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#include <vector>
#include <iostream>
#include <amgcl/backend/builtin.hpp>
#include <amgcl/adapter/crs_tuple.hpp>
#include <amgcl/make_solver.hpp>
#include <amgcl/amg.hpp>
#include <amgcl/coarsening/smoothed_aggregation.hpp>
#include <amgcl/relaxation/spai0.hpp>
#include <amgcl/solver/bicgstab.hpp>
#include <amgcl/io/mm.hpp>
#include <amgcl/profiler.hpp>
int main(int argc, char *argv[]) {
// The matrix and the RHS file names should be in the command line options:
if (argc < 3) {
std::cerr << "Usage: " << argv[0] << " <matrix.mtx> <rhs.mtx>" << std::endl;
return 1;
}
// The profiler:
amgcl::profiler<> prof("poisson3Db");
// Read the system matrix and the RHS:
ptrdiff_t rows, cols;
std::vector<ptrdiff_t> ptr, col;
std::vector<double> val, rhs;
prof.tic("read");
std::tie(rows, cols) = amgcl::io::mm_reader(argv[1])(ptr, col, val);
std::cout << "Matrix " << argv[1] << ": " << rows << "x" << cols << std::endl;
std::tie(rows, cols) = amgcl::io::mm_reader(argv[2])(rhs);
std::cout << "RHS " << argv[2] << ": " << rows << "x" << cols << std::endl;
prof.toc("read");
// We use the tuple of CRS arrays to represent the system matrix.
// Note that std::tie creates a tuple of references, so no data is actually
// copied here:
auto A = std::tie(rows, ptr, col, val);
// Compose the solver type
// the solver backend:
typedef amgcl::backend::builtin<double> SBackend;
// the preconditioner backend:
#ifdef MIXED_PRECISION
typedef amgcl::backend::builtin<float> PBackend;
#else
typedef amgcl::backend::builtin<double> PBackend;
#endif
typedef amgcl::make_solver<
amgcl::amg<
PBackend,
amgcl::coarsening::smoothed_aggregation,
amgcl::relaxation::spai0
>,
amgcl::solver::bicgstab<SBackend>
> Solver;
// Initialize the solver with the system matrix:
prof.tic("setup");
Solver solve(A);
prof.toc("setup");
// Show the mini-report on the constructed solver:
std::cout << solve << std::endl;
// Solve the system with the zero initial approximation:
int iters;
double error;
std::vector<double> x(rows, 0.0);
prof.tic("solve");
std::tie(iters, error) = solve(A, rhs, x);
prof.toc("solve");
// Output the number of iterations, the relative error,
// and the profiling data:
std::cout << "Iters: " << iters << std::endl
<< "Error: " << error << std::endl
<< prof << std::endl;
}
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