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wip commit, memory optimization

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This commit is contained in:
Oliver Hahn 2023-02-04 19:59:03 -08:00
parent 33d784a137
commit 437e77f74f
6 changed files with 364 additions and 341 deletions
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12
src/convolution_kernel.cc
View file

@ -298,9 +298,15 @@ public:
}
else
{
cparam_.nx = 2 * prefh_->size(ilevel, 0);
cparam_.ny = 2 * prefh_->size(ilevel, 1);
cparam_.nz = 2 * prefh_->size(ilevel, 2);
if( prefh_->get_margin() < 0 ){
cparam_.nx = 2 * prefh_->size(ilevel, 0);
cparam_.ny = 2 * prefh_->size(ilevel, 1);
cparam_.nz = 2 * prefh_->size(ilevel, 2);
}else{
cparam_.nx = prefh_->size(ilevel, 0) + 2*prefh_->get_margin();
cparam_.ny = prefh_->size(ilevel, 1) + 2*prefh_->get_margin();
cparam_.nz = prefh_->size(ilevel, 2) + 2*prefh_->get_margin();
}
cparam_.lx = (double)cparam_.nx / (double)(1 << ilevel) * boxlength_;
cparam_.ly = (double)cparam_.ny / (double)(1 << ilevel) * boxlength_;

22
src/densities.cc
View file

@ -632,12 +632,22 @@ void GenerateDensityHierarchy(config_file &cf, transfer_function *ptf, tf_type t
LOGUSER(" size =(%5d,%5d,%5d)", refh.size(levelmin + i, 0),
refh.size(levelmin + i, 1), refh.size(levelmin + i, 2));
fine = new PaddedDensitySubGrid<real_t>(refh.offset(levelmin + i, 0),
refh.offset(levelmin + i, 1),
refh.offset(levelmin + i, 2),
refh.size(levelmin + i, 0),
refh.size(levelmin + i, 1),
refh.size(levelmin + i, 2));
if( refh.get_margin() > 0 )
fine = new PaddedDensitySubGrid<real_t>(refh.offset(levelmin + i, 0),
refh.offset(levelmin + i, 1),
refh.offset(levelmin + i, 2),
refh.size(levelmin + i, 0),
refh.size(levelmin + i, 1),
refh.size(levelmin + i, 2),
refh.get_margin(), refh.get_margin(), refh.get_margin() );
else
fine = new PaddedDensitySubGrid<real_t>(refh.offset(levelmin + i, 0),
refh.offset(levelmin + i, 1),
refh.offset(levelmin + i, 2),
refh.size(levelmin + i, 0),
refh.size(levelmin + i, 1),
refh.size(levelmin + i, 2));
/////////////////////////////////////////////////////////////////////////
// load white noise for patch

2
src/main.cc
View file

@ -230,7 +230,7 @@ void store_grid_structure( config_file& cf, const refinement_hierarchy& rh )
for( int j=0; j<3; ++j )
{
sprintf(str1,"offset(%d,%d)",i,j);
sprintf(str2,"%d",rh.offset(i,j));
sprintf(str2,"%ld",rh.offset(i,j));
cf.insertValue("setup",str1,str2);
sprintf(str1,"size(%d,%d)",i,j);

35
src/mesh.hh
View file

@ -1207,17 +1207,6 @@ class refinement_hierarchy
std::vector<index3_t> absoffsets_;
std::vector<index3_t> len_;
// std::vector<unsigned>
// ox_, //!< relative x-coordinates of grid origins (in coarser grid cells)
// oy_, //!< relative y-coordinates of grid origins (in coarser grid cells)
// oz_, //!< relative z-coordinates of grid origins (in coarser grid cells)
// oax_, //!< absolute x-coordinates of grid origins (in fine grid cells)
// oay_, //!< absolute y-coordinates of grid origins (in fine grid cells)
// oaz_, //!< absolute z-coordinates of grid origins (in fine grid cells)
// nx_, //!< x-extent of grids (in fine grid cells)
// ny_, //!< y-extent of grids (in fine grid cells)
// nz_; //!< z-extent of grids (in fine grid cells)
unsigned
levelmin_, //!< minimum grid level for Poisson solver
levelmax_, //!< maximum grid level for all operations
@ -1225,16 +1214,14 @@ class refinement_hierarchy
padding_, //!< padding in number of coarse cells between refinement levels
blocking_factor_;
int margin_; //!< number of cells used for additional padding for convolutions with isolated boundaries (-1 = double padding)
config_file &cf_; //!< reference to config_file
bool align_top_, //!< bool whether to align all grids with coarsest grid cells
preserve_dims_, //!< bool whether to preserve user-specified grid dimensions
equal_extent_; //!< bool whether the simulation code requires squared refinement regions (e.g. RAMSES)
// double
// x0ref_[3], //!< coordinates of refinement region origin (in [0..1[)
// lxref_[3]; //!< extent of refinement region (int [0..1[)
vec3_t x0ref_; //!< coordinates of refinement region origin (in [0..1[)
vec3_t lxref_; //!< extent of refinement region (int [0..1[)
@ -1264,6 +1251,7 @@ public:
preserve_dims_ = cf_.getValueSafe<bool>("setup", "preserve_dims", false);
equal_extent_ = cf_.getValueSafe<bool>("setup", "force_equal_extent", false);
blocking_factor_ = cf.getValueSafe<unsigned>("setup", "blocking_factor", 0);
margin_ = cf.getValueSafe<int>("setup","convolution_margin",32);
bool bnoshift = cf_.getValueSafe<bool>("setup", "no_shift", false);
bool force_shift = cf_.getValueSafe<bool>("setup", "force_shift", false);
@ -1289,14 +1277,8 @@ public:
// if not doing any refinement levels, set extent to full box
if (levelmin_ == levelmax_)
{
x0ref_ = {0.0, 0.0, 0.0};
// x0ref_[0] = 0.0;
// x0ref_[1] = 0.0;
// x0ref_[2] = 0.0;
lxref_[0] = 1.0;
lxref_[1] = 1.0;
lxref_[2] = 1.0;
x0ref_ = { 0.0, 0.0, 0.0 };
lxref_ = { 1.0, 1.0, 1.0 };
}
unsigned ncoarse = 1 << levelmin_;
@ -1658,6 +1640,7 @@ public:
offsets_ = o.offsets_;
absoffsets_ = o.absoffsets_;
len_ = o.len_;
margin_ = o.margin_;
// ox_ = o.ox_;
// oy_ = o.oy_;
// oz_ = o.oz_;
@ -1776,6 +1759,12 @@ public:
return xshift_[idim];
}
//! get the margin reserved for isolated convolutions (-1=double pad)
int get_margin() const
{
return margin_;
}
//! get the total shift of the coordinate system in box coordinates
const double *get_coord_shift(void) const
{

73
src/random.cc
View file

@ -1697,12 +1697,26 @@ void random_number_generator<rng, T>::compute_random_numbers(void)
int lfac = 1 << (ilevel - levelmin_poisson);
lx[0] = 2 * prefh_->size(ilevel, 0);
lx[1] = 2 * prefh_->size(ilevel, 1);
lx[2] = 2 * prefh_->size(ilevel, 2);
x0[0] = prefh_->offset_abs(ilevel, 0) - lfac * shift[0] - lx[0] / 4;
x0[1] = prefh_->offset_abs(ilevel, 1) - lfac * shift[1] - lx[1] / 4;
x0[2] = prefh_->offset_abs(ilevel, 2) - lfac * shift[2] - lx[2] / 4;
int margin[3];
if (prefh_->get_margin()>0){
margin[0] = prefh_->get_margin();
margin[1] = prefh_->get_margin();
margin[2] = prefh_->get_margin();
}else{
margin[0] = prefh_->size(ilevel, 0)/2;
margin[1] = prefh_->size(ilevel, 1)/2;
margin[2] = prefh_->size(ilevel, 2)/2;
}
lx[0] = prefh_->size(ilevel, 0) + 2*margin[0];
lx[1] = prefh_->size(ilevel, 1) + 2*margin[1];
lx[2] = prefh_->size(ilevel, 2) + 2*margin[2];
x0[0] = prefh_->offset_abs(ilevel, 0) - lfac * shift[0] - margin[0];//lx[0] / 4;
x0[1] = prefh_->offset_abs(ilevel, 1) - lfac * shift[1] - margin[1];//lx[1] / 4;
x0[2] = prefh_->offset_abs(ilevel, 2) - lfac * shift[2] - margin[2];//lx[2] / 4;
LOGINFO("margin=%ld",prefh_->get_margin());
LOGINFO("x0=(%ld,%ld,%ld) lx=(%ld,%ld,%ld)",x0[0],x0[1],x0[2],lx[0],lx[1],lx[2]);
if (randc[ilevel] == NULL)
randc[ilevel] = new rng(*randc[ilevel - 1], ran_cube_size_, rngseeds_[ilevel], kavg, ilevel == levelmin_ + 1, x0, lx);
@ -1812,7 +1826,7 @@ void random_number_generator<rng, T>::store_rnd(int ilevel, rng *prng)
char fname[128];
sprintf(fname, "grafic_wnoise_%04d.bin", ilevel);
LOGUSER("Storing white noise field for grafic in file \'%s\'...", fname);
LOGINFO("Storing white noise field for grafic in file \'%s\'...", fname);
LOGDEBUG("(%d,%d,%d) -- (%d,%d,%d) -- lfac = %d", nx, ny, nz, i0, j0, k0, lfac);
std::ofstream ofs(fname, std::ios::binary | std::ios::trunc);
@ -1884,12 +1898,23 @@ void random_number_generator<rng, T>::store_rnd(int ilevel, rng *prng)
int nx, ny, nz;
int i0, j0, k0;
nx = 2 * prefh_->size(ilevel, 0);
ny = 2 * prefh_->size(ilevel, 1);
nz = 2 * prefh_->size(ilevel, 2);
i0 = prefh_->offset_abs(ilevel, 0) - lfac * shift[0] - nx / 4;
j0 = prefh_->offset_abs(ilevel, 1) - lfac * shift[1] - ny / 4; // was nx/4
k0 = prefh_->offset_abs(ilevel, 2) - lfac * shift[2] - nz / 4; // was nx/4
int margin[3];
if (prefh_->get_margin()>0){
margin[0] = prefh_->get_margin();
margin[1] = prefh_->get_margin();
margin[2] = prefh_->get_margin();
}else{
margin[0] = prefh_->size(ilevel, 0)/2;
margin[1] = prefh_->size(ilevel, 1)/2;
margin[2] = prefh_->size(ilevel, 2)/2;
}
nx = prefh_->size(ilevel, 0) + 2*margin[0];
ny = prefh_->size(ilevel, 1) + 2*margin[1];
nz = prefh_->size(ilevel, 2) + 2*margin[2];
i0 = prefh_->offset_abs(ilevel, 0) - lfac * shift[0] - margin[0]; //nx / 4;
j0 = prefh_->offset_abs(ilevel, 1) - lfac * shift[1] - margin[1]; //ny / 4; // was nx/4
k0 = prefh_->offset_abs(ilevel, 2) - lfac * shift[2] - margin[2]; //nz / 4; // was nx/4
char fname[128];
sprintf(fname, "wnoise_%04d.bin", ilevel);
@ -1930,12 +1955,22 @@ void random_number_generator<rng, T>::store_rnd(int ilevel, rng *prng)
}
else
{
nx = 2 * prefh_->size(ilevel, 0);
ny = 2 * prefh_->size(ilevel, 1);
nz = 2 * prefh_->size(ilevel, 2);
i0 = prefh_->offset_abs(ilevel, 0) - lfac * shift[0] - nx / 4;
j0 = prefh_->offset_abs(ilevel, 1) - lfac * shift[1] - ny / 4; // was nx/4
k0 = prefh_->offset_abs(ilevel, 2) - lfac * shift[2] - nz / 4; // was nx/4
int margin[3];
if (prefh_->get_margin()>0){
margin[0] = prefh_->get_margin();
margin[1] = prefh_->get_margin();
margin[2] = prefh_->get_margin();
}else{
margin[0] = prefh_->size(ilevel, 0)/2;
margin[1] = prefh_->size(ilevel, 1)/2;
margin[2] = prefh_->size(ilevel, 2)/2;
}
nx = prefh_->size(ilevel, 0) + 2*margin[0];
ny = prefh_->size(ilevel, 1) + 2*margin[1];
nz = prefh_->size(ilevel, 2) + 2*margin[2];
i0 = prefh_->offset_abs(ilevel, 0) - lfac * shift[0] - margin[0];//nx / 4;
j0 = prefh_->offset_abs(ilevel, 1) - lfac * shift[1] - margin[1];//ny / 4; // was nx/4
k0 = prefh_->offset_abs(ilevel, 2) - lfac * shift[2] - margin[2];//nz / 4; // was nx/4
}
mem_cache_[ilevel - levelmin_] = new std::vector<T>(nx * ny * nz, 0.0);

561
src/random.hh
View file

@ -1,22 +1,22 @@
/*
random.hh - This file is part of MUSIC -
a code to generate multi-scale initial conditions
for cosmological simulations
a code to generate multi-scale initial conditions
for cosmological simulations
Copyright (C) 2010 Oliver Hahn
*/
//... for testing purposes.............
//#define DEGRADE_RAND1
//#define DEGRADE_RAND2
// #define DEGRADE_RAND1
// #define DEGRADE_RAND2
//.....................................
#ifndef __RANDOM_HH
#define __RANDOM_HH
#define DEF_RAN_CUBE_SIZE 32
#define DEF_RAN_CUBE_SIZE 32
#include <fstream>
#include <algorithm>
@ -31,407 +31,390 @@
#include "mg_operators.hh"
#include "constraints.hh"
class RNG_plugin{
class RNG_plugin
{
protected:
config_file *pcf_; //!< pointer to config_file from which to read parameters
config_file *pcf_; //!< pointer to config_file from which to read parameters
public:
explicit RNG_plugin( config_file& cf )
: pcf_( &cf )
{ }
virtual ~RNG_plugin() { }
virtual bool is_multiscale() const = 0;
explicit RNG_plugin(config_file &cf)
: pcf_(&cf)
{
}
virtual ~RNG_plugin() {}
virtual bool is_multiscale() const = 0;
};
struct RNG_plugin_creator
{
virtual RNG_plugin * create( config_file& cf ) const = 0;
virtual ~RNG_plugin_creator() { }
virtual RNG_plugin *create(config_file &cf) const = 0;
virtual ~RNG_plugin_creator() {}
};
std::map< std::string, RNG_plugin_creator *>&
std::map<std::string, RNG_plugin_creator *> &
get_RNG_plugin_map();
void print_RNG_plugins( void );
void print_RNG_plugins(void);
template< class Derived >
template <class Derived>
struct RNG_plugin_creator_concrete : public RNG_plugin_creator
{
//! register the plugin by its name
RNG_plugin_creator_concrete( const std::string& plugin_name )
{
get_RNG_plugin_map()[ plugin_name ] = this;
}
//! register the plugin by its name
RNG_plugin_creator_concrete(const std::string &plugin_name)
{
get_RNG_plugin_map()[plugin_name] = this;
}
//! create an instance of the plugin
RNG_plugin* create( config_file& cf ) const
{
return new Derived( cf );
}
//! create an instance of the plugin
RNG_plugin *create(config_file &cf) const
{
return new Derived(cf);
}
};
typedef RNG_plugin RNG_instance;
RNG_plugin *select_RNG_plugin( config_file& cf );
RNG_plugin *select_RNG_plugin(config_file &cf);
/*!
* @brief encapsulates all things random number generator related
*/
template< typename T >
template <typename T>
class random_numbers
{
public:
unsigned
res_, //!< resolution of the full mesh
cubesize_, //!< size of one independent random number cube
ncubes_; //!< number of random number cubes to cover the full mesh
long baseseed_; //!< base seed from which cube seeds are computed
unsigned
res_, //!< resolution of the full mesh
cubesize_, //!< size of one independent random number cube
ncubes_; //!< number of random number cubes to cover the full mesh
long baseseed_; //!< base seed from which cube seeds are computed
protected:
//! vector of 3D meshes (the random number cubes) with random numbers
std::vector< Meshvar<T>* > rnums_;
//! map of 3D indices to cube index
std::map<size_t,size_t> cubemap_;
typedef std::map<size_t,size_t>::iterator cubemap_iterator;
std::vector<Meshvar<T> *> rnums_;
//! map of 3D indices to cube index
std::map<size_t, size_t> cubemap_;
typedef std::map<size_t, size_t>::iterator cubemap_iterator;
protected:
//! register a cube with the hash map
void register_cube( int i, int j, int k);
//! register a cube with the hash map
void register_cube(int i, int j, int k);
//! fills a subcube with random numbers
double fill_cube( int i, int j, int k);
double fill_cube(int i, int j, int k);
//! subtract a constant from an entire cube
void subtract_from_cube( int i, int j, int k, double val );
void subtract_from_cube(int i, int j, int k, double val);
//! copy random numbers from a cube to a full grid array
template< class C >
void copy_cube( int i, int j, int k, C& dat )
template <class C>
void copy_cube(int i, int j, int k, C &dat)
{
int offi, offj, offk;
offi = i*cubesize_;
offj = j*cubesize_;
offk = k*cubesize_;
i = (i+ncubes_)%ncubes_;
j = (j+ncubes_)%ncubes_;
k = (k+ncubes_)%ncubes_;
size_t icube = (i*ncubes_+j)*ncubes_+k;
cubemap_iterator it = cubemap_.find( icube );
if( it == cubemap_.end() )
{
LOGERR("attempting to copy data from non-existing RND cube %d,%d,%d",i,j,k);
throw std::runtime_error("attempting to copy data from non-existing RND cube");
}
size_t cubeidx = it->second;
for( int ii=0; ii<(int)cubesize_; ++ii )
for( int jj=0; jj<(int)cubesize_; ++jj )
for( int kk=0; kk<(int)cubesize_; ++kk )
dat(offi+ii,offj+jj,offk+kk) = (*rnums_[cubeidx])(ii,jj,kk);
offi = i * cubesize_;
offj = j * cubesize_;
offk = k * cubesize_;
i = (i + ncubes_) % ncubes_;
j = (j + ncubes_) % ncubes_;
k = (k + ncubes_) % ncubes_;
size_t icube = (i * ncubes_ + j) * ncubes_ + k;
cubemap_iterator it = cubemap_.find(icube);
if (it == cubemap_.end())
{
LOGERR("attempting to copy data from non-existing RND cube %d,%d,%d", i, j, k);
throw std::runtime_error("attempting to copy data from non-existing RND cube");
}
size_t cubeidx = it->second;
for (int ii = 0; ii < (int)cubesize_; ++ii)
for (int jj = 0; jj < (int)cubesize_; ++jj)
for (int kk = 0; kk < (int)cubesize_; ++kk)
dat(offi + ii, offj + jj, offk + kk) = (*rnums_[cubeidx])(ii, jj, kk);
}
//! free the memory associated with a subcube
void free_cube( int i, int j, int k );
void free_cube(int i, int j, int k);
//! initialize member variables and allocate memory
void initialize( void );
void initialize(void);
//! fill a cubic subvolume of the full grid with random numbers
double fill_subvolume( int *i0, int *n );
double fill_subvolume(int *i0, int *n);
//! fill an entire grid with random numbers
double fill_all( void );
double fill_all(void);
//! fill an external array instead of the internal field
template< class C >
double fill_all( C& dat )
template <class C>
double fill_all(C &dat)
{
double sum = 0.0;
for( int i=0; i<(int)ncubes_; ++i )
for( int j=0; j<(int)ncubes_; ++j )
for( int k=0; k<(int)ncubes_; ++k )
for (int i = 0; i < (int)ncubes_; ++i)
for (int j = 0; j < (int)ncubes_; ++j)
for (int k = 0; k < (int)ncubes_; ++k)
{
int ii(i),jj(j),kk(k);
register_cube(ii,jj,kk);
}
#pragma omp parallel for reduction(+:sum)
for( int i=0; i<(int)ncubes_; ++i )
for( int j=0; j<(int)ncubes_; ++j )
for( int k=0; k<(int)ncubes_; ++k )
int ii(i), jj(j), kk(k);
register_cube(ii, jj, kk);
}
#pragma omp parallel for reduction(+ \
: sum)
for (int i = 0; i < (int)ncubes_; ++i)
for (int j = 0; j < (int)ncubes_; ++j)
for (int k = 0; k < (int)ncubes_; ++k)
{
int ii(i),jj(j),kk(k);
ii = (ii+ncubes_)%ncubes_;
jj = (jj+ncubes_)%ncubes_;
kk = (kk+ncubes_)%ncubes_;
sum+=fill_cube(ii, jj, kk);
copy_cube(ii,jj,kk,dat);
int ii(i), jj(j), kk(k);
ii = (ii + ncubes_) % ncubes_;
jj = (jj + ncubes_) % ncubes_;
kk = (kk + ncubes_) % ncubes_;
sum += fill_cube(ii, jj, kk);
copy_cube(ii, jj, kk, dat);
free_cube(ii, jj, kk);
}
return sum/(ncubes_*ncubes_*ncubes_);
return sum / (ncubes_ * ncubes_ * ncubes_);
}
//! write the number of allocated random number cubes to stdout
void print_allocated( void );
void print_allocated(void);
public:
//! constructor
random_numbers( unsigned res, unsigned cubesize, long baseseed, int *x0, int *lx );
random_numbers(unsigned res, unsigned cubesize, long baseseed, int *x0, int *lx);
//! constructor for constrained fine field
random_numbers( random_numbers<T>& rc, unsigned cubesize, long baseseed,
bool kspace=false, bool isolated=false, int *x0_=NULL, int *lx_=NULL, bool zeromean=true );
random_numbers(random_numbers<T> &rc, unsigned cubesize, long baseseed,
bool kspace = false, bool isolated = false, int *x0_ = NULL, int *lx_ = NULL, bool zeromean = true);
//! constructor
random_numbers( unsigned res, unsigned cubesize, long baseseed, bool zeromean=true );
random_numbers(unsigned res, unsigned cubesize, long baseseed, bool zeromean = true);
//! constructor to read white noise from file
random_numbers( unsigned res, std::string randfname, bool rndsign );
random_numbers(unsigned res, std::string randfname, bool rndsign);
//! copy constructor for averaged field (not copying) hence explicit!
explicit random_numbers( /*const*/ random_numbers <T>& rc, bool kdegrade = true );
explicit random_numbers(/*const*/ random_numbers<T> &rc, bool kdegrade = true);
//! destructor
~random_numbers()
{
for( unsigned i=0; i<rnums_.size(); ++i )
if( rnums_[i] != NULL )
for (unsigned i = 0; i < rnums_.size(); ++i)
if (rnums_[i] != NULL)
delete rnums_[i];
rnums_.clear();
}
//! access a random number, this allocates a cube and fills it with consistent random numbers
inline T& operator()( int i, int j, int k, bool fillrand=true )
inline T &operator()(int i, int j, int k, bool fillrand = true)
{
int ic, jc, kc, is, js, ks;
if( ncubes_ == 0 )
if (ncubes_ == 0)
throw std::runtime_error("random_numbers: internal error, not properly initialized");
//... determine cube
ic = (int)((double)i/cubesize_ + ncubes_) % ncubes_;
jc = (int)((double)j/cubesize_ + ncubes_) % ncubes_;
kc = (int)((double)k/cubesize_ + ncubes_) % ncubes_;
size_t icube = ((size_t)ic*ncubes_+(size_t)jc)*ncubes_+(size_t)kc;
cubemap_iterator it = cubemap_.find( icube );
if( it == cubemap_.end() )
{
LOGERR("Attempting to copy data from non-existing RND cube %d,%d,%d @ %d,%d,%d",ic,jc,kc,i,j,k);
throw std::runtime_error("attempting to copy data from non-existing RND cube");
}
size_t cubeidx = it->second;
if( rnums_[ cubeidx ] == NULL )
ic = (int)((double)i / cubesize_ + ncubes_) % ncubes_;
jc = (int)((double)j / cubesize_ + ncubes_) % ncubes_;
kc = (int)((double)k / cubesize_ + ncubes_) % ncubes_;
size_t icube = ((size_t)ic * ncubes_ + (size_t)jc) * ncubes_ + (size_t)kc;
cubemap_iterator it = cubemap_.find(icube);
if (it == cubemap_.end())
{
LOGERR("Attempting to access data from non-allocated RND cube %d,%d,%d",ic,jc,kc);
throw std::runtime_error("attempting to access data from non-allocated RND cube");
LOGERR("Attempting to copy data from non-existing RND cube %d,%d,%d @ %d,%d,%d", ic, jc, kc, i, j, k);
throw std::runtime_error("attempting to copy data from non-existing RND cube");
}
size_t cubeidx = it->second;
if (rnums_[cubeidx] == NULL)
{
LOGERR("Attempting to access data from non-allocated RND cube %d,%d,%d", ic, jc, kc);
throw std::runtime_error("attempting to access data from non-allocated RND cube");
}
//... determine cell in cube
is = (i - ic * cubesize_ + cubesize_) % cubesize_;
js = (j - jc * cubesize_ + cubesize_) % cubesize_;
ks = (k - kc * cubesize_ + cubesize_) % cubesize_;
return (*rnums_[ cubeidx ])(is,js,ks);
return (*rnums_[cubeidx])(is, js, ks);
}
//! free all cubes
void free_all_mem( void )
void free_all_mem(void)
{
for( unsigned i=0; i<rnums_.size(); ++i )
if( rnums_[i] != NULL )
for (unsigned i = 0; i < rnums_.size(); ++i)
if (rnums_[i] != NULL)
{
delete rnums_[i];
delete rnums_[i];
rnums_[i] = NULL;
}
}
};
/*!
* @brief encapsulates all things for multi-scale white noise generation
*/
template< typename rng, typename T >
template <typename rng, typename T>
class random_number_generator
{
protected:
config_file * pcf_;
refinement_hierarchy * prefh_;
constraint_set constraints;
int levelmin_,
levelmax_,
levelmin_seed_;
std::vector<long> rngseeds_;
std::vector<std::string> rngfnames_;
bool disk_cached_;
bool restart_;
std::vector< std::vector<T>* > mem_cache_;
unsigned ran_cube_size_;
config_file *pcf_;
refinement_hierarchy *prefh_;
constraint_set constraints;
int levelmin_,
levelmax_,
levelmin_seed_;
std::vector<long> rngseeds_;
std::vector<std::string> rngfnames_;
bool disk_cached_;
bool restart_;
std::vector<std::vector<T> *> mem_cache_;
unsigned ran_cube_size_;
protected:
//! checks if the specified string is numeric
bool is_number(const std::string& s);
bool is_number(const std::string &s);
//! parses the random number parameters in the conf file
void parse_rand_parameters( void );
void parse_rand_parameters(void);
//! correct coarse grid averages for the change in small scale when using Fourier interpolation
void correct_avg( int icoarse, int ifine );
void correct_avg(int icoarse, int ifine);
//! the main driver routine for multi-scale white noise generation
void compute_random_numbers( void );
void compute_random_numbers(void);
//! store the white noise fields in memory or on disk
void store_rnd( int ilevel, rng* prng );
void store_rnd(int ilevel, rng *prng);
public:
//! constructor
random_number_generator( config_file& cf, refinement_hierarchy& refh, transfer_function *ptf = NULL );
random_number_generator(config_file &cf, refinement_hierarchy &refh, transfer_function *ptf = NULL);
//! destructor
~random_number_generator();
//! load random numbers to a new array
template< typename array >
void load( array& A, int ilevel )
template <typename array>
void load(array &A, int ilevel)
{
if( restart_ )
LOGINFO("Attempting to restart using random numbers for level %d\n from file \'wnoise_%04d.bin\'.",ilevel,ilevel);
if( disk_cached_ )
if (restart_)
LOGINFO("Attempting to restart using random numbers for level %d\n from file \'wnoise_%04d.bin\'.", ilevel, ilevel);
if (disk_cached_)
{
char fname[128];
sprintf(fname,"wnoise_%04d.bin",ilevel);
LOGUSER("Loading white noise from file \'%s\'...",fname);
std::ifstream ifs( fname, std::ios::binary );
if( !ifs.good() )
{
LOGERR("White noise file \'%s\'was not found.",fname);
sprintf(fname, "wnoise_%04d.bin", ilevel);
LOGUSER("Loading white noise from file \'%s\'...", fname);
std::ifstream ifs(fname, std::ios::binary);
if (!ifs.good())
{
LOGERR("White noise file \'%s\'was not found.", fname);
throw std::runtime_error("A white noise file was not found. This is an internal inconsistency and bad.");
}
int nx,ny,nz;
ifs.read( reinterpret_cast<char*> (&nx), sizeof(int) );
ifs.read( reinterpret_cast<char*> (&ny), sizeof(int) );
ifs.read( reinterpret_cast<char*> (&nz), sizeof(int) );
if( nx!=(int)A.size(0) || ny!=(int)A.size(1) || nz!=(int)A.size(2) )
{
if( nx==(int)A.size(0)*2 && ny==(int)A.size(1)*2 && nz==(int)A.size(2)*2 )
{
std::cerr << "CHECKPOINT" << std::endl;
int nx, ny, nz;
ifs.read(reinterpret_cast<char *>(&nx), sizeof(int));
ifs.read(reinterpret_cast<char *>(&ny), sizeof(int));
ifs.read(reinterpret_cast<char *>(&nz), sizeof(int));
if (nx != (int)A.size(0) || ny != (int)A.size(1) || nz != (int)A.size(2))
{
int ox = nx/4, oy = ny/4, oz = nz/4;
std::vector<T> slice( ny*nz, 0.0 );
for( int i=0; i<nx; ++i )
if (nx == (int)A.size(0) * 2 && ny == (int)A.size(1) * 2 && nz == (int)A.size(2) * 2)
{
ifs.read( reinterpret_cast<char*> ( &slice[0] ), ny*nz*sizeof(T) );
if( i<ox ) continue;
if( i>=3*ox ) break;
std::cerr << "CHECKPOINT" << std::endl;
#pragma omp parallel for
for( int j=oy; j<3*oy; ++j )
for( int k=oz; k<3*oz; ++k )
A(i-ox,j-oy,k-oz) = slice[j*nz+k];
}
ifs.close();
}
else
{
LOGERR("White noise file is not aligned with array. File: [%d,%d,%d]. Mem: [%d,%d,%d].",
nx,ny,nz,A.size(0),A.size(1),A.size(2));
throw std::runtime_error("White noise file is not aligned with array. This is an internal inconsistency and bad.");
}
}else{
for( int i=0; i<nx; ++i )
{
std::vector<T> slice( ny*nz, 0.0 );
ifs.read( reinterpret_cast<char*> ( &slice[0] ), ny*nz*sizeof(T) );
#pragma omp parallel for
for( int j=0; j<ny; ++j )
for( int k=0; k<nz; ++k )
A(i,j,k) = slice[j*nz+k];
}
ifs.close();
int ox = nx / 4, oy = ny / 4, oz = nz / 4;
std::vector<T> slice(ny * nz, 0.0);
for (int i = 0; i < nx; ++i)
{
ifs.read(reinterpret_cast<char *>(&slice[0]), ny * nz * sizeof(T));
if (i < ox)
continue;
if (i >= 3 * ox)
break;
#pragma omp parallel for
for (int j = oy; j < 3 * oy; ++j)
for (int k = oz; k < 3 * oz; ++k)
A(i - ox, j - oy, k - oz) = slice[j * nz + k];
}
ifs.close();
}
else
{
LOGERR("White noise file is not aligned with array. File: [%d,%d,%d]. Mem: [%d,%d,%d].",
nx, ny, nz, A.size(0), A.size(1), A.size(2));
throw std::runtime_error("White noise file is not aligned with array. This is an internal inconsistency and bad.");
}
}
else
{
for (int i = 0; i < nx; ++i)
{
std::vector<T> slice(ny * nz, 0.0);
ifs.read(reinterpret_cast<char *>(&slice[0]), ny * nz * sizeof(T));
#pragma omp parallel for
for (int j = 0; j < ny; ++j)
for (int k = 0; k < nz; ++k)
A(i, j, k) = slice[j * nz + k];
}
ifs.close();
}
}
else
{
LOGUSER("Copying white noise from memory cache...");
if( mem_cache_[ilevel-levelmin_] == NULL )
LOGERR("Tried to access mem-cached random numbers for level %d. But these are not available!\n",ilevel);
int nx( A.size(0) ), ny( A.size(1) ), nz( A.size(2) );
if ( (size_t)nx*(size_t)ny*(size_t)nz != mem_cache_[ilevel-levelmin_]->size() )
if (mem_cache_[ilevel - levelmin_] == NULL)
LOGERR("Tried to access mem-cached random numbers for level %d. But these are not available!\n", ilevel);
int nx(A.size(0)), ny(A.size(1)), nz(A.size(2));
if ((size_t)nx * (size_t)ny * (size_t)nz != mem_cache_[ilevel - levelmin_]->size())
{
LOGERR("White noise file is not aligned with array. File: [%d,%d,%d]. Mem: [%d,%d,%d].",nx,ny,nz,A.size(0),A.size(1),A.size(2));
LOGERR("White noise file is not aligned with array. File: [%d,%d,%d]. Mem: [%d,%d,%d].", nx, ny, nz, A.size(0), A.size(1), A.size(2));
throw std::runtime_error("White noise file is not aligned with array. This is an internal inconsistency and bad");
}
#pragma omp parallel for
for( int i=0; i<nx; ++i )
for( int j=0; j<ny; ++j )
for( int k=0; k<nz; ++k )
A(i,j,k) = (*mem_cache_[ilevel-levelmin_])[((size_t)i*ny+(size_t)j)*nz+(size_t)k];
std::vector<T>().swap( *mem_cache_[ilevel-levelmin_] );
delete mem_cache_[ilevel-levelmin_];
mem_cache_[ilevel-levelmin_] = NULL;
}
#pragma omp parallel for
for (int i = 0; i < nx; ++i)
for (int j = 0; j < ny; ++j)
for (int k = 0; k < nz; ++k)
A(i, j, k) = (*mem_cache_[ilevel - levelmin_])[((size_t)i * ny + (size_t)j) * nz + (size_t)k];
std::vector<T>().swap(*mem_cache_[ilevel - levelmin_]);
delete mem_cache_[ilevel - levelmin_];
mem_cache_[ilevel - levelmin_] = NULL;
}
}
};
typedef random_numbers<real_t> rand_nums;
typedef random_number_generator< rand_nums,real_t> rand_gen;
typedef random_number_generator<rand_nums, real_t> rand_gen;
#endif //__RANDOM_HH