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phase_transition_mpi.cpp
Go to the documentation of this file.
#include "data_type.hpp"
#include "monte_carlo.hpp"
#include "utils.hpp"
#include <algorithm>
#include <fstream>
#include <iostream>
#include <iterator>
#include <mpi.h>
#include <sstream>
int main(int argc, char **argv)
{
if (argc < 5) {
std::cout << "You need at least 4 arguments" << std::endl;
abort();
}
double t0, t1;
t0 = MPI_Wtime();
double start = atof(argv[1]), end = atof(argv[2]);
int points = atoi(argv[3]), N;
int lattice_sizes[] = {20, 40, 60, 80, 100};
double dt = (end - start) / points;
int cycles = atoi(argv[4]);
std::ofstream ofile;
data_t data[points];
// MPI stuff
int rank, cluster_size;
// Initialize MPI
MPI_Init(&argc, &argv);
// Get the cluster size and rank
MPI_Comm_size(MPI_COMM_WORLD, &cluster_size);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
int remainder = points % cluster_size;
double i_start;
int i_points;
// Distribute temperature points
if (rank < remainder) {
i_points = points / cluster_size + 1;
i_start = start + dt * i_points * rank;
}
else {
i_points = points / cluster_size;
i_start = start + dt * (i_points * rank + remainder);
}
data_t i_data[i_points];
std::cout << "Rank " << rank << ": " << i_points << ',' << i_start << '\n';
for (int L : lattice_sizes) {
N = L * L;
for (size_t i = 0; i < i_points; i++) {
i_data[i] = monte_carlo_parallel(L, i_start + dt * i, cycles);
}
if (rank == 0) {
std::copy_n(i_data, i_points, data);
for (size_t i = 1; i < cluster_size; i++) {
if (rank < remainder) {
MPI_Recv((void *)i_data,
sizeof(data_t) * (points / cluster_size + 1),
MPI_CHAR, i, MPI_ANY_TAG, MPI_COMM_WORLD,
MPI_STATUS_IGNORE);
std::copy_n(i_data, points / cluster_size + 1,
data + (points / cluster_size) * i);
}
else {
MPI_Recv((void *)i_data,
sizeof(data_t) * (points / cluster_size), MPI_CHAR,
i, MPI_ANY_TAG, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
std::copy_n(i_data, points / cluster_size,
data + (points / cluster_size) * i + remainder);
}
}
std::stringstream outfile;
outfile << "output/phase_transition/size_" << L << ".txt";
utils::mkpath(utils::dirname(outfile.str()));
ofile.open(outfile.str());
double temp, CV, X;
using utils::scientific_format;
for (size_t i = 0; i < points; i++) {
temp = start + dt * i;
CV = (data[i].E2 - data[i].E * data[i].E)
/ ((double)N * temp * temp);
X = (data[i].M2 - data[i].M_abs * data[i].M_abs)
/ ((double)N * temp);
ofile << scientific_format(temp) << ','
<< scientific_format(data[i].E / N) << ','
<< scientific_format(data[i].M_abs / N) << ','
<< scientific_format(CV) << ',' << scientific_format(X)
<< '\n';
}
ofile.close();
}
else {
MPI_Send(i_data, i_points * sizeof(data_t), MPI_CHAR, 0, rank,
MPI_COMM_WORLD);
}
}
t1 = MPI_Wtime();
if (rank == 0) {
std::cout << "Time: " << t1 - t0 << " seconds\n";
}
MPI_Finalize();
}
data_t
Definition: data_type.hpp:18
data_type.hpp
Header for the data_t type.
monte_carlo.hpp
Functions for monte carlo simulations.
monte_carlo_parallel
data_t monte_carlo_parallel(int L, double T, int cycles)
Execute the Metropolis algorithm for a certain amount of Monte Carlo cycles in parallel.
Definition: monte_carlo.cpp:128
main
int main()
The main function.
Definition: test_suite.cpp:148
utils.hpp
Function prototypes and macros that are useful.