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improve the new experimental sparse product
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Gael Guennebaud
parent
eda4e98c61
commit
023e0dfb4e
@ -450,6 +450,12 @@ class SparseMatrix
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return *this;
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}
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template<typename Lhs, typename Rhs>
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inline SparseMatrix& operator=(const SparseProduct<Lhs,Rhs>& product)
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{
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return Base::operator=(product);
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}
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template<typename OtherDerived>
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EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other)
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{
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@ -147,13 +147,16 @@ static void ei_sparse_product_impl2(const Lhs& lhs, const Rhs& rhs, ResultType&
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ei_assert(lhs.outerSize() == rhs.innerSize());
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std::vector<bool> mask(rows,false);
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Matrix<Scalar,Dynamic,1> values(rows);
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Matrix<int,Dynamic,1> indices(rows);
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// estimate the number of non zero entries
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float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
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float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
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float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
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float ratio;
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int t200 = rows/(log2(200)*1.39);
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int t = (rows*100)/139;
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res.resize(rows, cols);
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res.reserve(int(ratioRes*rows*cols));
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@ -162,6 +165,7 @@ static void ei_sparse_product_impl2(const Lhs& lhs, const Rhs& rhs, ResultType&
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{
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res.startVec(j);
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int nnz = 0;
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for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
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{
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Scalar y = rhsIt.value();
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@ -173,42 +177,42 @@ static void ei_sparse_product_impl2(const Lhs& lhs, const Rhs& rhs, ResultType&
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if(!mask[i])
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{
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mask[i] = true;
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values[i] = x * y;
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res.insertBackNoCheck(j,i);
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// values[i] = x * y;
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// indices[nnz] = i;
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++nnz;
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}
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else
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res._valuePtr()[mask[i]] += x* y;
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values[i] += x * y;
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}
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}
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// FIXME reserve nnz non zeros
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// FIXME implement fast sort algorithms for very small nnz
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// if the result is sparse enough => use a quick sort
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// otherwise => loop through the entire vector
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SparseInnerVectorSet<ResultType,1> vec(res,j);
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int nnz = vec.nonZeros();
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if(rows/1.39 > nnz * log2(nnz))
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{
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std::sort(vec._innerIndexPtr(), vec._innerIndexPtr()+vec.nonZeros());
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for (typename ResultType::InnerIterator it(res, j); it; ++it)
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{
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it.valueRef() = values[it.index()];
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mask[it.index()] = false;
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}
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}
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else
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{
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// dense path
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int count = 0;
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for(int i=0; i<rows; ++i)
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{
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if(mask[i])
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{
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mask[i] = false;
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vec._innerIndexPtr()[count] = i;
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vec._valuePtr()[count] = i;
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++count;
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}
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}
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}
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// In order to avoid to perform an expensive log2 when the
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// result is clearly very sparse we use a linear bound up to 200.
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// if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t)
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// {
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// if(nnz>1) std::sort(indices.data(),indices.data()+nnz);
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// for(int k=0; k<nnz; ++k)
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// {
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// int i = indices[k];
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// res.insertBackNoCheck(j,i) = values[i];
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// mask[i] = false;
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// }
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// }
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// else
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// {
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// // dense path
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// for(int i=0; i<rows; ++i)
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// {
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// if(mask[i])
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// {
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// mask[i] = false;
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// res.insertBackNoCheck(j,i) = values[i];
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// }
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// }
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// }
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}
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res.finalize();
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@ -218,6 +222,8 @@ static void ei_sparse_product_impl2(const Lhs& lhs, const Rhs& rhs, ResultType&
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template<typename Lhs, typename Rhs, typename ResultType>
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static void ei_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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// return ei_sparse_product_impl2(lhs,rhs,res);
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typedef typename ei_traits<typename ei_cleantype<Lhs>::type>::Scalar Scalar;
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// make sure to call innerSize/outerSize since we fake the storage order.
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@ -274,6 +280,7 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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// std::cerr << __LINE__ << "\n";
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typename ei_cleantype<ResultType>::type _res(res.rows(), res.cols());
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ei_sparse_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res);
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res.swap(_res);
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@ -285,6 +292,7 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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// std::cerr << __LINE__ << "\n";
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// we need a col-major matrix to hold the result
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typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
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SparseTemporaryType _res(res.rows(), res.cols());
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@ -298,6 +306,7 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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// std::cerr << __LINE__ << "\n";
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// let's transpose the product to get a column x column product
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typename ei_cleantype<ResultType>::type _res(res.rows(), res.cols());
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ei_sparse_product_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res);
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@ -310,11 +319,20 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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// std::cerr << "here...\n";
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typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
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ColMajorMatrix colLhs(lhs);
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ColMajorMatrix colRhs(rhs);
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// std::cerr << "more...\n";
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ei_sparse_product_impl<ColMajorMatrix,ColMajorMatrix,ResultType>(colLhs, colRhs, res);
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// std::cerr << "OK.\n";
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// let's transpose the product to get a column x column product
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typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
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SparseTemporaryType _res(res.cols(), res.rows());
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ei_sparse_product_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
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res = _res.transpose();
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// typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
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// SparseTemporaryType _res(res.cols(), res.rows());
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// ei_sparse_product_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
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// res = _res.transpose();
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}
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};
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@ -327,6 +345,7 @@ template<typename Derived>
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template<typename Lhs, typename Rhs>
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inline Derived& SparseMatrixBase<Derived>::operator=(const SparseProduct<Lhs,Rhs>& product)
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{
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// std::cerr << "there..." << typeid(Lhs).name() << " " << typeid(Lhs).name() << " " << (Derived::Flags&&RowMajorBit) << "\n";
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ei_sparse_product_selector<
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typename ei_cleantype<Lhs>::type,
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typename ei_cleantype<Rhs>::type,
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@ -348,7 +367,7 @@ struct ei_sparse_product_selector2<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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ei_sparse_product_impl2<Lhs,Rhs,ResultType>(lhs, rhs, res, 0);
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ei_sparse_product_impl2<Lhs,Rhs,ResultType>(lhs, rhs, res);
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}
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};
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@ -357,11 +376,11 @@ struct ei_sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
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RowMajorMatrix rhsRow = rhs;
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RowMajorMatrix resRow(res.rows(), res.cols());
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ei_sparse_product_impl2<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow);
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res = resRow;
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// typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
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// RowMajorMatrix rhsRow = rhs;
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// RowMajorMatrix resRow(res.rows(), res.cols());
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// ei_sparse_product_impl2<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow);
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// res = resRow;
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}
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};
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