galois 0.0.12

A performant numpy extension for Galois fields

Galois: A performant numpy extension for Galois fields

• Motivation
• Documentation
• Installation
• Versioning
• Basic Usage
  • Array construction
  • Field arithmetic
  • Numpy functions
  • Numpy ufunc methods
  • Field element display modes
  • Polynomial construction
  • Polynomial arithmetic
• Performance
  • GF(31) addition speed test
• Acknowledgements

Motivation

The project goals are for galois to be:

• General: Support all Galois fields GF(p^m), even arbitrarily large fields!
• Accurate: Guarantee arithmetic accuracy -- tests against industry-standard mathematics software.
• Compatible: Seamlessly integrate with numpy arrays -- arithmetic operators (x + y), broadcasting, view casting, type casting, numpy functions, ufuncs, ufunc methods.
• Performant: Run as fast as numpy or C -- avoids the speed sinkhole of Python for loops.
• Reconfigurable: Dynamically optimize JIT-compiled code for performance based on data size and processor (single-core CPU, multi-core CPU, or GPU).

Documentation

Our documentation can be found at https://galois.readthedocs.io/en/stable/. The documentation includes installation instructions, development guide, basic usage, tutorials, and an API reference.

Installation

The latest version of galois can be installed from PyPI via pip.

python3 -m pip install galois

Versioning

This project uses semantic versioning. Releases are versioned major.minor.patch. Major releases introduce API-changing features. Minor releases add features and are backwards compatible with other releases in major.x.x. Patch releases fix bugs in a minor release and are backwards compatible with other releases in major.minor.x.

Releases before 1.0.0 are alpha and beta releases. Alpha releases are 0.0.alpha. There is no API compatibility guarantee for them. They can be thought of as 0.0.alpha-major. Beta releases are 0.beta.x and are API compatible. They can be thought of as 0.beta-major.beta-minor.

Basic Usage

Array construction

Construct Galois field array classes using the galois.GF() class factory function.

>>> import numpy as np

>>> import galois

>>> GF31 = galois.GF(31)

>>> print(GF31)
<class 'numpy.ndarray' over GF(31)>

>>> issubclass(GF31, np.ndarray)
True

Galois field array classes contain extra class attributes related to the finite field.

# The size of the finite field
>>> GF31.order
31

# A primitive element of the finite field
>>> GF31.alpha
GF(3, order=31)

# The primitive polynomial of the finite field
>>> GF31.prim_poly
Poly(x + 28, GF(31))

Create any Galois field array class type: GF(2^m), GF(p), or GF(p^m). Even arbitrarily-large fields!

# Field used in AES
>>> GF256 = galois.GF(2**8); print(GF256)
<class 'numpy.ndarray' over GF(2^8)>

>>> prime = 36893488147419103183; galois.is_prime(prime)
True

# Large prime field
>>> GFp = galois.GF(prime); print(GFp)
<class 'numpy.ndarray' over GF(36893488147419103183)>

# Large characteristic-2 field
>>> GF2_100 = galois.GF(2**100); print(GF2_100)
<class 'numpy.ndarray' over GF(2^100)>

Create arrays from existing numpy arrays, either explicitly or by view casting.

# Represents an existing numpy array
>>> array = np.random.randint(0, GF256.order, 10, dtype=int); array
array([ 71, 240, 210,  27, 124, 254,  13, 170, 221, 166])

# Explicit Galois field construction
>>> GF256(array)
GF([ 71, 240, 210,  27, 124, 254,  13, 170, 221, 166], order=2^8)

# Numpy view casting to a Galois field
>>> array.view(GF256)
GF([ 71, 240, 210,  27, 124, 254,  13, 170, 221, 166], order=2^8)

Field arithmetic

Here, GF is any Galois field array class created from galois.GF, x and y are GF arrays, and z is an integer numpy.ndarray. All arithmetic operations follow normal numpy broadcasting rules.

• Addition: x + y == np.add(x, y)
• Subtraction: x - y == np.subtract(x, y)
• Multiplication: x * y == np.multiply(x, y)
• Division: x / y == x // y == np.divide(x, y)
• Scalar multiplication: x * z == z * x, e.g. x * 3 == x + x + x
• Reciprocal: 1 / x == np.reciprocal(x)
• Exponentiation: x ** z == np.power(x, z)
• Logarithm base alpha: np.log(x), where alpha == GF.alpha
• COMING SOON: Logarithm base b: GF.log(x, b), where b is any field element
• COMING SOON: Matrix multiplication: x @ y = np.matmul(x, y)

Note

Generally, we don't allow Galois field array operations with scalars, i.e. x + 5 or x + z, even if 5 is a valid element in x's Galois field, or z's integers are elements too. We prefer explicit over implicit. Instead, the correct notation would be x + GF(5) and y = GF(y); x + y.

There are a couple exceptions: scalar multiplication and exponentiation.

For multiplication, x * y is interpreted as field multiplication. Whereas, x * 3, which is valid syntax, is interpreted as x + x + x. In prime fields, x * GF(3) == x * 3. In extension fields, x * GF(3) != x * 3 so be careful!

For exponentiation, x ** 3 or x ** -2 are valid. The exponent can be any integer, not just a field element.

Numpy functions

The galois package also supports linear algebra routines. They can be accessed using the natural numpy syntax.

• COMING SOON: np.inner
• COMING SOON: np.dot
• COMING SOON: np.tensordot

Numpy ufunc methods

Galois field arrays also support numpy ufunc methods. This allows you to apply a ufunc in a unique was across the target array.

The ufunc method signature is <ufunc>.<method>(*args, **kwargs). Below are the supported ufuncs and their methods.

• <ufunc>: np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.negative, np.power, np.square, np.log

• <method>: reduce, accumulate, reduceat, outer, at

Below is are examples of how to use the reduce and outer methods with the np.multiply ufunc.

>>> a = GF31.Random((2,5)); a
GF([[28, 30, 17, 21, 22],
    [23, 29, 23, 27, 17]], order=31)

>>> np.multiply.reduce(a, axis=0)
GF([24,  2, 19,  9,  2], order=31)

>>> x = GF256.Random(10); x
GF([118,  49, 122, 166, 136, 118,  53,  19, 233, 119], order=2^8)

>>> y = GF256.Random(10, low=1); y
GF([239,  63,  81, 225, 150,  12,  56,  24,  98, 245], order=2^8)

>>> np.multiply.outer(x, y)
GF([[231, 157, 137,  89, 159,  82, 194, 164,  70, 175],
    [ 21,  91, 218,  38,  52,  81, 204, 162, 208, 213],
    [ 87, 132,  98, 161,  57,   2, 255,   4, 228, 167],
    [126, 199, 230, 212, 184, 251, 146, 235, 218, 196],
    [161,  19,  93, 130,  24,  46, 140,  92,  96, 240],
    [231, 157, 137,  89, 159,  82, 194, 164,  70, 175],
    [142, 167, 131, 133,  86,  97,  44, 194,  69,  38],
    [122, 150, 138, 136,  50, 212, 239, 181, 200, 233],
    [167, 228,   7, 240, 215, 152,  65,  45, 123,  69],
    [  8, 162, 216, 184,   9,  94, 250, 188,  36,  90]], order=2^8)

Field element display modes

The user may display the finite field elements as either integers or polynomials.

>>> print(x)
GF([118,  49, 122, 166, 136, 118,  53,  19, 233, 119], order=2^8)

# Temporarily set the display mode to represent GF(p^m) field elements as polynomials over GF(p)[x].
>>> with GF256.display("poly"):
...     print(x)
GF([x^6 + x^5 + x^4 + x^2 + x, x^5 + x^4 + 1, x^6 + x^5 + x^4 + x^3 + x,
    x^7 + x^5 + x^2 + x, x^7 + x^3, x^6 + x^5 + x^4 + x^2 + x,
    x^5 + x^4 + x^2 + 1, x^4 + x + 1, x^7 + x^6 + x^5 + x^3 + 1,
    x^6 + x^5 + x^4 + x^2 + x + 1], order=2^8)

Polynomial construction

Construct Galois field polynomials.

# Construct a polynomial by specifying all the coefficients in descending-degree order
>>> p = galois.Poly([1, 22, 0, 17, 25], field=GF31); p
Poly(x^4 + 22x^3 + 17x + 25, GF(31))

# Construct a polynomial by specifying only the non-zero coefficients
>>> q = galois.Poly.Degrees([2, 0], coeffs=[4, 14], field=GF31); q
Poly(4x^2 + 14, GF(31))

Polynomial arithmetic

Galois field polynomial arithmetic is similar to numpy array operations.

>>> p + q
Poly(x^4 + 22x^3 + 4x^2 + 17x + 8, GF(31))

>>> p // q, p % q
(Poly(8x^2 + 21x + 3, GF(31)), Poly(2x + 14, GF(31)))

>>> p ** 2
Poly(x^8 + 13x^7 + 19x^6 + 3x^5 + 23x^4 + 15x^3 + 10x^2 + 13x + 5, GF(31))

Galois field polynomials can also be evaluated at constants or arrays.

>>> p
Poly(x^4 + 22x^3 + 17x + 25, GF(31))

>>> a
GF([[28, 30, 17, 21, 22],
    [23, 29, 23, 27, 17]], order=31)

# Evaluate a polynomial at a single value
>>> p(1)
GF(3, order=31)

# Evaluate a polynomial at an array of values
>>> p(a)
GF([[19, 18,  0,  7,  5],
    [ 6, 17,  6, 14,  0]], order=31)

Performance

GF(31) addition speed test

>>> import numpy as np
>>> import galois

>>> GFp = galois.GF(31)
>>> print(GFp)>
<class 'numpy.ndarray' over GF(31)>

>>> def construct_arrays(GF, N):
...     order = GF.order
...
...     a = np.random.randint(0, order, N, dtype=int)
...     b = np.random.randint(0, order, N, dtype=int)
...
...     ga = GF(a)
...     gb = GF(b)
...
...     return a, b, ga, gb, order

>>> def pure_python_add(a, b, modulus):
...     c = np.zeros(a.size, dtype=a.dtype)
...     for i in range(a.size):
...         c[i] = (a[i] + b[i]) % modulus
...     return c

>>> N = int(10e3)
... a, b, ga, gb, order = construct_arrays(GFp, N)
...
... print(f"Pure python addition in GF({order})")
... %timeit pure_python_add(a, b, order)
...
... print(f"\nNative numpy addition in GF({order})")
... %timeit (a + b) % order
...
... print(f"\n`galois` implementation of addition in GF({order})")
... %timeit ga + gb
Pure python addition in GF(31)
5.84 ms ± 218 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Native numpy addition in GF(31)
112 µs ± 14.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

`galois` implementation of addition in GF(31)
73.1 µs ± 746 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Acknowledgements

This package heavily relies on Numba and its just-in-time compiler for performance. We use Frank Luebeck's compilation of Conway polynomials for computing primitive polynomials for extension fields. We utilize SageMath for generating test vectors.