celeritas::XorwowRngEngine Class Reference

Generate random data using the XORWOW algorithm. More...

#include <XorwowRngEngine.hh>

Public Types
Type aliases
using	uint_t = XorwowUInt
using	result_type = uint_t
using	Initializer_t = XorwowRngInitializer
using	ParamsRef = NativeCRef< XorwowRngParamsData >
using	StateRef = NativeRef< XorwowRngStateData >

Public Member Functions
CELER_FUNCTION	XorwowRngEngine (ParamsRef const &params, StateRef const &state, TrackSlotId tid)
	Construct from state and persistent data.
CELER_FUNCTION XorwowRngEngine &	operator= (Initializer_t const &)
	Initialize the RNG engine. More...
CELER_FUNCTION result_type	operator() ()
	Generate a 32-bit pseudorandom number using the 'xorwow' engine.
CELER_FUNCTION void	discard (ull_int count)
	Advance the state count times.

Static Public Member Functions
static CELER_CONSTEXPR_FUNCTION result_type	min ()
	Lowest value potentially generated.
static CELER_CONSTEXPR_FUNCTION result_type	max ()
	Highest value potentially generated.

Detailed Description

Generate random data using the XORWOW algorithm.

The XorwowRngEngine uses a C++11-like interface to generate random data. The sampling of uniform floating point data is done with specializations to the GenerateCanonical class.

The resize function for XorwowRngStateData will fully randomize the state at initialization. Alternatively, the state can be initialized with a seed, subsequence, and offset.

See Marsaglia (2003) for the theory underlying the algorithm and the the "example" xorwow that combines an xorshift output with a Weyl sequence (https://www.jstatsoft.org/index.php/jss/article/view/v008i14/916).

For a description of the jump ahead method using the polynomial representation of the recurrence, see: Haramoto, H., Matsumoto, M., Nishimura, T., Panneton, F., L’Ecuyer, P. 2008. "Efficient jump ahead for F2-linear random number generators". INFORMS Journal on Computing. https://pubsonline.informs.org/doi/10.1287/ijoc.1070.0251.

The jump polynomials were precomputed using https://github.com/celeritas-project/utils/blob/main/prng/xorwow-jump.py. For a more detailed description of how to calculate the jump polynomials using Knuth's square-and-multiply algorithm in \( O(k^2 log d) \) time (where k is the number of bits in the state and d is the number of steps to skip ahead), see: Collins, J. 2008. "Testing, Selection, and Implementation of Random Number Generators". ARL-TR-4498. https://apps.dtic.mil/sti/pdfs/ADA486637.pdf.

Member Function Documentation

operator=()

CELER_FUNCTION XorwowRngEngine & celeritas::XorwowRngEngine::operator= ( Initializer_t const &  init )

inline

Initialize the RNG engine.

This moves the state ahead to the given subsequence (a subsequence has size 2^67) and skips offset random numbers.

It is recommended to initialize the state using a very different generator from the one being initialized to avoid correlations. Here the 64-bit SplitMix64 generator is used for initialization (See Matsumoto, Wada, Kuramoto, and Ashihara, "Common defects in initialization of pseudorandom number generators". https://dl.acm.org/doi/10.1145/1276927.1276928.)

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The documentation for this class was generated from the following file:

• XorwowRngEngine.hh