bool EigenLisLinearSolver::solve(EigenMatrix &A_, EigenVector& b_,
EigenVector &x_)
{
static_assert(EigenMatrix::RawMatrixType::IsRowMajor,
"Sparse matrix is required to be in row major storage.");
auto &A = A_.getRawMatrix();
auto &b = b_.getRawVector();
auto &x = x_.getRawVector();
if (!A.isCompressed())
A.makeCompressed();
int nnz = A.nonZeros();
int* ptr = A.outerIndexPtr();
int* col = A.innerIndexPtr();
double* data = A.valuePtr();
LisMatrix lisA(A_.getNumberOfRows(), nnz, ptr, col, data);
LisVector lisb(b.rows(), b.data());
LisVector lisx(x.rows(), x.data());
LisLinearSolver lissol; // TODO not always creat Lis solver here
lissol.setOption(_lis_option);
lissol.solve(lisA, lisb, lisx);
for (std::size_t i=0; i<lisx.size(); i++)
x[i] = lisx[i];
return true; // TODO implement checks
}
bool EigenLinearSolver::solve(EigenMatrix &A, EigenVector& b, EigenVector &x)
{
INFO("------------------------------------------------------------------");
INFO("*** Eigen solver computation");
#ifdef USE_EIGEN_UNSUPPORTED
std::unique_ptr<Eigen::IterScaling<EigenMatrix::RawMatrixType>> scal;
if (_option.scaling)
{
INFO("-> scale");
scal.reset(new Eigen::IterScaling<EigenMatrix::RawMatrixType>());
scal->computeRef(A.getRawMatrix());
b.getRawVector() = scal->LeftScaling().cwiseProduct(b.getRawVector());
}
#endif
auto const success = _solver->solve(A.getRawMatrix(), b.getRawVector(),
x.getRawVector(), _option);
#ifdef USE_EIGEN_UNSUPPORTED
if (scal)
x.getRawVector() = scal->RightScaling().cwiseProduct(x.getRawVector());
#endif
INFO("------------------------------------------------------------------");
return success;
}
//---------------------------------------------------------------------------
int main(int argc, char *argv[]) {
namespace po = boost::program_options;
try {
std::string ifile;
std::string ofile = "partition.mtx";
int nparts, block_size;
po::options_description desc("Options");
desc.add_options()
("help,h", "show help")
("input,i", po::value<std::string>(&ifile)->required(), "Input matrix")
("output,o", po::value<std::string>(&ofile)->default_value(ofile), "Output file")
("nparts,n", po::value<int>(&nparts)->required(), "Number of parts")
("block_size,b", po::value<int>(&block_size)->default_value(1), "Block size")
;
po::positional_options_description pd;
pd.add("input", 1);
po::variables_map vm;
po::store(po::command_line_parser(argc, argv).options(desc).positional(pd).run(), vm);
if (vm.count("help")) {
std::cout << desc << std::endl;
return 0;
}
po::notify(vm);
EigenMatrix A;
precondition(
Eigen::loadMarket(A, ifile),
"Failed to load matrix file"
);
PartVector part = partition(A.rows(), nparts, block_size,
A.outerIndexPtr(), A.innerIndexPtr());
precondition(
Eigen::saveMarketVector(part, ofile),
"Failed to write partition data"
);
} catch (const std::exception &e) {
std::cerr << "Error: " << e.what() << std::endl;
return 1;
}
}
void addToMatrix(EigenMatrix& m,
std::initializer_list<double> values)
{
auto const rows = m.getNumberOfRows();
auto const cols = m.getNumberOfColumns();
assert(static_cast<EigenMatrix::IndexType>(values.size()) == rows*cols);
Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> tmp(rows, cols);
auto it = values.begin();
for (GlobalIndexType r=0; r<rows; ++r) {
for (GlobalIndexType c=0; c<cols; ++c) {
tmp(r, c) = *(it++);
}
}
m.getRawMatrix() += tmp.sparseView();
}
EigenLinearSolver::EigenLinearSolver(EigenMatrix &A,
const std::string /*solver_name*/,
BaseLib::ConfigTree const*const option)
{
if (option)
setOption(*option);
if (!A.getRawMatrix().isCompressed())
A.getRawMatrix().makeCompressed();
if (_option.solver_type==EigenOption::SolverType::SparseLU) {
using SolverType = Eigen::SparseLU<EigenMatrix::RawMatrixType, Eigen::COLAMDOrdering<int>>;
_solver = new details::EigenDirectLinearSolver<SolverType, IEigenSolver>(A.getRawMatrix());
} else if (_option.solver_type==EigenOption::SolverType::BiCGSTAB) {
using SolverType = Eigen::BiCGSTAB<EigenMatrix::RawMatrixType, Eigen::DiagonalPreconditioner<double>>;
_solver = new details::EigenIterativeLinearSolver<SolverType, IEigenSolver>(A.getRawMatrix());
} else if (_option.solver_type==EigenOption::SolverType::CG) {
using SolverType = Eigen::ConjugateGradient<EigenMatrix::RawMatrixType, Eigen::Lower, Eigen::DiagonalPreconditioner<double>>;
_solver = new details::EigenIterativeLinearSolver<SolverType, IEigenSolver>(A.getRawMatrix());
}
}
bool Eigen::solve(SLE& system, base::DataMatrix& B, base::DataMatrix& X) const {
#ifdef USE_EIGEN
Printer::getInstance().printStatusBegin("Solving linear system (Eigen)...");
const size_t n = system.getDimension();
EigenMatrix A = EigenMatrix::Zero(n, n);
size_t nnz = 0;
// parallelize only if the system is cloneable
#pragma omp parallel if (system.isCloneable()) shared(system, A, nnz) default(none)
{
SLE* system2 = &system;
#ifdef _OPENMP
std::unique_ptr<CloneableSLE> clonedSLE;
if (system.isCloneable() && (omp_get_max_threads() > 1)) {
dynamic_cast<CloneableSLE&>(system).clone(clonedSLE);
system2 = clonedSLE.get();
}
#endif /* _OPENMP */
// copy system matrix to Eigen matrix object
#pragma omp for ordered schedule(dynamic)
for (size_t i = 0; i < n; i++) {
for (size_t j = 0; j < n; j++) {
A(i, j) = system2->getMatrixEntry(i, j);
// count nonzero entries
// (not necessary, you can also remove that if you like)
if (A(i, j) != 0) {
#pragma omp atomic
nnz++;
}
}
// status message
if (i % 100 == 0) {
#pragma omp ordered
{
char str[10];
snprintf(str, sizeof(str), "%.1f%%",
static_cast<float_t>(i) / static_cast<float_t>(n) * 100.0);
Printer::getInstance().printStatusUpdate("constructing matrix (" + std::string(str) +
")");
}
}
}
}
Printer::getInstance().printStatusUpdate("constructing matrix (100.0%)");
Printer::getInstance().printStatusNewLine();
// print ratio of nonzero entries
{
char str[10];
float_t nnz_ratio =
static_cast<float_t>(nnz) / (static_cast<float_t>(n) * static_cast<float_t>(n));
snprintf(str, sizeof(str), "%.1f%%", nnz_ratio * 100.0);
Printer::getInstance().printStatusUpdate("nnz ratio: " + std::string(str));
Printer::getInstance().printStatusNewLine();
}
// calculate QR factorization of system matrix
Printer::getInstance().printStatusUpdate("step 1: Householder QR factorization");
::Eigen::HouseholderQR<EigenMatrix> A_QR = A.householderQr();
base::DataVector x(n);
base::DataVector b(n);
X.resize(n, B.getNcols());
// solve system for each RHS
for (size_t i = 0; i < B.getNcols(); i++) {
B.getColumn(i, b);
Printer::getInstance().printStatusNewLine();
if (B.getNcols() == 1) {
Printer::getInstance().printStatusUpdate("step 2: solving");
} else {
Printer::getInstance().printStatusUpdate("step 2: solving (RHS " + std::to_string(i + 1) +
" of " + std::to_string(B.getNcols()) + ")");
}
if (solveInternal(A, A_QR, b, x)) {
X.setColumn(i, x);
} else {
Printer::getInstance().printStatusEnd("error: Could not solve linear system!");
return false;
}
}
Printer::getInstance().printStatusEnd();
return true;
#else
std::cerr << "Error in sle_solver::Eigen::solve: "
<< "SG++ was compiled without Eigen support!\n";
return false;
#endif /* USE_EIGEN */
}
void setMatrix(EigenMatrix& m, Eigen::MatrixXd const& tmp)
{
m.getRawMatrix() = tmp.sparseView();
}
