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image_prediction_GPU.cu
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Tue, Dec 3, 04:54

image_prediction_GPU.cu
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#include <iostream>
#include <string.h>
//#include <cuda_runtime.h>
#include <math.h>
#include <sys/time.h>
#include <fstream>
#include <unistd.h>
#include <assert.h>
#ifndef __xlC__
#warning "gnu compilers"
#include <immintrin.h>
#endif
//#include "simd_math.h"
#include "image_prediction_GPU.cuh"
#ifdef _OPENMP
#include <omp.h>
#endif
#ifdef __WITH_MPI
#warning "MPI enabled"
#include <mpi.h>
#include "mpi_check.h"
#include "chi_comm.hpp"
#endif
//#include "chi_computation.hpp"
#ifdef __WITH_GPU
#include <cuda_runtime.h>
#include "cudafunctions.cuh"
#endif
#define MIN(a,b) (((a)<(b))?(a):(b))
#define MAX(a,b) (((a)>(b))?(a):(b))
//returns predicted images for every constraint (images) given
void image_prediction(struct galaxy *predicted_images, const int MAXIMPERSOURCE, runmode_param *runmode, const struct Potential_SOA *lens, const struct grid_param *frame, const int *nImagesSet,struct galaxy *images){
struct galaxy sources[runmode->nsets];
point predicted_images_pos[runmode->nsets][MAXIMPERSOURCE];
double *grid_gradient_x, *grid_gradient_y;
int grid_size = runmode->nbgridcells;
const double dx = (frame->xmax - frame->xmin)/(runmode->nbgridcells - 1);
const double dy = (frame->ymax - frame->ymin)/(runmode->nbgridcells - 1);
int index = 0;
//==============Calculating the gradient over the whole grid =====================
//Allocating memory for gradient
#ifdef __WITH_GPU
cudaMallocManaged(&grid_gradient_x, grid_size*grid_size*sizeof(double));
cudasafe(cudaGetLastError(), "grid_gradient_x allocation");
cudaMallocManaged(&grid_gradient_y, grid_size*grid_size*sizeof(double));
cudasafe(cudaGetLastError(), "grid_gradient_y allocation");
#else
grid_gradient_x = (double *)malloc((int) grid_size*grid_size*sizeof(double));
grid_gradient_y = (double *)malloc((int) grid_size*grid_size*sizeof(double));
#endif
//Computing Gradient
#ifdef __WITH_GPU
//@@printf("Using GPUs...\n"); fflush(stdout);
#ifdef __WITH_UM
//@@printf("Unified memory...\n"); fflush(stdout);
gradient_grid_GPU_UM(grid_gradient_x, grid_gradient_y, frame, lens, runmode->nhalos, dx, dy, grid_size, grid_size, 0, 0);
#else
gradient_grid_GPU(grid_gradient_x, grid_gradient_y, frame, lens, runmode->nhalos, dx, dy, grid_size, grid_size, 0, 0);
#endif
//gradient_grid_GPU(grid_gradient_x, grid_gradient_y, frame, lens, runmode->nhalos, grid_size);
#else
int zero = 0;
gradient_grid_CPU(grid_gradient_x, grid_gradient_y, frame, lens, runmode->nhalos, runmode->nbgridcells);
#endif
int numsets = 0;
for( int source_id = 0; source_id < runmode->nsets; source_id ++)
numsets += nImagesSet[source_id];
//
for( int source_id = 0; source_id < runmode->nsets; source_id ++)
{
// number of images in the image plane for the specific image (1,3,5...)
unsigned short int nimages = nImagesSet[source_id];
//@@printf("@@ source_id = %d, nimages = %d\n", source_id, nimages_strongLensing[source_id]);
//Init sources
sources[source_id].center.x = sources[source_id].center.y = 0.;
//____________________________ image (constrains) loop ________________________________
for(unsigned short int image_id = 0; image_id < nimages; image_id++)
{
//
struct galaxy sources_temp;
struct point Grad = module_potentialDerivatives_totalGradient_SOA(&images[index + image_id].center, lens, runmode->nhalos);
//
// find the position of the constrain in the source plane
sources_temp.center.x = images[index + image_id].center.x - images[index + image_id].dr*Grad.x;
sources_temp.center.y = images[index + image_id].center.y - images[index + image_id].dr*Grad.y;
//________ Adding up for barycenter comp _________
sources[source_id].center.x += sources_temp.center.x;
sources[source_id].center.y += sources_temp.center.y;
//time += myseconds();
sources[source_id].redshift = 0.;
sources[source_id].shape.a = sources[source_id].shape.b = sources[source_id].shape.theta = (type_t) 0.;
sources[source_id].mag = 0.;
//
}
//________ Dividing by nimages for final barycenter result _________
sources[source_id].center.x /= nimages;
sources[source_id].center.y /= nimages;
sources[source_id].redshift = images[index].redshift;
sources[source_id].dr = images[index].dr;
sources[source_id].dls = images[index].dls;
sources[source_id].dos = images[index].dos;
index += nimages;
}
int locimagesfound[runmode->nsets];
int numimg = 0;
delense_barycenter(&predicted_images_pos[0][0], MAXIMPERSOURCE, &locimagesfound[0], &numimg, runmode, lens, frame, nImagesSet, sources, grid_gradient_x, grid_gradient_y);
std::cout << "Images:" << std::endl;
for(int source_id = 0; source_id < runmode->nsets; source_id++){
std::cout << "Sources:" << source_id << ": " << sources[source_id].center.x << " " << sources[source_id].center.y << std::endl;
unsigned short int nimages = nImagesSet[source_id];
for(unsigned short int image_id = 0; image_id < nimages; image_id++){
std::cout << "Images:" << predicted_images_pos[source_id][image_id].x << " " << predicted_images_pos[source_id][image_id].y << " " << std::endl;
predicted_images[source_id * MAXIMPERSOURCE + image_id].center.x = predicted_images_pos[source_id][image_id].x;
predicted_images[source_id * MAXIMPERSOURCE + image_id].center.y = predicted_images_pos[source_id][image_id].y;
predicted_images[source_id * MAXIMPERSOURCE + image_id].redshift = sources[source_id].redshift;
predicted_images[source_id * MAXIMPERSOURCE + image_id].shape.a = predicted_images[source_id * MAXIMPERSOURCE + image_id].shape.b = predicted_images[source_id * MAXIMPERSOURCE + image_id].shape.theta = (type_t) 0.;
predicted_images[source_id * MAXIMPERSOURCE + image_id].mag = 0.;
}
}
}

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