SPECK Counter-based RNG¶

class randomgen.speck128.SPECK128(seed=None, *, counter=None, key=None, rounds=34, mode='sequence')¶

Container for the SPECK (128 x 256) pseudo-random number generator.

Parameters:¶

seed=None¶

Entropy initializing the pseudo-random number generator. Can be an integer in [0, 2**256), a SeedSequence instance or None (the default). If seed is None, then data is read from /dev/urandom (or the Windows analog) if available. If unavailable, a hash of the time and process ID is used.

counter=None¶

Counter to use in the SPECK128 state. Can be either a Python int in [0, 2**128) or a 2-element uint64 array. If not provided, the counter is initialized at 0.

key=None¶

Key to use in the SPECK128 state. Unlike seed, which is run through another RNG before use, the value in key is directly set. Can be either a Python int in [0, 2**256) or a 4-element uint64 array. key and seed cannot both be used.

rounds=34¶

Number of rounds of the SPECK algorithm to run. The default value 34 is the official value used in encryption. Reduced-round variant might (untested) perform well statistically with improved performance.

mode='sequence'¶

Deprecated parameter. Do not use.

lock¶

Lock instance that is shared so that the same bit git generator can be used in multiple Generators without corrupting the state. Code that generates values from a bit generator should hold the bit generator’s lock.

Type:¶

threading.Lock

seed_seq¶

The SeedSequence instance used to initialize the generator if mode is “sequence” or is seed is a SeedSequence.

Type:¶

{None, SeedSequence}

Notes

SPECK128 is a 64-bit PRNG that uses a counter-based design based on the SPECK-128 cryptographic function [1]. Instances using different values of the key produce distinct sequences. SPECK128 has a large period \(2^{129}\) and supports arbitrary advancing and jumping the sequence in increments of \(2^{64}\). These features allow multiple non-overlapping sequences to be generated.

SPECK128 provides a capsule containing function pointers that produce doubles, and unsigned 32 and 64- bit integers. These are not directly consumable in Python and must be consumed by a Generator or similar object that supports low-level access.

See AESCounter, Philox and ThreeFry for a related counter-based PRNG.

State and Seeding

The SPECK128 state vector consists of a 12-element array of uint64 values that capture buffered draws from the distribution, a 34-element array of uint64s holding the round key, and an 12-element array of uint64 that holds the 128-bit counters (6 by 128 bits). The offset varies between 0 and 96 and shows the location in the buffer of the next 64 bits.

SPECK128 is seeded using either a single 256-bit unsigned integer or a vector of 4 64-bit unsigned integers. In either case, the seed is used as an input for a second random number generator, SplitMix64, and the output of this PRNG function is used as the initial state. Using a single 64-bit value for the seed can only initialize a small range of the possible initial state values.

Parallel Features

SPECK128 can be used in parallel applications by calling the jump method to advances the state as-if \(2^{64}\) random numbers have been generated. Alternatively, advance can be used to advance the counter for any positive step in [0, 2**128). When using jump, all generators should be initialized with the same seed to ensure that the segments come from the same sequence.

>>> from numpy.random import Generator
>>> from randomgen import SPECK128
>>> rg = [Generator(SPECK128(1234)) for _ in range(10)]
# Advance each SPECK128 instances by i jumps
>>> for i in range(10):
...     rg[i].bit_generator.jump(i)

Alternatively, SPECK128 can be used in parallel applications by using a sequence of distinct keys where each instance uses different key.

>>> key = 2**93 + 2**65 + 2**33 + 2**17 + 2**9
>>> rg = [Generator(SPECK128(key=key+i)) for i in range(10)]

Compatibility Guarantee

SPECK128 makes a guarantee that a fixed seed and will always produce the same random integer stream.

Examples

>>> from numpy.random import Generator
>>> from randomgen import SPECK128
>>> rg = Generator(SPECK128(1234))
>>> rg.standard_normal()
0.123  # random

References

Seeding and State¶

Parallel generation¶

Extending¶

Testing¶