int main (int argc, char **argv)
{
int x, y, i = 0, k;
grid_t a, b, *v, *w;
init_grid(&a, SZ);
init_grid(&b, SZ);
// LIVE! LIVE, DAMN YOU
for (x = 0; x < SZ; x++)
for (y = 0; y < SZ; y++)
{
k = x * SZ + y;
if (p[i] == k)
{
wcell(&a, x, y, 1);
i++;
if (i == PSZ) i = 0;
}
}
// flip-flop next/current grid
v = &a; w = &b; x = 0;
for (;;) {
draw(v);
//
if (v->i && !v->dt) break;
//
if (wait()) break;
//
next_grid(w, v);
//
if (x) { v = &a; w = &b; x = 0; }
else { v = &b; w = &a; x = 1; }
}
free_grid(&a);
free_grid(&b);
return 0;
}
//
// benchmarking program
//
int main(int argc, char **argv) {
if( find_option( argc, argv, "-h" ) >= 0 )
{
printf( "Options:\n" );
printf( "-h to see this help\n" );
printf( "-n <int> to set number of particles\n" );
printf( "-o <filename> to specify the output file name\n" );
printf( "-s <filename> to specify a summary file name\n" );
printf( "-no turns off all correctness checks and particle output\n");
printf( "-p <int> to set the (maximum) number of threads used\n");
return 0;
}
const int n = read_int( argc, argv, "-n", 1000 );
const bool fast = (find_option( argc, argv, "-no" ) != -1);
const char *savename = read_string( argc, argv, "-o", NULL );
const char *sumname = read_string( argc, argv, "-s", NULL );
const int num_threads_override = read_int( argc, argv, "-p", 0);
FILE *fsave = ((!fast) && savename) ? fopen( savename, "w" ) : NULL;
FILE *fsum = sumname ? fopen ( sumname, "a" ) : NULL;
const double size = set_size( n );
// We need to set the size of a grid square so that the average number of
// particles per grid square is constant. The simulation already ensures
// that the average number of particles in an arbitrary region is constant
// and proportional to the area. So this is just a constant.
const double grid_square_size = sqrt(0.0005) + 0.000001;
const int num_grid_squares_per_side = size / grid_square_size;
printf("Using %d grid squares of side-length %f for %d particles.\n", num_grid_squares_per_side*num_grid_squares_per_side, grid_square_size, n);
std::unique_ptr<std::vector<particle_t> > particles = init_particles(n);
if (num_threads_override > 0) {
omp_set_dynamic(0); // fixed number of threads
omp_set_num_threads(num_threads_override); // assign number of threads
}
//
// simulate a number of time steps
//
double simulation_time = read_timer( );
int max_num_threads = omp_get_max_threads();
int num_actual_threads;
// User-defined reductions aren't available in the version of OMP we're
// using. Instead, we accumulate per-thread stats in this global array
// and reduce manually when we're done.
Stats per_thread_stats[max_num_threads];
// Shared across threads.
std::unique_ptr<OmpThreadsafeGrid> old_grid(new OmpThreadsafeGrid(size, num_grid_squares_per_side));
std::unique_ptr<OmpThreadsafeGrid> next_grid(new OmpThreadsafeGrid(size, num_grid_squares_per_side));
#pragma omp parallel
{
#pragma omp atomic write
num_actual_threads = omp_get_num_threads(); //get number of actual threads
int thread_idx = omp_get_thread_num();
Stats thread_stats;
for (int step = 0; step < 1000; step++) {
// If this is the first step, we must initialize the grid here
// without respecting cache locality. Since we cannot use the existing
// grid, we have to just divide the particles arbitrarily. This
// means that the subsequent code for simulating forces and movement
// will have almost no cache locality on the first iteration: Each thread
// has picked up an arbitrary subset of the particles to insert into the
// grid, and then the threads are responsible for simulating a different,
// mostly-disjoint subset of the particles. On subsequent iterations,
// only the particles that have moved will cause cache misses, so we
// should have much better locality. If we want to really optimize,
// it may be worth rethinking how we store particles and communicate among
// threads. But at that point we might as well write distributed-memory
// code.
if (step == 0) {
#pragma omp for
for (int i = 0; i < n; i++) {
next_grid->add((*particles)[i]);
}
}
// Here we are building the grid that maps locations to sets of
// particles. This step does O(n) work, so it is a bottleneck if done
// serially. For performance comparisons, we have two versions of the
// grid-formation code. The second simply forms the grid serially, in a
// single arbitrary thread. The first is parallel and attempts
// some cache locality. Each thread is responsible for re-inserting
// the grid elements that previously lay in its subgrid. For that reason
// we need to keep around the old grid while we are building the new one;
// this is why we have old_grid and next_grid.
// NOTE: We could instead re-insert each particle right after moving it.
// This would be faster, but it would require us to think about
// simultaneous parallel delete and add, while the current scheme needs
// only support parallel add. (Deleting the entire grid at once is an
// O(1) operation, so we can do it in one thread with a barrier.)
// (The actual simulation operations are read-only on the grid structure
// and write to each particle only once, so we can simply use two
// barriers to protect them.
#pragma omp single
{
old_grid.swap(next_grid);
next_grid.reset(new OmpThreadsafeGrid(size, num_grid_squares_per_side));
}
// Now insert each particle into the new grid.
{
std::unique_ptr<SimpleIterator<particle_t&> > particles_to_insert = old_grid->subgrid(thread_idx, num_actual_threads);
while (particles_to_insert->hasNext()) {
particle_t& p = particles_to_insert->next();
next_grid->add(p);
}
}
// Now we compute forces for particles. Each thread handles its assigned
// subgrid. We first need a barrier to ensure that everyone sees all
// the particles in next_grid.
#pragma omp barrier
{
std::unique_ptr<SimpleIterator<particle_t&> > particles_to_force = next_grid->subgrid(thread_idx, num_actual_threads);
while (particles_to_force->hasNext()) {
particle_t& p = particles_to_force->next();
p.ax = p.ay = 0;
std::unique_ptr<SimpleIterator<particle_t&> > neighbors = next_grid->neighbor_iterator(p);
while (neighbors->hasNext()) {
particle_t& neighbor = neighbors->next();
apply_force(p, neighbor, thread_stats);
}
}
}
// The barrier here ensures that no particle is moved before it is used
// in apply_force above.
#pragma omp barrier
// Now we move each particle.
std::unique_ptr<SimpleIterator<particle_t&> > particles_to_move = next_grid->subgrid(thread_idx, num_actual_threads);
while (particles_to_move->hasNext()) {
particle_t& p = particles_to_move->next();
move(p);
}
// This barrier is probably unnecessary unless save() is going to happen.
#pragma omp barrier
if (!fast) {
//
// save if necessary
//
#pragma omp master
if( fsave && (step%SAVEFREQ) == 0 ) {
save( fsave, n, (*particles).data() );
}
}
// This barrier is probably unnecessary unless save() happened.
#pragma omp barrier
}
#pragma omp critical
per_thread_stats[thread_idx] = thread_stats;
}
simulation_time = read_timer( ) - simulation_time;
// Could do a tree reduce here, but it seems unnecessary.
Stats overall_stats;
for (int thread_idx = 0; thread_idx < max_num_threads; thread_idx++) {
overall_stats.aggregate_left(per_thread_stats[thread_idx]);
}
printf( "n = %d,threads = %d, simulation time = %g seconds", n,num_actual_threads, simulation_time);
if (!fast) {
//
// -the minimum distance absmin between 2 particles during the run of the simulation
// -A Correct simulation will have particles stay at greater than 0.4 (of cutoff) with typical values between .7-.8
// -A simulation were particles don't interact correctly will be less than 0.4 (of cutoff) with typical values between .01-.05
//
// -The average distance absavg is ~.95 when most particles are interacting correctly and ~.66 when no particles are interacting
//
printf( ", absmin = %lf, absavg = %lf", overall_stats.min, overall_stats.avg);
if (overall_stats.min < 0.4) printf ("\nThe minimum distance is below 0.4 meaning that some particle is not interacting");
if (overall_stats.avg < 0.8) printf ("\nThe average distance is below 0.8 meaning that most particles are not interacting");
}
printf("\n");
//
// Printing summary data
//
if( fsum)
fprintf(fsum,"%d %d %g\n",n,num_actual_threads, simulation_time);
//
// Clearing space
//
if( fsum )
fclose( fsum );
if( fsave )
fclose( fsave );
return 0;
}