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1.1.1 Nov 17, 2023

#119 in Data structures

Download history 16/week @ 2023-11-06 204/week @ 2023-11-13 148/week @ 2023-11-20 151/week @ 2023-11-27 100/week @ 2023-12-04

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Used in 2 crates

MIT license

711 lines


Build & Test

GxHash is a blazingly fast and robust non-cryptographic hashing algorithm.


cargo add gxhash

Used directly as a hash function:

let bytes: &[u8] = "hello world".as_bytes();
let seed = 1234;
println!(" 32-bit hash: {:x}", gxhash::gxhash32(&bytes, seed));
println!(" 64-bit hash: {:x}", gxhash::gxhash64(&bytes, seed));
println!("128-bit hash: {:x}", gxhash::gxhash128(&bytes, seed));

Used in HashMap/HashSet:

// Type alias for HashSet::<String, GxBuildHasher>
let mut hashset = gxhash::GxHashSet::default();
hashset.insert("hello world");


Blazingly Fast 🚀

Up to this date, GxHash is the fastest non-cryptographic hashing algorithm of its class, for all input sizes. This performance is possible mostly thanks to heavy usage of SIMD intrinsics, high ILP construction and a small bytecode (easily inlined and cached).
See the benchmarks.

Highly Robust 🗿

GxHash uses several rounds of hardware-accelerated AES block cipher for efficient bit mixing.
Thanks to this, GxHash passes all SMHasher tests, which is the de facto quality benchmark for non-cryptographic hash functions, gathering most of the existing algorithms. GxHash has low collisions, uniform distribution and high avalanche properties.

Check out the paper for more technical details.


Architecture Compatibility

GxHash is compatible with:

  • X86 processors with AES-NI intrinsics
  • ARM processors with NEON intrinsics

Warning Other platforms are currently not supported (there is no fallback). The behavior on these platforms is undefined.

Hashes Stability

All generated hashes for a given version of GxHash are stable, meaning that for a given input the output hash will be the same across all supported platforms. An exception to this is the AVX2 version of GxHash (nightly).


To run the benchmarks locally use one of the following:

# Benchmark throughput
cargo bench --bench throughput
# Benchmark performance of GxHash's Hasher when used in a HashSet
cargo bench --bench hashset
# Benchmark throughput and get output as a markdown table
cargo bench --bench throughput --features bench-md
# Benchmark throughput and get output as .svg plots
cargo bench --bench throughput --features bench-plot

GxHash is continuously benchmarked on X86 and ARM Github runners.

Lastest Benchmark Results:
aarch64 x86_64 x86_64-avx2


DOS Resistance

GxHash is a seeded hashing algorithm, meaning that depending on the seed used, it will generate completely different hashes. The default HasherBuilder (GxHasherBuilder::default()) uses seed randomization, making any HashMap/HashSet more DOS resistant, as it will make it much more difficult for attackers to be able to predict which hashes may collide without knowing the seed used. This does not mean however that it is completely DOS resistant. This has to be analyzed further.

Multicollisions Resistance

GxHash uses a 128-bit internal state (and even 256-bit with the avx2 feature). This makes GxHash a widepipe construction when generating hashes of size 64-bit or smaller, which had amongst other properties to be inherently more resistant to multicollision attacks. See this paper for more details.

Cryptographic Properties

GxHash is a non-cryptographic hashing algorithm, thus it is not recommended to use it as a cryptographic algorithm (it is not a replacement for SHA). It has not been assessed if GxHash is preimage resistant and how difficult it is to be reversed.


  • Feel free to submit PRs
  • Repository is entirely usable via cargo commands
  • Versioning is the following
    • Major for stability breaking changes (output hashes for a same input are different after changes)
    • Minor for API changes/removal
    • Patch for new APIs, bug fixes and performance improvements

ℹ️ cargo-asm is an easy way to view the actual generated assembly code (cargo asm gxhash::gxhash::gxhash64) (method #[inline] should be removed otherwise it won't be seen by the tool)
ℹ️ AMD μProf gives some useful insights on time spent per instruction.


Author note: I'm committed to the open dissemination of scientific knowledge. In an era where access to information is more democratized than ever, I believe that science should be freely available to all – both for consumption and contribution. Traditional scientific journals often involve significant financial costs, which can introduce biases and can shift the focus from purely scientific endeavors to what is currently trendy.

To counter this trend and to uphold the true spirit of research, I have chosen to share my work on "gxhash" directly on GitHub, ensuring that it's openly accessible to anyone interested. Additionally, the use of a free Zenodo DOI ensures that this research is citable and can be referenced in other works, just as traditional publications are.

I strongly believe in a world where science is not behind paywalls, and I am in for a more inclusive, unbiased, and open scientific community.


Cite this publication / algorithm: