hamming-bitwise-fast-1.0.0/.cargo_vcs_info.json0000644000000001360000000000100150510ustar { "git": { "sha1": "a36c02294b998488b607becc21484dc4f47b60fe" }, "path_in_vcs": "" }hamming-bitwise-fast-1.0.0/.gitignore000064400000000000000000000000101046102023000156200ustar 00000000000000/target hamming-bitwise-fast-1.0.0/Cargo.toml0000644000000030000000000000100130400ustar # THIS FILE IS AUTOMATICALLY GENERATED BY CARGO # # When uploading crates to the registry Cargo will automatically # "normalize" Cargo.toml files for maximal compatibility # with all versions of Cargo and also rewrite `path` dependencies # to registry (e.g., crates.io) dependencies. # # If you are reading this file be aware that the original Cargo.toml # will likely look very different (and much more reasonable). # See Cargo.toml.orig for the original contents. [package] edition = "2021" name = "hamming-bitwise-fast" version = "1.0.0" authors = ["Evan Schwartz"] build = false autolib = false autobins = false autoexamples = false autotests = false autobenches = false description = "A fast, zero-dependency implementation of bitwise Hamming Distance using a method amenable to auto-vectorization." documentation = "https://docs.rs/hamming-bitwise-fast" readme = "README.md" license = "MIT OR Apache-2.0" repository = "https://github.com/emschwartz/hamming-bitwise-fast" [lib] name = "hamming_bitwise_fast" path = "src/lib.rs" [[bench]] name = "hamming" path = "benches/hamming.rs" harness = false [dev-dependencies.bitarray] version = "0.10.0" [dev-dependencies.criterion] version = "0.4.0" features = ["html_reports"] [dev-dependencies.hamming] version = "0.1.3" [dev-dependencies.hex] version = "0.4.3" [dev-dependencies.simsimd] version = "6.2.1" [target.'cfg(target_arch = "x86")'.dev-dependencies.hamming_rs] version = "0.2.22" [target.'cfg(target_arch = "x86_64")'.dev-dependencies.hamming_rs] version = "0.2.22" hamming-bitwise-fast-1.0.0/Cargo.toml.orig000064400000000000000000000013401046102023000165260ustar 00000000000000[package] name = "hamming-bitwise-fast" description = "A fast, zero-dependency implementation of bitwise Hamming Distance using a method amenable to auto-vectorization." version = "1.0.0" edition = "2021" authors = ["Evan Schwartz"] license = "MIT OR Apache-2.0" repository = "https://github.com/emschwartz/hamming-bitwise-fast" documentation = "https://docs.rs/hamming-bitwise-fast" [dev-dependencies] criterion = { version = "0.4.0", features = ["html_reports"] } hex = "0.4.3" bitarray = "0.10.0" hamming = "0.1.3" simsimd = "6.2.1" [target.'cfg(target_arch = "x86")'.dev-dependencies] hamming_rs = "0.2.22" [target.'cfg(target_arch = "x86_64")'.dev-dependencies] hamming_rs = "0.2.22" [[bench]] name = "hamming" harness = false hamming-bitwise-fast-1.0.0/LICENSE-APACHE000064400000000000000000000261201046102023000155660ustar 00000000000000 Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2024 Evan Schwartz Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. hamming-bitwise-fast-1.0.0/LICENSE-MIT000064400000000000000000000016051046102023000152770ustar 00000000000000MIT License Copyright 2024 Evan Schwartz Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. hamming-bitwise-fast-1.0.0/README.md000064400000000000000000000044001046102023000151160ustar 00000000000000# Hamming Bitwise Fast > A fast, zero-dependency implementation of bitwise Hamming Distance using > a method amenable to auto-vectorization. This started out as a benchmark of various bitwise Hamming distance implementations in Rust. However, after finding that a simple implementation that is amenable to auto-vectorization was comparable, if not faster, than other implementations, I decided to publish it as a crate. **Note:** This is for comparing bit-vectors, _not_ for comparing strings. ## Usage ```rust use hamming_bitwise_fast::hamming_bitwise_fast; assert_eq!(hamming_bitwise_fast(&[0xFF; 1024], &[0xFF; 1024]), 0); assert_eq!(hamming_bitwise_fast(&[0xFF; 1024], &[0x00; 1024]), 1024); ``` ## Benchmarks This uses [Criterion](https://github.com/bheisler/criterion.rs) to benchmark various Hamming distance implementations: - The auto-vectorized implementation in this crate - A naive for-loop based implementation - A naive iterator based implementation - [`hamming`](https://crates.io/crates/hamming) ![hamming](https://img.shields.io/crates/d/hamming) - [`hamming_rs`](https://crates.io/crates/hamming_rs) ![hamming_rs](https://img.shields.io/crates/d/hamming_rs) - [`simsimd`](https://crates.io/crates/simsimd) ![simsimd](https://img.shields.io/crates/d/simsimd) ### Running the benchmark ```sh cargo bench ``` Then open the `target/criterion/report/index.html` file in your browser to view the results. ### Results These were the results running on 3 different types of machines: ### 2023 MacBook Pro M2 Max ![Benchmark results](results/line-chart-macbook.svg) ![Benchmark results](results/violin-chart-macbook.svg) ### Linode 2 CPU 4GB ![Benchmark results](results/line-chart-linode.svg) ![Benchmark results](results/violin-chart-linode.svg) ### Fly.io 2 CPU 4GB ![Benchmark results](results/line-chart-fly.svg) ![Benchmark results](results/violin-chart-fly.svg) ## License This project is licensed under either of the following licenses, at your option: - [Apache License, Version 2.0](LICENSE-APACHE) - [MIT License](LICENSE-MIT) Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions. hamming-bitwise-fast-1.0.0/benches/hamming.rs000064400000000000000000000035041046102023000172400ustar 00000000000000use criterion::{criterion_group, criterion_main, Bencher, BenchmarkId, Criterion}; use hamming_bitwise_fast::*; const BIT_SIZES: [usize; 4] = [512, 768, 1024, 2048]; fn distance_bench u64>( mut f: F, ) -> impl FnMut(&mut Bencher, &usize) { move |b, size| { let data = vec![0xFF; *size / 8]; b.iter(|| { let d1 = criterion::black_box(&data); let d2 = criterion::black_box(&data); f(d1, d2) }) } } fn bench_hamming(c: &mut Criterion) { let mut group = c.benchmark_group("hamming"); for size in BIT_SIZES { group.bench_with_input( BenchmarkId::new("hamming-bitwise-fast", size), &size, distance_bench(|x, y| hamming_bitwise_fast(x, y) as u64), ); group.bench_with_input( BenchmarkId::new("naive", size), &size, distance_bench(naive_hamming_distance), ); group.bench_with_input( BenchmarkId::new("naive_iter", size), &size, distance_bench(naive_hamming_distance_iter), ); group.bench_with_input( BenchmarkId::new("hamming", size), &size, distance_bench(|x, y| hamming::distance_fast(x, y).unwrap()), ); #[cfg(any(target_arch = "x86", target_arch = "x86_64"))] group.bench_with_input( BenchmarkId::new("hamming_rs", size), &size, distance_bench(hamming_rs::distance_faster), ); group.bench_with_input( BenchmarkId::new("simsimd", size), &size, distance_bench(|x, y| simsimd::BinarySimilarity::hamming(x, y).unwrap() as u64), ); } group.finish(); } criterion_group!(benches, bench_hamming); criterion_main!(benches); hamming-bitwise-fast-1.0.0/results/line-chart-fly.svg000064400000000000000000000662651046102023000207200ustar 00000000000000 Gnuplot Produced by GNUPLOT 5.4 patchlevel 4 0 20 40 60 80 100 120 140 160 180 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 Average time (ns) Input hamming_bitwise_fast hamming_bitwise_fast gnuplot_plot_2 hamming hamming gnuplot_plot_4 hamming_rs hamming_rs gnuplot_plot_6 naive naive gnuplot_plot_8 naive_iter naive_iter gnuplot_plot_10 simsimd simsimd gnuplot_plot_12 hamming: Comparison hamming-bitwise-fast-1.0.0/results/line-chart-linode.svg000064400000000000000000000645071046102023000213750ustar 00000000000000 Gnuplot Produced by GNUPLOT 6.0 patchlevel 0 0 20 40 60 80 100 120 140 160 180 400 600 800 1000 1200 1400 1600 1800 2000 2200 hamming_bitwise_fast hamming_bitwise_fast gnuplot_plot_2 hamming hamming gnuplot_plot_4 hamming_rs hamming_rs gnuplot_plot_6 naive naive gnuplot_plot_8 naive_iter naive_iter gnuplot_plot_10 simsimd simsimd gnuplot_plot_12 Average time (ns) Input hamming: Comparison hamming-bitwise-fast-1.0.0/results/line-chart-macbook.svg000064400000000000000000000576351046102023000215420ustar 00000000000000 Gnuplot Produced by GNUPLOT 6.0 patchlevel 1 4 6 8 10 12 14 16 18 20 400 600 800 1000 1200 1400 1600 1800 2000 2200 hamming_bitwise_fast hamming_bitwise_fast gnuplot_plot_2 hamming hamming gnuplot_plot_4 naive naive gnuplot_plot_6 naive_iter naive_iter gnuplot_plot_8 simsimd simsimd gnuplot_plot_10 Average time (ns) Input hamming: Comparison hamming-bitwise-fast-1.0.0/results/violin-chart-fly.svg000064400000000000000000013046701046102023000212650ustar 00000000000000 Gnuplot Produced by GNUPLOT 5.4 patchlevel 4 hamming/simsimd/2048 hamming/simsimd/1024 hamming/simsimd/768 hamming/simsimd/512 hamming/naive_iter/2048 hamming/naive_iter/1024 hamming/naive_iter/768 hamming/naive_iter/512 hamming/naive/2048 hamming/naive/1024 hamming/naive/768 hamming/naive/512 hamming/hamming_rs/2048 hamming/hamming_rs/1024 hamming/hamming_rs/768 hamming/hamming_rs/512 hamming/hamming/2048 hamming/hamming/1024 hamming/hamming/768 hamming/hamming/512 hamming/hamming_bitwise_fast/2048 hamming/hamming_bitwise_fast/1024 hamming/hamming_bitwise_fast/768 hamming/hamming_bitwise_fast/512 0 50 100 150 200 250 Input Average time (ns) PDF PDF gnuplot_plot_2 gnuplot_plot_3 gnuplot_plot_4 gnuplot_plot_5 gnuplot_plot_6 gnuplot_plot_7 gnuplot_plot_8 gnuplot_plot_9 gnuplot_plot_10 gnuplot_plot_11 gnuplot_plot_12 gnuplot_plot_13 gnuplot_plot_14 gnuplot_plot_15 gnuplot_plot_16 gnuplot_plot_17 gnuplot_plot_18 gnuplot_plot_19 gnuplot_plot_20 gnuplot_plot_21 gnuplot_plot_22 gnuplot_plot_23 gnuplot_plot_24 hamming: Violin plot hamming-bitwise-fast-1.0.0/results/violin-chart-linode.svg000064400000000000000000013050031046102023000217340ustar 00000000000000 Gnuplot Produced by GNUPLOT 6.0 patchlevel 0 hamming/simsimd/2048 hamming/simsimd/1024 hamming/simsimd/768 hamming/simsimd/512 hamming/naive_iter/2048 hamming/naive_iter/1024 hamming/naive_iter/768 hamming/naive_iter/512 hamming/naive/2048 hamming/naive/1024 hamming/naive/768 hamming/naive/512 hamming/hamming_rs/2048 hamming/hamming_rs/1024 hamming/hamming_rs/768 hamming/hamming_rs/512 hamming/hamming/2048 hamming/hamming/1024 hamming/hamming/768 hamming/hamming/512 hamming/hamming_bitwise_fast/2048 hamming/hamming_bitwise_fast/1024 hamming/hamming_bitwise_fast/768 hamming/hamming_bitwise_fast/512 0 50 100 150 200 PDF PDF gnuplot_plot_2 gnuplot_plot_3 gnuplot_plot_4 gnuplot_plot_5 gnuplot_plot_6 gnuplot_plot_7 gnuplot_plot_8 gnuplot_plot_9 gnuplot_plot_10 gnuplot_plot_11 gnuplot_plot_12 gnuplot_plot_13 gnuplot_plot_14 gnuplot_plot_15 gnuplot_plot_16 gnuplot_plot_17 gnuplot_plot_18 gnuplot_plot_19 gnuplot_plot_20 gnuplot_plot_21 gnuplot_plot_22 gnuplot_plot_23 gnuplot_plot_24 Input Average time (ns) hamming: Violin plot hamming-bitwise-fast-1.0.0/results/violin-chart-macbook.svg000064400000000000000000011204251046102023000221000ustar 00000000000000 Gnuplot Produced by GNUPLOT 6.0 patchlevel 1 hamming/simsimd/2048 hamming/simsimd/1024 hamming/simsimd/768 hamming/simsimd/512 hamming/naive_iter/2048 hamming/naive_iter/1024 hamming/naive_iter/768 hamming/naive_iter/512 hamming/naive/2048 hamming/naive/1024 hamming/naive/768 hamming/naive/512 hamming/hamming/2048 hamming/hamming/1024 hamming/hamming/768 hamming/hamming/512 hamming/hamming_bitwise_fast/2048 hamming/hamming_bitwise_fast/1024 hamming/hamming_bitwise_fast/768 hamming/hamming_bitwise_fast/512 0 5 10 15 20 PDF PDF gnuplot_plot_2 gnuplot_plot_3 gnuplot_plot_4 gnuplot_plot_5 gnuplot_plot_6 gnuplot_plot_7 gnuplot_plot_8 gnuplot_plot_9 gnuplot_plot_10 gnuplot_plot_11 gnuplot_plot_12 gnuplot_plot_13 gnuplot_plot_14 gnuplot_plot_15 gnuplot_plot_16 gnuplot_plot_17 gnuplot_plot_18 gnuplot_plot_19 gnuplot_plot_20 Input Average time (ns) hamming: Violin plot hamming-bitwise-fast-1.0.0/src/lib.rs000064400000000000000000000065051046102023000155520ustar 00000000000000//! A fast, zero-dependency implementation of bitwise Hamming Distance using //! a method amenable to auto-vectorization. /// Calculate the bitwise Hamming distance between two byte slices. /// /// While this implementation does not explicitly use SIMD, it uses /// a technique that is amenable to auto-vectorization. Its performance /// is similar to or faster than more complex implementations that use /// explicit SIMD instructions for specific architectures. /// /// # Panics /// /// Panics if the two slices are not the same length. #[inline] pub fn hamming_bitwise_fast(x: &[u8], y: &[u8]) -> u32 { assert_eq!(x.len(), y.len()); // Process 8 bytes at a time using u64 let mut distance = x .chunks_exact(8) .zip(y.chunks_exact(8)) .map(|(x_chunk, y_chunk)| { // This is safe because we know the chunks are exactly 8 bytes. // Also, we don't care whether the platform uses little-endian or big-endian // byte order. Since we're only XORing values, we just care that the // endianness is the same for both. let x_val = u64::from_ne_bytes(x_chunk.try_into().unwrap()); let y_val = u64::from_ne_bytes(y_chunk.try_into().unwrap()); (x_val ^ y_val).count_ones() }) .sum::(); if x.len() % 8 != 0 { distance += x .chunks_exact(8) .remainder() .iter() .zip(y.chunks_exact(8).remainder()) .map(|(x_byte, y_byte)| (x_byte ^ y_byte).count_ones()) .sum::(); } distance } #[doc(hidden)] #[inline] pub fn naive_hamming_distance(x: &[u8], y: &[u8]) -> u64 { assert_eq!(x.len(), y.len()); let mut distance: u32 = 0; for i in 0..x.len() { distance += (x[i] ^ y[i]).count_ones(); } distance as u64 } #[doc(hidden)] #[inline] pub fn naive_hamming_distance_iter(x: &[u8], y: &[u8]) -> u64 { x.iter() .zip(y) .fold(0, |a, (b, c)| a + (*b ^ *c).count_ones()) as u64 } #[test] fn all_same_results() { let a ="cd8e98b29187133982909fc8b30e39c7b4dca73128ece9cf22ce64eefcf75a3adb0f129b1b00f63a20209e83cb873df707f1af6a4e3558941556b215461a9cbbbce984233c8b8a51e8bd2d1e7f6500caf59fb497440d15365b81e75d3ca4fc9947d5fcb97a0a7b5e44a6b93ee4f622c9b3157991fecac58f364b23f01fd8621e"; let b = "860e297e5ce51d3bee094b69bedaaf4ec5d74aa639fec1980ac8d6debb77ff8a323350ab4217867a2521d1248f878dc71f39ede3ea357ef39065da261f9ab470ce6884a3e8a6727d1a3c2614ab66481683f63c01de17b4f59d11659ab5a4310121fccc69418839ff6783f9ce7d760ac8e3db7824eef28d0f12fc6b3c1ef8d75c"; let a = hex::decode(a).unwrap(); let b = hex::decode(b).unwrap(); let expected = naive_hamming_distance(&a, &b); // Compare with naive_iter implementation assert_eq!(expected, naive_hamming_distance_iter(&a, &b)); // Compare with auto vectorized implementation assert_eq!(expected, hamming_bitwise_fast(&a, &b) as u64); // Compare with hamming crate assert_eq!(expected, hamming::distance_fast(&a, &b).unwrap()); // Compare with hamming_rs crate (x86/x86_64 only) #[cfg(any(target_arch = "x86", target_arch = "x86_64"))] assert_eq!(expected, hamming_rs::distance_faster(&a, &b)); // Compare with simsimd crate assert_eq!( expected, simsimd::BinarySimilarity::hamming(&a, &b).unwrap() as u64 ); }