OilfieldToolsNet/opm-simulators
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Improve performance of subset() and superset().

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Using a different construction method for the sparse matrix
used makes the methods a little faster. There is probably
still room for improvement by refactoring to avoid the
sparse matrix products.
This commit is contained in:
Atgeirr Flø Rasmussen 2014-09-25 11:38:48 +02:00
parent 7fffc2c99a
commit 6c7061096a
1 changed files with 11 additions and 17 deletions
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28
opm/autodiff/AutoDiffHelpers.hpp
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@ -187,26 +187,18 @@ struct HelperOps
namespace {
template <typename Scalar, class IntVec>
Eigen::SparseMatrix<Scalar>
constructSubsetSparseMatrix(const int full_size, const IntVec& indices)
{
typedef Eigen::Triplet<Scalar> Tri;
const int subset_size = indices.size();
std::vector<Tri> triplets(subset_size);
for (int i = 0; i < subset_size; ++i) {
triplets[i] = Tri(i, indices[i], 1);
}
Eigen::SparseMatrix<Scalar> sub(subset_size, full_size);
sub.setFromTriplets(triplets.begin(), triplets.end());
return sub;
}
template <typename Scalar, class IntVec>
Eigen::SparseMatrix<Scalar>
constructSupersetSparseMatrix(const int full_size, const IntVec& indices)
{
return constructSubsetSparseMatrix<Scalar>(full_size, indices).transpose();
const int subset_size = indices.size();
Eigen::SparseMatrix<Scalar> mat(full_size, subset_size);
mat.reserve(Eigen::VectorXi::Constant(subset_size, 1));
for (int i = 0; i < subset_size; ++i) {
mat.insert(indices[i], i) = 1;
}
return mat;
}
} // anon namespace
@ -218,7 +210,9 @@ AutoDiffBlock<Scalar>
subset(const AutoDiffBlock<Scalar>& x,
const IntVec& indices)
{
return constructSubsetSparseMatrix<Scalar>(x.value().size(), indices) * x;
Eigen::SparseMatrix<Scalar> sub
= constructSupersetSparseMatrix<Scalar>(x.value().size(), indices).transpose();
return sub * x;
}
@ -229,7 +223,7 @@ Eigen::Array<Scalar, Eigen::Dynamic, 1>
subset(const Eigen::Array<Scalar, Eigen::Dynamic, 1>& x,
const IntVec& indices)
{
return (constructSubsetSparseMatrix<Scalar>(x.size(), indices) * x.matrix()).array();
return (constructSupersetSparseMatrix<Scalar>(x.size(), indices).transpose() * x.matrix()).array();
}