | Integral ⨳ | Chi² | Throughput
-------------+------------+------------+------------+----------+--------------
Blake3 | 0.49842 % | 3.75977 % | 42.347 % | 4401.729 | 66.1 MiB/s
SeaHash | 0.59017 % | 4.44336 % | 42.055 % | 4353.642 | 791.8 MiB/s
SipHash | 0.64224 % | 4.88281 % | 41.682 % | 4330.098 | 479.7 MiB/s
FoldHash | 0.65240 % | 4.88281 % | 42.379 % | 4411.870 | 1533.8 MiB/s
FarmHash | 0.66107 % | 5.07812 % | 42.112 % | 4321.060 | 890.7 MiB/s
xxHash3 | 0.67025 % | 5.07812 % | 42.096 % | 4387.052 | 1373.7 MiB/s
gxHash | 0.70988 % | 5.41992 % | 41.964 % | 4302.142 | 1500.2 MiB/s
StringZilla | 0.71370 % | 5.51758 % | 41.932 % | 4305.659 | 1055.7 MiB/s
MurMur3 | 0.72892 % | 5.56641 % | 42.200 % | 4314.708 | 614.1 MiB/s
aHash | 0.80697 % | 6.20117 % | 42.094 % | 4275.918 | 1320.6 MiB/s
FxHash | 4.33818 % | 6.92404 % | 42.130 % | 4384.974 | 1652.4 MiB/s
Crc32 | 19.01042 % | 37.50000 % | 42.828 % | 1994.412 | 563.5 MiB/s
RabinKarp32 | 50.00000 % | 50.00000 % | 38.630 % | 1981.562 | 574.9 MiB/s Function | Avg.Bias | Worst.Bias | Chi² | Throughput
-------------+------------+------------+----------+--------------
SeaHash | 0.00469 % | 0.00545 % | 1034.607 | 1893.7 MiB/s
xxHash3 | 0.00500 % | 0.00627 % | 1012.462 | 4016.5 MiB/s
FarmHash | 0.00502 % | 0.00645 % | 1010.069 | 2688.5 MiB/s
FoldHash | 0.00506 % | 0.00715 % | 1013.351 | 4202.9 MiB/s
SipHash | 0.00520 % | 0.00638 % | 1044.576 | 1272.2 MiB/s
gxHash | 0.00523 % | 0.00723 % | 1011.157 | 4359.3 MiB/s
Blake3 | 0.00536 % | 0.00818 % | 1012.602 | 214.6 MiB/s
MurMur3 | 0.00544 % | 0.00682 % | 1017.549 | 1646.4 MiB/s
StringZilla | 0.00556 % | 0.00730 % | 1010.320 | 3470.7 MiB/s
aHash | 0.00576 % | 0.00676 % | 1018.854 | 3913.9 MiB/s
FxHash | 1.19828 % | 5.17577 % | 1017.994 | 4507.2 MiB/s
Crc32 | 14.78365 % | 20.31250 % | 1038.868 | 807.9 MiB/s
RabinKarp32 | 50.00000 % | 50.00000 % | 1033.444 | 497.3 MiB/s Function | Avg.Bias | Worst.Bias | Chi² | Throughput
-------------+------------+------------+----------+---------------
aHash | 0.00485 % | 0.00592 % | 1037.755 | 19606.7 MiB/s
MurMur3 | 0.00491 % | 0.00545 % | 1012.738 | 5598.8 MiB/s
StringZilla | 0.00495 % | 0.00589 % | 1052.259 | 21763.4 MiB/s
gxHash | 0.00512 % | 0.00605 % | 1020.838 | 921.4 MiB/s
SeaHash | 0.00513 % | 0.00626 % | 1008.996 | 4353.9 MiB/s
FarmHash | 0.00523 % | 0.00633 % | 982.769 | 8735.3 MiB/s
Blake3 | 0.00523 % | 0.00584 % | 1033.929 | 969.0 MiB/s
FoldHash | 0.00538 % | 0.00718 % | 1022.016 | 16155.2 MiB/s
SipHash | 0.00563 % | 0.00687 % | 1010.762 | 3673.6 MiB/s
xxHash3 | 0.00569 % | 0.00692 % | 987.596 | 10743.6 MiB/s
FxHash | 0.00635 % | 0.01340 % | 1049.392 | 16451.8 MiB/s
Crc32 | 14.06250 % | 18.75000 % | 1007.642 | 4796.7 MiB/s
RabinKarp32 | 50.00000 % | 50.00000 % | 1051.375 | 293.0 MiB/s Build and execute like any standard Cargo binary:
cargo run --release -- --list-hashes # list supported hash functions
cargo run --release -- --help # show CLI optionsTo run a very small sample set for a quick sanity check:
RUSTFLAGS="-C target-cpu=native" cargo run --release -- --samples 100 --verboseFor a proper comparison, consider running for 1 million samples:
RUSTFLAGS="-C target-cpu=native" cargo run --release -- --samples 1000000Add new hashers by implementing HashFunction trait in hash_functions.rs and pushing the boxed instance into get_all_hash_functions().
Each implementation exposes its display name, bit width, and hash(&[u8]) -> u64 method, which the test harness dispatches automatically across all metrics.
If you want to add a new stress-testing methodology, please open an issue or PR to discuss the design!