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[docs] theory on PCG random number generator (#3134)

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Co-authored-by: Matej Rzehulka <matej.rzehulka@suro.cz>
Co-authored-by: Paul Romano <paul.k.romano@gmail.com>
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43
docs/source/methods/random_numbers.rst
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@ -7,7 +7,7 @@ Random Number Generation
In order to sample probability distributions, one must be able to produce random
numbers. The standard technique to do this is to generate numbers on the
interval :math:`[0,1)` from a deterministic sequence that has properties that
make it appear to be random, e.g. being uniformly distributed and not exhibiting
make it appear to be random, e.g., being uniformly distributed and not exhibiting
correlation between successive terms. Since the numbers produced this way are
not truly "random" in a strict sense, they are typically referred to as
pseudorandom numbers, and the techniques used to generate them are pseudorandom
@ -15,6 +15,11 @@ number generators (PRNGs). Numbers sampled on the unit interval can then be
transformed for the purpose of sampling other continuous or discrete probability
distributions.
There are many different algorithms for pseudorandom number generation. OpenMC
currently uses `permuted congruential generator`_ (PCG), which builds on top of
the simpler linear congruential generator (LCG). Both algorithms are described
below.
------------------------------
Linear Congruential Generators
------------------------------
@ -37,8 +42,8 @@ be generated with a method chosen at random. Some theory should be used."
Typically, :math:`M` is chosen to be a power of two as this enables :math:`x
\mod M` to be performed using the bitwise AND operator with a bit mask. The
constants for the linear congruential generator used by default in OpenMC are
:math:`g = 2806196910506780709`, :math:`c = 1`, and :math:`M = 2^{63}` (see
`L'Ecuyer`_).
:math:`g = 2806196910506780709`, :math:`c = 1`, and :math:`M = 2^{63}` (from
`L'Ecuyer <https://doi.org/10.1090/S0025-5718-99-00996-5>`_).
Skip-ahead Capability
---------------------
@ -50,7 +55,8 @@ want to skip ahead :math:`N` random numbers and :math:`N` is large, the cost of
sampling :math:`N` random numbers to get to that position may be prohibitively
expensive. Fortunately, algorithms have been developed that allow us to skip
ahead in :math:`O(\log_2 N)` operations instead of :math:`O(N)`. One algorithm
to do so is described in a paper by Brown_. This algorithm relies on the following
to do so is described in a `paper by Brown
<https://www.osti.gov/biblio/976209>`_. This algorithm relies on the following
relationship:
.. math::
@ -58,15 +64,26 @@ relationship:
\xi_{i+k} = g^k \xi_i + c \frac{g^k - 1}{g - 1} \mod M
Note that equation :eq:`lcg-skipahead` has the same general form as equation :eq:`lcg`, so
the idea is to determine the new multiplicative and additive constants in
:math:`O(\log_2 N)` operations.
.. only:: html
.. rubric:: References
Note that equation :eq:`lcg-skipahead` has the same general form as equation
:eq:`lcg`, so the idea is to determine the new multiplicative and additive
constants in :math:`O(\log_2 N)` operations.
.. _L'Ecuyer: https://doi.org/10.1090/S0025-5718-99-00996-5
.. _Brown: https://laws.lanl.gov/vhosts/mcnp.lanl.gov/pdf_files/anl-rn-arb-stride.pdf
--------------------------------
Permuted Congruential Generators
--------------------------------
The `permuted congruential generator`_ (PCG) algorithm aims to improve upon the
LCG algorithm by permuting the output. The algorithm works on the basic
principle of first advancing the generator state using the LCG algorithm and
then applying a permutation function on the LCG state to obtain the output. This
results in increased statistical quality as measured by common statistical tests
while exhibiting a very small performance overhead relative to the LCG algorithm
and an equivalent memory footprint. For further details, see the original
technical report by `O'Neill
<https://www.pcg-random.org/pdf/hmc-cs-2014-0905.pdf>`_. OpenMC uses the
PCG-RXS-M-XS variant with a 64-bit state and 64-bit output.
.. _linear congruential generator: https://en.wikipedia.org/wiki/Linear_congruential_generator
.. _permuted congruential generator: https://en.wikipedia.org/wiki/Permuted_congruential_generator

3
src/random_lcg.cpp
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@ -17,7 +17,8 @@ constexpr uint64_t prn_stride {152917LL}; // stride between particles
//==============================================================================
// 64 bit implementation of the PCG-RXS-M-XS 64-bit state / 64-bit output
// geneator Adapted from: https://github.com/imneme/pcg-c
// geneator Adapted from: https://github.com/imneme/pcg-c, in particular
// https://github.com/imneme/pcg-c/blob/83252d9c23df9c82ecb42210afed61a7b42402d7/include/pcg_variants.h#L188-L192
// @techreport{oneill:pcg2014,
// title = "PCG: A Family of Simple Fast Space-Efficient Statistically Good
// Algorithms for Random Number Generation", author = "Melissa E. O'Neill",