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[alexxy/gromacs.git] / src / gromacs / random / tabulatednormaldistribution.h

/*
 * This file is part of the GROMACS molecular simulation package.
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 * Copyright (c) 2015,2016,2018,2019, by the GROMACS development team, led by
 * Mark Abraham, David van der Spoel, Berk Hess, and Erik Lindahl,
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/*! \file
 * \brief Tabulated normal distribution
 *
 * A very fast normal distribution, but with limited resolution.
 *
 * \author Erik Lindahl <erik.lindahl@gmail.com>
 * \inpublicapi
 * \ingroup module_random
 */

#ifndef GMX_RANDOM_TABULATEDNORMALDISTRIBUTION_H
#define GMX_RANDOM_TABULATEDNORMALDISTRIBUTION_H

#include <cmath>

#include <array>
#include <limits>

#include "gromacs/math/functions.h"
#include "gromacs/math/utilities.h"
#include "gromacs/utility/basedefinitions.h"
#include "gromacs/utility/classhelpers.h"
#include "gromacs/utility/gmxassert.h"
#include "gromacs/utility/real.h"

namespace gmx
{

namespace detail
{

//! Number of bits that determines the resolution of the lookup table for the normal distribution.
constexpr int c_TabulatedNormalDistributionDefaultBits = 14;

}

/*! \brief Tabulated normal random distribution
 *
 *  Random distribution compatible with C++11 distributions - it can be
 *  used with any C++11 random engine.
 *
 *  \tparam RealType  Type of the return value. Float or double. Note that
 *                    GROMACS uses "real" type by default in contrast to the C++11
 *                    standard library, to avoid double/float conversions.
 *  \tparam tableBits Size of the table, specified in bits. The storage
 *                    space required is sizeof(RealType)*2^tableBits. To
 *                    keep things sane this is limited to 24 bits.
 *
 *  Some stochastic integrators depend on drawing a lot of normal
 *  distribution random numbers quickly, but in many cases the only
 *  important property is the distribution - given the noise in forces
 *  we do not need very high resolution.
 *  This distribution uses an internal table to return samples from a
 *  normal distribution with limited resolution. By default the table
 *  uses c_TabulatedNormalDistributionDefaultBits bits, but this is
 *  specified with a template parameter.
 *
 *  Since this distribution only uses tableBits bits per value generated,
 *  the values draw from the random engine are used for several results.
 *  To make sure you get a reproducible result when using counter-based
 *  random engines (such as ThreeFry2x64), remember to call the reset()
 *  method to cancel the internal memory of the distribution.
 *
 *  \note For modern NUMA systems, you likely want to use separate
 *        distributions for each thread, and make sure they are initialized
 *        on the CPU where they will run, so the table is placed in that
 *        NUMA memory pool.
 *  \note The finite table resolution means this distribution will NOT
 *        return arbitrarily small/large values, but with e.g. 14 bits
 *        the results are limited to roughly +/- 4 standard deviations.
 */
template<class RealType = real, unsigned int tableBits = detail::c_TabulatedNormalDistributionDefaultBits>
class TabulatedNormalDistribution
{
    static_assert(tableBits <= 24, "Normal distribution table is limited to 24bits (64MB in single precision)");

    public:
        /*! \brief  Type of normal distribution results */
        typedef RealType result_type;

        /*! \brief  Normal distribution parameter class (mean and stddev) */
        class param_type
        {
            public:
                /*! \brief The type of distribution the parameters describe */
                typedef TabulatedNormalDistribution distribution_type;

                /*! \brief Constructor. Default is classical distr. with mean 0, stddev 1.
                 *
                 * \param mean     Expectation value.
                 * \param stddev   Standard deviation.
                 *
                 */
                explicit param_type(result_type mean = 0.0, result_type stddev = 1.0)
                    : mean_(mean), stddev_(stddev) {}

                /*! \brief Return mean parameter of normal distribution */
                result_type
                mean() const { return mean_; }

                /*! \brief Return standard deviation parameter of normal distribution */
                result_type
                stddev() const { return stddev_; }

                /*! \brief True if two sets of normal distributions parameters are identical
                 *
                 * \param x Instance to compare with.
                 */
                bool
                operator==(const param_type &x) const
                {
                    return (mean_ == x.mean_ && stddev_ == x.stddev_);
                }

                /*! \brief True if two sets of normal distributions parameters are different.
                 *
                 * \param x Instance to compare with.
                 */
                bool
                operator!=(const param_type &x) const { return !operator==(x); }

            private:
                /*! \brief Internal storage for mean of normal distribution */
                result_type mean_;
                /*! \brief Internal storage for standard deviation of normal distribution */
                result_type stddev_;
        };

        /*! \brief Fill the table with values for the normal distribution
         *
         *  This routine returns a new a std::array with the table data.
         *
         *  This routine is used to help construct objects of this class,
         *  and is exposed only to permit testing. Normal code should not
         *  need to call this function.
         */
        static
        std::array<RealType, 1<<tableBits>
        makeTable()
        {
            /* Fill the table with the integral of a gaussian distribution, which
             * corresponds to the inverse error function.
             * We avoid integrating a gaussian numerically, since that leads to
             * some loss-of-precision which also accumulates so it is worse for
             * larger indices in the table. */
            constexpr std::size_t            tableSize        = 1 << tableBits;
            constexpr std::size_t            halfSize         = tableSize/2;
            constexpr double                 invHalfSize      = 1.0/halfSize;

            std::array<RealType, tableSize>  table;

            // Fill in all but the extremal entries of the table
            for (std::size_t i = 0; i < halfSize-1; i++)
            {
                double r = (i + 0.5) * invHalfSize;
                double x = std::sqrt(2.0) * erfinv(r);

                table.at(halfSize-1-i) = -x;
                table.at(halfSize+i)   =  x;
            }
            // We want to fill in the extremal table entries with
            // values that make the total variance equal to 1, so
            // measure the variance by summing the squares of the
            // other values of the distribution, starting from the
            // smallest values.
            double sumOfSquares = 0;
            for (std::size_t i = 1; i < halfSize; i++)
            {
                double value = table.at(i);
                sumOfSquares += value * value;
            }
            double missingVariance = 1.0 - 2.0*sumOfSquares/tableSize;
            GMX_RELEASE_ASSERT(missingVariance > 0, "Incorrect computation of tabulated normal distribution");
            double extremalValue   = std::sqrt(0.5*missingVariance*tableSize);
            table.at(0)  = -extremalValue;
            table.back() = extremalValue;

            return table;
        }

    public:

        /*! \brief Construct new normal distribution with specified mean & stdddev.
         *
         *  \param mean    Mean value of tabulated normal distribution
         *  \param stddev  Standard deviation of tabulated normal distribution
         */
        explicit TabulatedNormalDistribution(result_type mean = 0.0, result_type stddev = 1.0 )
            : param_(param_type(mean, stddev)), savedRandomBits_(0), savedRandomBitsLeft_(0)
        {
        }

        /*! \brief Construct new normal distribution from parameter type.
         *
         *  \param param Parameter class containing mean and standard deviation.
         */
        explicit TabulatedNormalDistribution(  const param_type &param )
            : param_(param), savedRandomBits_(0), savedRandomBitsLeft_(0)
        {
        }

        /*! \brief Smallest value that can be generated in normal distrubiton.
         *
         * \note The smallest value is not -infinity with a table, but it
         *       depends on the table resolution. With 14 bits, this is roughly
         *       four standard deviations below the mean.
         */
        result_type
        min() const
        {
            return c_table_[0];
        }

        /*! \brief Largest value that can be generated in normal distribution.
         *
         * \note The largest value is not infinity with a table, but it
         *       depends on the table resolution. With 14 bits, this is roughly
         *       four standard deviations above the mean.
         */
        result_type
        max() const
        {
            return c_table_[c_table_.size()-1];
        }

        /*! \brief Mean of the present normal distribution */
        result_type
        mean() const
        {
            return param_.mean();
        }

        /*! \brief Standard deviation of the present normal distribution */

        result_type
        stddev() const
        {
            return param_.stddev();
        }

        /*! \brief The parameter class (mean & stddev) of the normal distribution */
        param_type
        param() const
        {
            return param_;
        }

        /*! \brief Clear all internal saved random bits from the random engine */
        void
        reset()
        {
            savedRandomBitsLeft_ = 0;
        }

        /*! \brief Return normal distribution value specified by internal parameters.
         *
         * \tparam Rng   Random engine type used to provide uniform random bits.
         * \param  g     Random engine of class Rng. For normal GROMACS usage
         *               you likely want to use ThreeFry2x64.
         */
        template<class Rng>
        result_type
        operator()(Rng &g)
        {
            return (*this)(g, param_);
        }

        /*! \brief Return normal distribution value specified by given parameters
         *
         * \tparam Rng   Random engine type used to provide uniform random bits.
         * \param  g     Random engine of class Rng. For normal GROMACS usage
         *               you likely want to use ThreeFry2x64.
         * \param  param Parameters used to specify normal distribution.
         */
        template<class Rng>
        result_type
        operator()(Rng &g, const param_type &param)
        {
            if (savedRandomBitsLeft_ < tableBits)
            {
                // We do not know whether the generator g returns 64 or 32 bits,
                // since g is not known when we construct this class.
                // To keep things simple, we always draw one random number,
                // store it in our 64-bit value, and set the number of active bits.
                // For tableBits up to 16 this will be as efficient both with 32
                // and 64 bit random engines when drawing multiple numbers
                // (our default value is
                // c_TabulatedNormalDistributionDefaultBits == 14). It
                // also avoids drawing multiple 32-bit random numbers
                // even if we just call this routine for a single
                // result.
                savedRandomBits_     = static_cast<uint64_t>(g());
                savedRandomBitsLeft_ = std::numeric_limits<typename Rng::result_type>::digits;
            }
            result_type value        = c_table_[savedRandomBits_ & ( (1ULL << tableBits) - 1 ) ];
            savedRandomBits_       >>= tableBits;
            savedRandomBitsLeft_    -= tableBits;
            return param.mean() + value * param.stddev();
        }

        /*!\brief Check if two tabulated normal distributions have identical states.
         *
         * \param  x     Instance to compare with.
         */
        bool
        operator==(const TabulatedNormalDistribution<RealType, tableBits> &x) const
        {
            return (param_ == x.param_ &&
                    savedRandomBits_ == x.savedRandomBits_ &&
                    savedRandomBitsLeft_ == x.savedRandomBitsLeft_);
        }

        /*!\brief Check if two tabulated normal distributions have different states.
         *
         * \param  x     Instance to compare with.
         */
        bool
        operator!=(const TabulatedNormalDistribution<RealType, tableBits> &x) const
        {
            return !operator==(x);
        }

    private:
        /*! \brief Parameters of normal distribution (mean and stddev) */
        param_type                                                   param_;
        /*! \brief Array with tabluated values of normal distribution */
        static const std::array<RealType, 1 << tableBits>            c_table_;
        /*! \brief Saved output from random engine, shifted tableBits right each time */
        uint64_t                                                     savedRandomBits_;
        /*! \brief Number of valid bits remaining i savedRandomBits_ */
        unsigned int                                                 savedRandomBitsLeft_;

        GMX_DISALLOW_COPY_AND_ASSIGN(TabulatedNormalDistribution);
};

// MSVC does not handle extern template class members correctly even in MSVC 2015,
// so in that case we have to instantiate in every object using it. In addition,
// doxygen is convinced this defines a function (which leads to crashes in our python
// scripts), so to avoid confusion we hide it from doxygen too.
#if !defined(_MSC_VER) && !defined(DOXYGEN)
// Declaration of template specialization
template<>
const std::array<real, 1<<detail::c_TabulatedNormalDistributionDefaultBits> TabulatedNormalDistribution<>::c_table_;

extern template
const std::array<real, 1<<detail::c_TabulatedNormalDistributionDefaultBits> TabulatedNormalDistribution<>::c_table_;
#endif

// Instantiation for all tables without specialization
template<class RealType, unsigned int tableBits>
const std::array<RealType, 1<<tableBits> TabulatedNormalDistribution<RealType, tableBits>::c_table_ = TabulatedNormalDistribution<RealType, tableBits>::makeTable();

}      // namespace gmx

#endif // GMX_RANDOM_TABULATEDNORMALDISTRIBUTION_H