hexhamming 1.3.1

Fast Hamming distance calculation for hexadecimal strings

What does it do?

This module performs a fast bitwise hamming distance of two hexadecimal strings.

This looks like:

DEADBEEF = 11011110101011011011111011101111
00000000 = 00000000000000000000000000000000
XOR      = 11011110101011011011111011101111
Hamming  = number of ones in DEADBEEF ^ 00000000 = 24

This essentially amounts to

>>> import gmpy
>>> gmpy.popcount(0xdeadbeef ^ 0x00000000)
24

except with Python strings, so

>>> import gmpy
>>> gmpy.popcount(int("deadbeef", 16) ^ int("00000000", 16))
24

A few assumptions are made and enforced:

• this is a valid hexadecimal string (i.e., [a-fA-F0-9]+)

• the strings are the same length

• the strings do not begin with "0x"

Why yet another Hamming distance library?

There are a lot of fantastic (python) libraries that offer methods to calculate various edit distances, including Hamming distances: Distance, textdistance, scipy, jellyfish, etc.

In this case, I needed a hamming distance library that worked on hexadecimal strings (i.e., a Python str) and performed blazingly fast. Furthermore, I often did not care about hex strings greater than 256 bits. That length constraint is different vs all the other libraries and enabled me to explore vectorization techniques via numba, numpy, and even SSE/AVX.

Lastly, I wanted to minimize dependencies, meaning you do not need to install numpy, gmpy, cython, pypy, pythran, etc.

Eventually, after playing around with gmpy.popcount, numba.jit, pythran.run, numpy, and AVX2, I decided to write what I wanted in a raw C header. At this point, I’m using raw char* and int*, so exploring re-writing this in Fortran makes little sense. Vectorization techniques also ended up adding more overhead due to converting the hex strings to vector-register-ingestible floats from char*.

Installation

To install, ensure you have Python 2.7 or 3.4+. Run

pip install hexhamming

or to install from source

git clone https://github.com/mrecachinas/hexhamming
cd hexhamming
python setup.py install # or pip install .

If you want to contribute to hexhamming, you should install the dev dependencies

pip install -r requirements-dev.txt

and make sure the tests pass with

pytest -vls

Example

To use the base C++ extension, you can simply run

>>> from hexhamming import hamming_distance
>>> hamming_distance("deadbeef", "00000000")
24

Benchmark

Note: For the below image, to show how optimized this is, I included the benchmark of a function that looks like

Below is a benchmark using pytest-benchmark with hexhamming==v1.3.0 my early 2016 1.2 GHz Intel m5 8 GB 1867 MHz LPDDR3 macOS Mojave (10.14.3) with Python 3.7.3 and Apple clang version 11.0.0 (clang-1100.0.33.17).

Name	Mean (ns)	Std (ns)	Median (ns)	Rounds	Iterations
test_hamming_distance_bench_short_same	182.21	282.187	140.1	137742	30
test_hamming_distance_bench_short	204.275	353.317	154.156	183723	32
test_hamming_distance_bench_long_same	431.369	553.671	329.5	132838	20
test_check_hexstrings_within_dist_bench	419.923	489.503	330.1	83718	20
test_hamming_distance_bench_256	649.275	2854.9	505	172118	1
test_hamming_distance_bench_long	3569.42	6408.05	2758	160591	1