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https://github.com/OPM/opm-simulators.git
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Implemented statically allocated memory for SparseMatrix
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babrodtk
parent
df1d0f795d
commit
0b1f993588
@ -24,7 +24,6 @@
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#include <Eigen/Eigen>
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#include <Eigen/Sparse>
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#include <boost/any.hpp>
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#include <opm/core/utility/platform_dependent/reenable_warnings.h>
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@ -76,7 +75,7 @@ namespace Opm
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: type_(D),
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rows_(d.rows()),
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cols_(d.cols()),
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data_(Diag(d.diagonal().array().data(), d.diagonal().array().data() + d.rows()))
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diag_(Diag(d.diagonal().array().data(), d.diagonal().array().data() + d.rows()))
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{
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}
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@ -85,15 +84,34 @@ namespace Opm
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explicit AutoDiffMatrix(const Eigen::SparseMatrix<double>& s)
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: type_(S),
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rows_(s.rows()),
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cols_(s.cols()),
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data_(s)
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cols_(s.cols())
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{
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sparse_[0] = s;
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}
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AutoDiffMatrix(const AutoDiffMatrix& other) = default;
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AutoDiffMatrix& operator=(const AutoDiffMatrix& other) = default;
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AutoDiffMatrix(const AutoDiffMatrix& other)
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{
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*this = other;
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}
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AutoDiffMatrix& operator=(const AutoDiffMatrix& other)
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{
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type_ = other.type_;
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rows_ = other.rows_;
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cols_ = other.cols_;
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switch(type_) {
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case D:
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diag_ = other.diag_;
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break;
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case S:
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sparse_[0] = other.sparse_[0];
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break;
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default:
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break;
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}
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}
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@ -118,7 +136,8 @@ namespace Opm
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std::swap(type_, other.type_);
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std::swap(rows_, other.rows_);
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std::swap(cols_, other.cols_);
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data_.swap(other.data_);
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diag_.swap(other.diag_);
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sparse_[0].swap(other.sparse_[0]);
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}
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@ -261,14 +280,13 @@ namespace Opm
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{
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AutoDiffMatrix retval(*this);
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retval.type_ = D;
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retval.data_ = boost::any(Diag(rows_, rhs));
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retval.diag_ = Diag(rows_, rhs);
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return retval;
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}
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case D:
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{
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AutoDiffMatrix retval(*this);
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Diag& d = boost::any_cast<Diag&>(retval.data_);
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for (double& elem : d) {
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for (double& elem : retval.diag_) {
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elem *= rhs;
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}
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return retval;
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@ -276,8 +294,7 @@ namespace Opm
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case S:
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{
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AutoDiffMatrix retval(*this);
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Sparse& s = boost::any_cast<Sparse&>(retval.data_);
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s *= rhs;
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retval.sparse_[0] *= rhs;
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return retval;
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}
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default:
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@ -299,14 +316,13 @@ namespace Opm
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{
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AutoDiffMatrix retval(*this);
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retval.type_ = D;
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boost::any_cast<Diag&>(retval.data_).assign(rows_, 1.0/rhs);
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retval.diag_.assign(rows_, 1.0/rhs);
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return retval;
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}
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case D:
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{
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AutoDiffMatrix retval(*this);
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Diag& d = boost::any_cast<Diag&>(retval.data_);
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for (double& elem : d) {
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for (double& elem : retval.diag_) {
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elem /= rhs;
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}
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return retval;
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@ -314,8 +330,7 @@ namespace Opm
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case S:
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{
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AutoDiffMatrix retval(*this);
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Sparse& s = boost::any_cast<Sparse&>(retval.data_);
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s /= rhs;
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retval.sparse_[0] /= rhs;
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return retval;
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}
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default:
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@ -338,13 +353,11 @@ namespace Opm
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return rhs;
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case D:
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{
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const Diag& d = boost::any_cast<const Diag&>(data_);
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return Eigen::Map<const Eigen::VectorXd>(d.data(), rows_) * rhs;
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return Eigen::Map<const Eigen::VectorXd>(diag_.data(), rows_) * rhs;
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}
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case S:
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{
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const Sparse& s = boost::any_cast<const Sparse&>(data_);
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return s * rhs;
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return sparse_[0] * rhs;
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}
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default:
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OPM_THROW(std::logic_error, "Invalid AutoDiffMatrix type encountered: " << type_);
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@ -364,7 +377,7 @@ namespace Opm
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retval.type_ = D;
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retval.rows_ = lhs.rows_;
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retval.cols_ = rhs.cols_;
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retval.data_ = boost::any(Diag(lhs.rows_, 2.0));
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retval.diag_.assign(lhs.rows_, 2.0);
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return retval;
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}
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@ -374,9 +387,8 @@ namespace Opm
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assert(lhs.type_ == D);
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assert(rhs.type_ == I);
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AutoDiffMatrix retval = lhs;
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Diag& d = boost::any_cast<Diag&>(retval.data_);
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for (int r = 0; r < lhs.rows_; ++r) {
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d[r] += 1.0;
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retval.diag_[r] += 1.0;
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}
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return retval;
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}
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@ -386,10 +398,8 @@ namespace Opm
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assert(lhs.type_ == D);
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assert(rhs.type_ == D);
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AutoDiffMatrix retval = lhs;
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Diag& d_lhs = boost::any_cast<Diag&>(retval.data_);
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const Diag& d_rhs = boost::any_cast<const Diag&>(rhs.data_);
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for (int r = 0; r < lhs.rows_; ++r) {
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d_lhs[r] += d_rhs[r];
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retval.diag_[r] += rhs.diag_[r];
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}
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return retval;
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}
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@ -399,12 +409,11 @@ namespace Opm
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assert(lhs.type_ == S);
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assert(rhs.type_ == I);
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AutoDiffMatrix retval;
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Eigen::SparseMatrix<double> ident = spdiag(Eigen::VectorXd::Ones(lhs.rows_));
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retval.type_ = S;
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retval.rows_ = lhs.rows_;
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retval.cols_ = rhs.cols_;
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const Sparse& s = boost::any_cast<const Sparse&>(lhs.data_);
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retval.data_ = static_cast<Sparse>(s + ident);
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retval.sparse_[0] = lhs.sparse_[0];
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retval.sparse_[0] += spdiag(Eigen::VectorXd::Ones(lhs.rows_));
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return retval;
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}
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@ -413,12 +422,11 @@ namespace Opm
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assert(lhs.type_ == S);
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assert(rhs.type_ == D);
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AutoDiffMatrix retval;
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Sparse diag = spdiag(boost::any_cast<const Diag&>(rhs.data_));
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retval.type_ = S;
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retval.rows_ = lhs.rows_;
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retval.cols_ = rhs.cols_;
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const Sparse& s = boost::any_cast<const Sparse&>(lhs.data_);
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retval.data_ = static_cast<Sparse>(s + diag);
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retval.sparse_[0] = lhs.sparse_[0];
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retval.sparse_[0] += spdiag(rhs.diag_);
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return retval;
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}
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@ -426,13 +434,8 @@ namespace Opm
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{
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assert(lhs.type_ == S);
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assert(rhs.type_ == S);
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AutoDiffMatrix retval;
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retval.type_ = S;
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retval.rows_ = lhs.rows_;
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retval.cols_ = rhs.cols_;
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const Sparse& s_lhs = boost::any_cast<const Sparse&>(lhs.data_);
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const Sparse& s_rhs = boost::any_cast<const Sparse&>(rhs.data_);
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retval.data_ = static_cast<Sparse>(s_lhs + s_rhs);
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AutoDiffMatrix retval = lhs;
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retval.sparse_[0] += rhs.sparse_[0];
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return retval;
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}
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@ -444,16 +447,9 @@ namespace Opm
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{
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assert(lhs.type_ == D);
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assert(rhs.type_ == D);
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AutoDiffMatrix retval;
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retval.type_ = D;
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retval.rows_ = lhs.rows_;
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retval.cols_ = rhs.cols_;
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retval.data_ = boost::any(Diag(lhs.rows_));
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Diag& d = boost::any_cast<Diag&>(retval.data_);
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const Diag& d_lhs = boost::any_cast<const Diag&>(lhs.data_);
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const Diag& d_rhs = boost::any_cast<const Diag&>(rhs.data_);
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AutoDiffMatrix retval = lhs;
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for (int r = 0; r < lhs.rows_; ++r) {
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d[r] = d_lhs[r] * d_rhs[r];
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retval.diag_[r] *= rhs.diag_[r];
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}
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return retval;
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}
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@ -466,11 +462,8 @@ namespace Opm
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retval.type_ = S;
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retval.rows_ = lhs.rows_;
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retval.cols_ = rhs.cols_;
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retval.data_ = boost::any(Sparse(retval.rows_, retval.cols_)); //FIXME: Superfluous?
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const Diag& a = boost::any_cast<const Diag&>(lhs.data_);
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const Sparse& b = boost::any_cast<const Sparse&>(rhs.data_);
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Sparse& c = boost::any_cast<Sparse&>(retval.data_);
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fastDiagSparseProduct(a, b, c);
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retval.sparse_[0] = Sparse(retval.rows_, retval.cols_);
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fastDiagSparseProduct(lhs.diag_, rhs.sparse_[0], retval.sparse_[0]);
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return retval;
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}
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@ -482,11 +475,8 @@ namespace Opm
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retval.type_ = S;
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retval.rows_ = lhs.rows_;
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retval.cols_ = rhs.cols_;
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retval.data_ = boost::any(Sparse(retval.rows_, retval.cols_)); //FIXME: Superfluous?
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const Sparse& a = boost::any_cast<const Sparse&>(lhs.data_);
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const Diag& b = boost::any_cast<const Diag&>(rhs.data_);
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Sparse& c = boost::any_cast<Sparse&>(retval.data_);
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fastSparseDiagProduct(a, b, c);
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retval.sparse_[0] = Sparse(retval.rows_, retval.cols_);
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fastSparseDiagProduct(lhs.sparse_[0], rhs.diag_, retval.sparse_[0]);
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return retval;
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}
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@ -498,11 +488,8 @@ namespace Opm
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retval.type_ = S;
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retval.rows_ = lhs.rows_;
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retval.cols_ = rhs.cols_;
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retval.data_ = boost::any(Sparse(retval.rows_, retval.cols_)); //FIXME: Superfluous?
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const Sparse& a = boost::any_cast<const Sparse&>(lhs.data_);
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const Sparse& b = boost::any_cast<const Sparse&>(rhs.data_);
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Sparse& c = boost::any_cast<Sparse&>(retval.data_);
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fastSparseProduct(a, b, c);
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retval.sparse_[0] = Sparse(retval.rows_, retval.cols_);
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fastSparseProduct(lhs.sparse_[0], rhs.sparse_[0], retval.sparse_[0]);
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return retval;
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}
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@ -518,10 +505,10 @@ namespace Opm
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s = spdiag(Eigen::VectorXd::Ones(rows_));
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return;
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case D:
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s = spdiag(boost::any_cast<const Diag&>(data_));
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s = spdiag(diag_);
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return;
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case S:
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s = boost::any_cast<const Sparse&>(data_);
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s = sparse_[0];
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return;
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}
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}
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@ -547,7 +534,7 @@ namespace Opm
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case D:
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return rows_;
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case S:
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return boost::any_cast<const Sparse&>(data_).nonZeros();
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return sparse_->nonZeros();
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default:
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OPM_THROW(std::logic_error, "Invalid AutoDiffMatrix type encountered: " << type_);
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}
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@ -562,9 +549,9 @@ namespace Opm
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case I:
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return (row == col) ? 1.0 : 0.0;
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case D:
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return (row == col) ? boost::any_cast<const Diag&>(data_)[row] : 0.0;
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return (row == col) ? diag_[row] : 0.0;
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case S:
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return boost::any_cast<const Sparse&>(data_).coeff(row, col);
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return sparse_->coeff(row, col);
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default:
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OPM_THROW(std::logic_error, "Invalid AutoDiffMatrix type encountered: " << type_);
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}
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@ -575,22 +562,24 @@ namespace Opm
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MatrixType type_;
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int rows_;
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int cols_;
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boost::any data_;
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/*
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std::vector<double> d_;
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Eigen::SparseMatrix<double> s_;
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*/
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Diag diag_;
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/**
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* Eigen uses memory allocation within the default constructor, so that
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* Sparse a; actually calls malloc. To prevent this, we here use
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* Sparse a[1], and only construct the object when needed.
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*/
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Sparse sparse_[1];
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template <class V>
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static inline
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Eigen::SparseMatrix<double>
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Sparse
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spdiag(const V& d)
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{
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typedef Eigen::SparseMatrix<double> M;
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const int n = d.size();
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M mat(n, n);
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Sparse mat(n, n);
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mat.reserve(Eigen::ArrayXi::Ones(n, 1));
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for (M::Index i = 0; i < n; ++i) {
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for (Sparse::Index i = 0; i < n; ++i) {
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if (d[i] != 0.0) {
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mat.insert(i, i) = d[i];
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}
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