blake2b_simd

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An implementation of the BLAKE2b hash with:

• 100% stable Rust.
• A fast AVX2 implementation ported from libsodium.
• A portable, safe implementation for other platforms.
• Dynamic CPU feature detection. Binaries for x86 include the AVX2 implementation by default and call it if the processor supports it at runtime.
• no_std support. std is on by default, for feature detection and std::io::Write.
• All the features from the the BLAKE2 spec, like adjustable length, keying, and associated data for tree hashing.

Example

```rust use blake2b_simd::{blake2b, Params};

assert!(blake2b(b"foo") == blake2b(b"foo")); assert!(blake2b(b"foo") != blake2b(b"bar"));

let hash = Params::new() .hashlength(16) .key(b"The Magic Words are Squeamish Ossifrage") .personal(b"L. P. Waterhouse") .tostate() .update(b"foo") .update(b"bar") .update(b"baz") .finalize(); asserteq!("ee8ff4e9be887297cf79348dc35dab56", &hash.tohex()); ```

Performance

The AVX2 implementation in this crate is ported from the C implementation in libsodium. That implementation was originally written by Samuel Neves and integrated into libsodium by Frank Denis. All credit for performance goes to those authors.

The benchmark_gig binary in this crate allocates a gigabyte (10⁹) array and repeatedly hashes it to measure throughput. A similar C program, benches/bench_libsodium.c, does the same thing using libsodium's implementation of BLAKE2b. Here are the results from my laptop:

• Intel Core i5-8250U, Arch Linux, kernel version 4.17.13
• libsodium version 1.0.16, gcc 8.2.0, gcc -O3 -lsodium benches/bench_libsodium.c (via the helper script benches/bench_libsodium.sh)
• rustc 1.30.0-nightly (73c78734b 2018-08-05), cargo +nightly run --release --bin benchmark_gig

table ╭────────────┬────────────╮ │ portable │ AVX2 │ ╭──────────────┼────────────┼────────────┤ │ blake2b_simd │ 0.771 GB/s │ 1.005 GB/s │ │ libsodium │ 0.743 GB/s │ 0.939 GB/s │ ╰──────────────┴────────────┴────────────╯

The b2sum sub-crate is a clone of the b2sum utility from coreutils. The benches/bench_b2sum.py script runs it against several coreutils hashes, on a 10 MB file of random data. Here are the results from my laptop:

table ╭───────────────────────────┬────────────╮ │ blake2b_simd b2sum --mmap │ 0.676 GB/s │ │ blake2b_simd b2sum │ 0.649 GB/s │ │ coreutils sha1sum │ 0.628 GB/s │ │ coreutils b2sum │ 0.536 GB/s │ │ coreutils md5sum │ 0.476 GB/s │ │ coreutils sha512sum │ 0.464 GB/s │ ╰───────────────────────────┴────────────╯