// Main programm
int main (int argc, char *argv[]) {
Search_settings sett;
Command_line_opts opts;
Search_range s_range;
Aux_arrays aux_arr;
double *F; // F-statistic array
int i, j, r, c, a, b, g;
int d, o, m, k;
int bins = 2, ROW, dim = 4; // neighbourhood of point will be divide into defined number of bins
double pc[4]; // % define neighbourhood around each parameter for initial grid
double pc2[4]; // % define neighbourhood around each parameter for direct maximum search (MADS & Simplex)
double tol = 1e-10;
// double delta = 1e-5; // initial step in MADS function
// double *results; // Vector with results from Fstatnet function
// double *maximum; // True maximum of Fstat
// double results_max[11];
double s1, s2, s3, s4;
double sgnlo[4];
double **arr; // arr[ROW][COL], arrg[ROW][COL];
double nSource[3];
double sinalt, cosalt, sindelt, cosdelt;
double F_min;
char path[512];
double x, y;
ROW = pow((bins+1),4);
#ifdef YEPPP
yepLibrary_Init();
Yep64f *results_max = (Yep64f*)malloc(sizeof(Yep64f)*11);
Yep64f *results_first = (Yep64f*)malloc(sizeof(Yep64f)*11);
Yep64f *results = (Yep64f*)malloc(sizeof(Yep64f)*11);
Yep64f *maximum = (Yep64f*)malloc(sizeof(Yep64f)*11);
// Yep64f *sgnlo = (Yep64f*)malloc(sizeof(Yep64f)*4);
// Yep64f *nSource = (Yep64f*)malloc(sizeof(Yep64f)*3);
Yep64f *mean = (Yep64f*)malloc(sizeof(Yep64f)*4);
enum YepStatus status;
#endif
pc[0] = 0.015;
pc[1] = 0.015;
pc[2] = 0.015;
pc[3] = 0.015;
for (i = 0; i < 4; i++){
pc2[i] = 2*pc[i]/bins;
}
// Time tests
double tdiff;
clock_t tstart, tend;
// Command line options
handle_opts(&sett, &opts, argc, argv);
// Output data handling
/* struct stat buffer;
if (stat(opts.prefix, &buffer) == -1) {
if (errno == ENOENT) {
// Output directory apparently does not exist, try to create one
if(mkdir(opts.prefix, S_IRWXU | S_IRGRP | S_IXGRP
| S_IROTH | S_IXOTH) == -1) {
perror (opts.prefix);
return 1;
}
} else { // can't access output directory
perror (opts.prefix);
return 1;
}
}
*/
sprintf(path, "%s/candidates.coi", opts.dtaprefix);
//Glue function
if(strlen(opts.glue)) {
glue(&opts);
sprintf(opts.dtaprefix, "./data_total");
sprintf(opts.dtaprefix, "%s/followup_total_data", opts.prefix);
opts.ident = 000;
}
FILE *coi;
int z;
if ((coi = fopen(path, "r")) != NULL) {
// while(!feof(coi)) {
/* if(!fread(&w, sizeof(unsigned short int), 1, coi)) { break; }
fread(&mean, sizeof(float), 5, coi);
fread(&fra, sizeof(unsigned short int), w, coi);
fread(&ops, sizeof(int), w, coi);
if((fread(&mean, sizeof(float), 4, coi)) == 4){
*/
while(fscanf(coi, "%le %le %le %le", &mean[0], &mean[1], &mean[2], &mean[3]) == 4){
//Time test
// tstart = clock();
arr = matrix(ROW, 4);
//Function neighbourhood - generating grid around point
arr = neigh(mean, pc, bins);
// Output data handling
/* struct stat buffer;
if (stat(opts.prefix, &buffer) == -1) {
if (errno == ENOENT) {
// Output directory apparently does not exist, try to create one
if(mkdir(opts.prefix, S_IRWXU | S_IRGRP | S_IXGRP
| S_IROTH | S_IXOTH) == -1) {
perror (opts.prefix);
return 1;
}
}
else { // can't access output directory
perror (opts.prefix);
return 1;
}
}
*/
// Grid data
if(strlen(opts.addsig)) {
read_grid(&sett, &opts);
}
// Search settings
search_settings(&sett);
// Detector network settings
detectors_settings(&sett, &opts);
// Array initialization
init_arrays(&sett, &opts, &aux_arr, &F);
// Amplitude modulation functions for each detector
for(i=0; i<sett.nifo; i++) rogcvir(&ifo[i]);
// Adding signal from file
if(strlen(opts.addsig)) {
add_signal(&sett, &opts, &aux_arr, &s_range);
}
// Setting number of using threads (not required)
omp_set_num_threads(1);
results_max[5] = 0.;
// ifo zostaje shared
// ifo....shft i ifo....xdatm{a,b] prerobić na lokalne tablice w fstatnet
// w regionie parallel wprowadzić tablice private aa i bb ; alokować i przekazywać je jako argumenty do fstatnet i amoeba
// Main loop - over all parameters + parallelisation
#pragma omp parallel default(shared) private(d, i, sgnlo, sinalt, cosalt, sindelt, cosdelt, nSource, results, maximum)
{
double **sigaa, **sigbb; // aa[nifo][N]
sigaa = matrix(sett.nifo, sett.N);
sigbb = matrix(sett.nifo, sett.N);
#pragma omp for
for (d = 0; d < ROW; ++d){
for (i = 0; i < 4; i++){
sgnlo[i] = arr[d][i];
// sgnlo[i] = mean[i];
}
sinalt = sin(sgnlo[3]);
cosalt = cos(sgnlo[3]);
sindelt = sin(sgnlo[2]);
cosdelt = cos(sgnlo[2]);
nSource[0] = cosalt*cosdelt;
nSource[1] = sinalt*cosdelt;
nSource[2] = sindelt;
for (i = 0; i < sett.nifo; ++i){
modvir(sinalt, cosalt, sindelt, cosdelt,
sett.N, &ifo[i], &aux_arr, sigaa[i], sigbb[i]);
}
// F-statistic in given point
results = Fstatnet(&sett, sgnlo, nSource, sigaa, sigbb);
//printf("Fstatnet: %le %le %le %le %le %le\n", results[6], results[7], results[8], results[9], results[5], results[4]);
#pragma omp critical
if(results[5] < results_max[5]){
for (i = 0; i < 11; i++){
results_max[i] = results[i];
}
}
// Maximum search using simplex algorithm
if(opts.simplex_flag){
// puts("Simplex");
maximum = amoeba(&sett, &aux_arr, sgnlo, nSource, results, dim, tol, pc2, sigaa, sigbb);
printf("Amoeba: %le %le %le %le %le %le\n", maximum[6], maximum[7], maximum[8], maximum[9], maximum[5], maximum[4]);
// Maximum value in points searching
#pragma omp critical
if(maximum[5] < results_max[5]){
for (i = 0; i < 11; i++){
results_max[i] = maximum[i];
}
}
} //simplex
} // d - main outside loop
free_matrix(sigaa, sett.nifo, sett.N);
free_matrix(sigbb, sett.nifo, sett.N);
} //pragma
for(g = 0; g < 11; g++) results_first[g] = results_max[g];
// Maximum search using MADS algorithm
if(opts.mads_flag) {
// puts("MADS");
maximum = MADS(&sett, &aux_arr, results_max, mean, tol, pc2, bins);
}
//Time test
// tend = clock();
// tdiff = (tend - tstart)/(double)CLOCKS_PER_SEC;
printf("%le %le %le %le %le %le\n", results_max[6], results_max[7], results_max[8], results_max[9], results_max[5], results_max[4]);
} // while fread coi
// }
} //if coi
else {
perror (path);
return 1;
}
// Output information
/* puts("**********************************************************************");
printf("*** Maximum value of F-statistic for grid is : (-)%.8le ***\n", -results_first[5]);
printf("Sgnlo: %.8le %.8le %.8le %.8le\n", results_first[6], results_first[7], results_first[8], results_first[9]);
printf("Amplitudes: %.8le %.8le %.8le %.8le\n", results_first[0], results_first[1], results_first[2], results_first[3]);
printf("Signal-to-noise ratio: %.8le\n", results_first[4]);
printf("Signal-to-noise ratio from estimated amplitudes (for h0 = 1): %.8le\n", results_first[10]);
puts("**********************************************************************");
if((opts.mads_flag)||(opts.simplex_flag)){
printf("*** True maximum is : (-)%.8le ***\n", -maximum[5]);
printf("Sgnlo for true maximum: %.8le %.8le %.8le %.8le\n", maximum[6], maximum[7], maximum[8], maximum[9]);
printf("Amplitudes for true maximum: %.8le %.8le %.8le %.8le\n", maximum[0], maximum[1], maximum[2], maximum[3]);
printf("Signal-to-noise ratio for true maximum: %.8le\n", maximum[4]);
printf("Signal-to-noise ratio from estimated amplitudes (for h0 = 1) for true maximum: %.8le\n", maximum[10]);
puts("**********************************************************************");
}*/
// Cleanup & memory free
free(results_max);
free(results_first);
free(results);
free(maximum);
free(mean);
free_matrix(arr, ROW, 4);
cleanup_followup(&sett, &opts, &s_range, &aux_arr, F);
return 0;
}
kernel void convolute(int4 imagesize, global unsigned char *input,
global unsigned char *output, global kernf *filterG) {
int4 gid = (int4)(get_global_id(0)*CONV_UNROLL, get_global_id(1), get_global_id(2), 0);
int4 lid = (int4)(get_local_id(0), get_local_id(1), get_local_id(2), 0);
int4 group = (int4)(get_group_id(0), get_group_id(1), get_group_id(2), 0);
// First (?) pixel to process with this kernel
int4 pixelid = gid;
// Starting offset of the first pixel to process
int imoffset = pixelid.s0 + imagesize.s0 * pixelid.s1 + imagesize.s0 * imagesize.s1 * pixelid.s2;
int i,j;
int dx,dy,dz;
/* MAD performs a single convolution operation for each kernel,
using the current 'raw' value as the input image
'ko' as an instance of an unrolled convolution filter
'pos' as the X-offset for each of the unrolled convolution filters
Note that all the if statements dependent only on static values -
meaning that they can be optimized away by the compiler
*/
#define MAD(ko,pos) {if(CONV_UNROLL>ko) { \
if(pos-ko >= 0 && pos-ko < kernsize) { \
val[ko] = mmad(val[ko],(kernf)(raw),filter[(pos-ko)+offset]); \
}}}
#define MADS(pos) {if(pos<kernsize) { \
raw=input[imoffset2+pos]; \
MAD(0,pos); MAD(1,pos); MAD(2,pos); MAD(3,pos); MAD(4,pos); MAD(5,pos); MAD(6,pos); MAD(7,pos); \
MAD(8,pos); MAD(9,pos); MAD(10,pos); MAD(11,pos); MAD(12,pos); MAD(13,pos); MAD(14,pos); MAD(15,pos); \
MAD(16,pos); MAD(17,pos); MAD(18,pos); MAD(19,pos); MAD(20,pos); MAD(21,pos); MAD(22,pos); MAD(23,pos); \
MAD(24,pos); MAD(25,pos); MAD(26,pos); MAD(27,pos); MAD(28,pos); MAD(29,pos); MAD(30,pos); MAD(31,pos); \
MAD(32,pos); MAD(33,pos); MAD(34,pos); MAD(35,pos); MAD(36,pos); MAD(37,pos); MAD(38,pos); MAD(39,pos); \
}}
kernf val[CONV_UNROLL];
for(j=0;j<CONV_UNROLL;j++)
val[j]=(kernf)(0.0);
int localSize = get_local_size(0) * get_local_size(1) * get_local_size(2);
local kernf filter[kernsize*kernsize*kernsize];
/* Copy global filter to local memory */
event_t event = async_work_group_copy(filter,filterG,kernsize*kernsize*kernsize,0);
wait_group_events(1, &event);
if(gid.s0 + kernsize + CONV_UNROLL > imagesize.s0 ||
gid.s1 + kernsize > imagesize.s1 ||
gid.s2 + kernsize > imagesize.s2) return;
for(dz=0;dz<kernsize;dz++)
for(dy=0;dy<kernsize;dy++) {
int offset = dy*kernsize*nkernels + dz*kernsize*kernsize*nkernels;
int imoffset2 = imoffset+dy*imagesize.s0 + dz*imagesize.s0*imagesize.s1;
unsigned char raw;
/* kernsize + convolution_unroll < 42 */
MADS(0); MADS(1); MADS(2); MADS(3); MADS(4); MADS(5);
MADS(6); MADS(7); MADS(8); MADS(9); MADS(10); MADS(11);
MADS(12); MADS(13); MADS(14); MADS(15); MADS(16); MADS(17);
MADS(18); MADS(19); MADS(20); MADS(21); MADS(22); MADS(23);
MADS(24); MADS(25); MADS(26); MADS(27); MADS(28); MADS(29);
MADS(30); MADS(31); MADS(32); MADS(33); MADS(34); MADS(35);
MADS(36); MADS(37); MADS(38); MADS(39); MADS(40); MADS(41);
}
for(j=0;j<CONV_UNROLL;j++) {
kernstore( convert_kernuc(val[j]), imoffset+j, output);
}
}
