diff --git a/bench/fmm-computation.hpp b/bench/fmm-computation.hpp index a60a9f8516afbfecf2b0627bf498e0cda1114b52..c7ca40f5cb1db1df896d9cac69217815de88ce17 100644 --- a/bench/fmm-computation.hpp +++ b/bench/fmm-computation.hpp @@ -17,11 +17,14 @@ #include "scalfmm/tree/cell.hpp" #include "scalfmm/tree/for_each.hpp" #include "scalfmm/tree/group_tree_view.hpp" +#include "scalfmm/tree/io.hpp" #include "scalfmm/tree/leaf_view.hpp" +#include "scalfmm/tree/utils.hpp" #include "scalfmm/utils/accurater.hpp" #include "scalfmm/matrix_kernels/debug.hpp" +#include "scalfmm/matrix_kernels/gaussian.hpp" #include "scalfmm/matrix_kernels/laplace.hpp" #include "scalfmm/utils/parameters.hpp" @@ -34,6 +37,25 @@ #include <random> #include <vector> +namespace local_utils +{ + // Primary template handles matrix kernels that are not gaussian kernels + template<typename MatrixKernelType> + struct is_gaussian_kernel : std::false_type + { + }; + + // Partial specialization recognizes gaussian kernels + template<typename MatrixKernelType> + struct is_gaussian_kernel<scalfmm::matrix_kernels::gaussian<MatrixKernelType>> : std::true_type + { + }; + + // Helper alias + template<typename MatrixKernelType> + inline static constexpr bool is_gaussian_kernel_v = is_gaussian_kernel<MatrixKernelType>::value; +} // namespace local_utils + namespace local_args { struct interp_settings : cpp_tools::cl_parser::required_tag @@ -53,7 +75,8 @@ namespace local_args "Matrix kernel:" "\n0) one_over_r\n1) one_over_r [non-symmetric]\n2) one_over_r [non-homogenous]" "\n3) one_over_r [non-homogenous - non-symmetric]\n4) grad_one_over_r<d>\n5) val_grad_one_over_r<d>" - "\n6) grad_one_over_r<d> (optimized)\n7) val_grad_one_over_r<d> (optimized)\n"; + "\n6) grad_one_over_r<d> (optimized)\n7) val_grad_one_over_r<d> (optimized)" + "\n8) gaussian (coeff=2.0) \n"; std::string input_hint = "int"; using type = int; type def = 0; @@ -188,7 +211,22 @@ namespace local_args flagged }; }; - + struct gauss_coeff + { + cpp_tools::cl_parser::str_vec flags = {"--gauss-coeff", "-gc"}; + std::string description = "Coefficient of the gaussian."; + using type = double; + std::string input_hint = "double"; /*!< The input hint */ + type def = type(1.0); + }; + struct gauss_regul + { + cpp_tools::cl_parser::str_vec flags = {"--gauss-regul", "-gr"}; + std::string description = "Regularisation Coefficient of the gaussian on the diagonal term."; + using type = double; + std::string input_hint = "double"; /*!< The input hint */ + type def = type(0.0); + }; template<typename T> std::ostream& operator<<(std::ostream& os, const std::vector<T>& vec) { @@ -426,11 +464,24 @@ auto run(ParserType const& parser) -> void // construct the fmm operator // construct the near field near_field_type near_field; - // a reference on the matrix_kernel of the near_field - auto near_mk = near_field.matrix_kernel(); + auto& near_mk = near_field.matrix_kernel(); + + far_matrix_kernel_type far_mk{}; + if constexpr(local_utils::is_gaussian_kernel_v<near_matrix_kernel_type>) + { + value_type coeff{parser.template get<local_args::gauss_coeff>()}; + value_type reg{parser.template get<local_args::gauss_regul>()}; + // if we deal with a gaussian kernel, we set the different parameters + // a reference on the matrix_kernel of the near_field + near_mk.set_coeff(coeff); + near_mk.set_epsilon(reg); + + far_mk.set_coeff(coeff); + far_mk.set_epsilon(reg); + } // build the approximation used in the near field - interpolator_type interpolator(order, tree_height, box.width(0)); + interpolator_type interpolator(far_mk, order, tree_height, box.width(0)); far_field_type far_field(interpolator); // construct the fmm operator fmm_operator_type fmm_operator(near_field, far_field); @@ -440,6 +491,9 @@ auto run(ParserType const& parser) -> void if(fmm_computation) { // FMM algorithm + std::cout << cpp_tools::colors::blue; + fmm_operator.settings(std::cout); + std::cout << cpp_tools::colors::reset; for(std::size_t i = 0; i < nb_runs; ++i) { // std::cout << "\tFMM run " << i << "\n"; @@ -724,6 +778,31 @@ auto select_kernel(Parser const& parser) -> void run<value_type, dimension, fmm_operator_type>(parser); break; } + case 8: + { + std::cout << cpp_tools::colors::cyan; + std::cout << "[run] matrix kernel 8) gaussian" << std::endl; + std::cout << cpp_tools::colors::reset; + + // matrix kernels + using far_matrix_kernel_type = scalfmm::matrix_kernels::gaussian<value_type>; + using near_matrix_kernel_type = scalfmm::matrix_kernels::gaussian<value_type>; + + // near field + using near_field_type = scalfmm::operators::near_field_operator<near_matrix_kernel_type>; + + // far field + using interp_settings_type = InterpSettingsType; + using interpolation_type = + scalfmm::interpolation::interpolator<value_type, dimension, far_matrix_kernel_type, interp_settings_type>; + using far_field_type = scalfmm::operators::far_field_operator<interpolation_type, false>; + + // fmm operators + using fmm_operator_type = scalfmm::operators::fmm_operators<near_field_type, far_field_type>; + + run<value_type, dimension, fmm_operator_type>(parser); + break; + } default: { throw std::invalid_argument("Invalid choice for kernel."); @@ -879,11 +958,12 @@ auto get_parser(int argc, char* argv[], Args&&... args) auto main([[maybe_unused]] int argc, [[maybe_unused]] char* argv[]) -> int { // Parameter handling - auto parser = get_parser( - argc, argv, local_args::size{}, local_args::order{}, local_args::tree_height{}, local_args::group_size{}, - local_args::nb_runs{}, local_args::direct_computation{}, local_args::fmm_computation{}, local_args::input_file{}, - local_args::output_file{}, local_args::dimension{}, local_args::use_float{}, local_args::kernel{}, - local_args::thread_count{}, local_args::interp_settings{}, local_args::operators_to_proceed{}); + auto parser = get_parser(argc, argv, local_args::size{}, local_args::order{}, local_args::tree_height{}, + local_args::group_size{}, local_args::nb_runs{}, local_args::direct_computation{}, + local_args::fmm_computation{}, local_args::input_file{}, local_args::output_file{}, + local_args::dimension{}, local_args::use_float{}, local_args::kernel{}, + local_args::thread_count{}, local_args::interp_settings{}, + local_args::operators_to_proceed{}, local_args::gauss_coeff(), local_args::gauss_regul()); select_precision(parser); diff --git a/checks/CMakeLists.txt b/checks/CMakeLists.txt index 85a49ab41189e8bb61db9fdff4c56fa26edc4263..db1f1388c43eed94cc0792075c8ca7593ea1f291 100644 --- a/checks/CMakeLists.txt +++ b/checks/CMakeLists.txt @@ -41,6 +41,8 @@ set(source_check_files check_reset.cpp check_points.cpp check_block.cpp + + check_smooth_kernel.cpp ) if(${CMAKE_PROJECT_NAME}_BUILD_PBC) diff --git a/checks/check_2d.cpp b/checks/check_2d.cpp index bd8ec0071167bf5bea8ff479da9b0e27a083b39e..e7bca81e00ffd0380b2a0f8352905b60840af77a 100644 --- a/checks/check_2d.cpp +++ b/checks/check_2d.cpp @@ -29,7 +29,7 @@ #include "scalfmm/tools/fma_loader.hpp" // Tree #include "scalfmm/interpolation/grid_storage.hpp" -#include "scalfmm/lists/sequential.hpp" +#include "scalfmm/lists/lists.hpp" #include "scalfmm/tree/box.hpp" #include "scalfmm/tree/cell.hpp" #include "scalfmm/tree/group_tree_view.hpp" @@ -264,19 +264,20 @@ auto run(const int& tree_height, const int& group_size, const std::size_t order, const bool mutual_near = false; typename FMM_OPERATOR_TYPE::near_field_type near_field(mk_near, mutual_near); - // - - std::cout << cpp_tools::colors::blue << "Fmm with kernels: " << std::endl - << " near " << mk_near.name() << " mutual " << std::boolalpha << near_field.mutual() << std::endl - << " far " << mk_far.name() << std::endl - << cpp_tools::colors::reset; FMM_OPERATOR_TYPE fmm_operator(near_field, far_field); // + std::cout << cpp_tools::colors::blue; + fmm_operator.settings(std::cout); + std::cout << cpp_tools::colors::reset; + // + bool verbose{true}; + // // Build interaction lists - int const& separation_criterion = fmm_operator.near_field().separation_criterion(); - bool const& mutual = fmm_operator.near_field().mutual(); - scalfmm::list::sequential::build_interaction_lists(tree, tree, separation_criterion, mutual); + // int const& separation_criterion = fmm_operator.near_field().separation_criterion(); + // bool const& mutual = fmm_operator.near_field().mutual(); + // scalfmm::list::sequential::build_interaction_lists(tree, tree, separation_criterion, mutual); + scalfmm::list::omp::build_interaction_lists(tree, tree, fmm_operator); // scalfmm::io::trace(std::cout, tree, 4); auto operator_to_proceed = scalfmm::algorithms::all; diff --git a/checks/check_smooth_kernel.cpp b/checks/check_smooth_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1a3436b6c86abd22260a497a93399a616828b394 --- /dev/null +++ b/checks/check_smooth_kernel.cpp @@ -0,0 +1,323 @@ +// scalfmm +#include "scalfmm/algorithms/fmm.hpp" +#include "scalfmm/algorithms/full_direct.hpp" +#include "scalfmm/container/particle.hpp" +#include "scalfmm/interpolation/interpolation.hpp" +#include "scalfmm/matrix_kernels/debug.hpp" +#include "scalfmm/matrix_kernels/gaussian.hpp" +#include "scalfmm/matrix_kernels/laplace.hpp" +#include "scalfmm/meta/utils.hpp" +#include "scalfmm/operators/fmm_operators.hpp" +#include "scalfmm/tools/fma_loader.hpp" +#include "scalfmm/tree/tree.hpp" +#include "scalfmm/utils/accurater.hpp" + +// cpp tools +#include <cpp_tools/cl_parser/help_descriptor.hpp> +#include <cpp_tools/cl_parser/tcli.hpp> + +// STL +#include <random> +#include <vector> + +#define PART_VAR + +namespace local_args +{ + struct tree_height + { + cpp_tools::cl_parser::str_vec flags = {"--tree-height", "-th"}; + std::string description = "Height of the tree."; + using type = std::size_t; + std::string input_hint = "int"; /*!< The input hint */ + type def = 4; + }; + + struct nb_particles + { + cpp_tools::cl_parser::str_vec flags = {"--N", "--number-particles"}; + std::string description = "Numbre of particles to generate"; + using type = std::size_t; + std::string input_hint = "int"; /*!< The input hint */ + type def = 1000; + }; + + struct order + { + cpp_tools::cl_parser::str_vec flags = {"--order", "-o"}; + std::string description = "Order of the approximation."; + using type = std::size_t; + std::string input_hint = "int"; /*!< The input hint */ + type def = 4; + }; + struct gauss_coeff + { + cpp_tools::cl_parser::str_vec flags = {"--gauss-coeff", "-gc"}; + std::string description = "Coefficient of the gaussian."; + using type = double; + std::string input_hint = "double"; /*!< The input hint */ + type def = type(1.0); + }; + struct gauss_regul + { + cpp_tools::cl_parser::str_vec flags = {"--gauss-regul", "-gr"}; + std::string description = "Regularisation Coefficient of the gaussian on the diagonal term."; + using type = double; + std::string input_hint = "double"; /*!< The input hint */ + type def = type(0.0); + }; + template<typename T> + std::ostream& operator<<(std::ostream& os, const std::vector<T>& vec) + { + os << "{ "; + for(auto el: vec) + { + os << el << ' '; + } + os << "}"; + return os; + } + + template<typename ParserType, typename T, typename... Args> + auto print_parameters(ParserType const& parser, T&& value, Args&&... args) -> void + { + T dummy; + std::cout << cpp_tools::colors::cyan; + std::cout << std::boolalpha; + std::cout << "[param] " << std::left << std::setw(24) << dummy.flags[0] << " = " << parser.template get<T>() + << "\n"; + if constexpr(sizeof...(args) > 0) + { + print_parameters(parser, std::forward<Args>(args)...); + } + std::cout << cpp_tools::colors::reset; + } +} // namespace local_args + +namespace local_utils +{ + // Primary template handles matrix kernels that are not gaussian kernels + template<typename MatrixKernelType> + struct is_gaussian_kernel : std::false_type + { + }; + + // Partial specialization recognizes gaussian kernels + template<typename MatrixKernelType> + struct is_gaussian_kernel<scalfmm::matrix_kernels::gaussian<MatrixKernelType>> : std::true_type + { + }; + + // Helper alias + template<typename MatrixKernelType> + inline static constexpr bool is_gaussian_kernel_v = is_gaussian_kernel<MatrixKernelType>::value; +} // namespace local_utils + +template<typename ValueType, std::size_t Dimension, typename MatrixKernelType, typename ParserType> +auto run(ParserType const& parser) -> void +{ + using value_type = ValueType; + static constexpr std::size_t dimension = Dimension; + using matrix_kernel_type = MatrixKernelType; + + static constexpr std::size_t nb_inputs{matrix_kernel_type::km}; + static constexpr std::size_t nb_outputs{matrix_kernel_type::kn}; + + std::cout << cpp_tools::colors::blue << "dimension: " << dimension << std::endl << cpp_tools::colors::reset; +#ifdef PART_VAR + using particle_type = + scalfmm::container::particle<value_type, dimension, value_type, nb_inputs, value_type, nb_outputs, std::int64_t>; +#else + using particle_type = + scalfmm::container::particle<value_type, dimension, value_type, nb_inputs, value_type, nb_outputs>; + +#endif + using position_type = typename particle_type::position_type; + using box_type = scalfmm::component::box<position_type>; + + using container_type = std::vector<particle_type>; + + // interpolation types + using near_field_type = scalfmm::operators::near_field_operator<matrix_kernel_type>; + using interpolator_type = scalfmm::interpolation::interpolator<value_type, dimension, matrix_kernel_type, + scalfmm::options::uniform_<scalfmm::options::fft_>>; + // scalfmm::options::chebyshev_<scalfmm::options::low_rank_>>; + using far_field_type = scalfmm::operators::far_field_operator<interpolator_type>; + using fmm_operator_type = scalfmm::operators::fmm_operators<near_field_type, far_field_type>; + + // tree types + using cell_type = scalfmm::component::cell<typename interpolator_type::storage_type>; + using leaf_type = scalfmm::component::leaf_view<particle_type>; + using group_tree_type = scalfmm::component::group_tree_view<cell_type, leaf_type, box_type>; + + const std::size_t nb_particles{parser.template get<local_args::nb_particles>()}; + const std::size_t tree_height{parser.template get<local_args::tree_height>()}; + const std::size_t order{parser.template get<local_args::order>()}; + + // we construct the tree + container_type container(nb_particles); + + const value_type box_width{2.}; + const position_type box_center(1.); + box_type box(box_width, box_center); + + // random generator + // std::random_device rd; + // std::mt19937 gen(rd()); + std::mt19937 gen(123); + std::uniform_real_distribution<value_type> dis(0.0, 2.0); + auto random_r = [&dis, &gen]() { return dis(gen); }; + + // inserting particles in the container + for(std::size_t idx = 0; idx < nb_particles; ++idx) + { + // particle_type p; + particle_type& p = container[idx]; + for(auto& e: p.position()) + { + e = random_r(); + } + for(auto& e: p.inputs()) + { + e = random_r(); + } + for(auto& e: p.outputs()) + { + e = value_type(0.); + } +#ifdef PART_VAR + + p.variables(idx); +#endif + } + + // construct the near field + near_field_type near_field{}; + + // a reference on the matrix_kernel of the near_field + auto& mk = near_field.matrix_kernel(); + + // print name of the kernel + std::cout << "- Kernel name: " << mk.name() << std::endl; + // if we deal with a gaussian kernel, we set the different parameters + if constexpr(local_utils::is_gaussian_kernel_v<matrix_kernel_type>) + { + value_type coeff{parser.template get<local_args::gauss_coeff>()}; + value_type reg{parser.template get<local_args::gauss_regul>()}; + mk.set_coeff(coeff); + mk.set_epsilon(reg); + } + + // test whether the kernel is smooth or not (in that case, just print message to the standard output) + if constexpr(scalfmm::meta::is_smooth_v<matrix_kernel_type>) + { + std::cout << "Kernel is smooth" << std::endl; + } + else + { + std::cout << "Kernel is not smooth" << std::endl; + } + std::cout << " Good order " << int(nb_particles / std::pow(2, tree_height)) << std::endl; + // build the approximation used in the near field + interpolator_type interpolator(mk, order, tree_height, box.width(0)); + far_field_type far_field(interpolator); + // construct the fmm operator + fmm_operator_type fmm_operator(near_field, far_field); + // using settings = typename interpolator_type::settings; + // settings s; + + std::cout << cpp_tools::colors::blue; + fmm_operator.settings(std::cout); + std::cout << cpp_tools::colors::reset; + + const std::size_t group_size{10}; // the number of cells and leaf grouped in the tree + group_tree_type tree(tree_height, order, box, group_size, group_size, container); + + tree.statistics("smmoth kernel", std::cout); + + scalfmm::list::sequential::build_interaction_lists(tree, tree, fmm_operator); + // now we have everything to call the fmm algorithm + scalfmm::algorithms::fmm[scalfmm::options::_s(scalfmm::options::seq)](tree, fmm_operator); + + scalfmm::algorithms::full_direct(container, mk); + // + scalfmm::utils::accurater<value_type> error{}; +#ifdef PART_VAR + + scalfmm::component::for_each_leaf(std::cbegin(tree), std::cend(tree), + [&container, &error](auto const& leaf) + { + // loop on the particles of the leaf + for(auto const p_ref: leaf) + { + // build a particle + const auto p = typename leaf_type::const_proxy_type(p_ref); + // + const auto& idx = std::get<0>(p.variables()); + + auto const& output_ref = container[idx].outputs(); + auto const& output = p.outputs(); + for(std::size_t i{0}; i < nb_outputs; ++i) + { + error.add(output_ref.at(i), output.at(i)); + } + } + }); + std::cout << cpp_tools::colors::red; + std::cout << error << '\n'; + std::cout << cpp_tools::colors::reset; + +#endif + + std::cout << " write direct computation in data_smmooth_direct.fma\n"; + scalfmm::io::FFmaGenericWriter<value_type> writer("data_smmooth_direct.fma", false); + writer.writeDataFrom(container, box.center(), tree.box_width()); + std::cout << " write fmm computation in data_smmooth_fmm.fma\n\n"; + scalfmm::io::FFmaGenericWriter<value_type> writer2("data_smmooth_fmm.fma", false); + writer2.writeDataFromTree(tree, nb_particles); +} + +template<typename... Args> +auto get_parser(int argc, char* argv[], Args&&... args) +{ + auto parser = cpp_tools::cl_parser::make_parser(cpp_tools::cl_parser::help{}, std::forward<Args>(args)...); + parser.parse(argc, argv); + local_args::print_parameters(parser, std::forward<Args>(args)...); + std::cout << "\n"; + + return parser; +} + +auto main([[maybe_unused]] int argc, [[maybe_unused]] char* argv[]) -> int +{ + using value_type = double; + static constexpr std::size_t dimension = 2; + + const std::size_t order{8}; + const std::size_t tree_height{4}; + const std::size_t nb_particles{10000}; + + // Parameter handling + auto parser = get_parser(argc, argv, local_args::nb_particles{}, local_args::tree_height{}, local_args::order{}, + local_args::gauss_coeff(), local_args::gauss_regul()); + + // Gaussian kernel + { + using matrix_kernel_type = scalfmm::matrix_kernels::gaussian<value_type>; + run<value_type, dimension, matrix_kernel_type>(parser); + } +#ifdef ALL_KERNELS + // One over r (modified) + { + using matrix_kernel_type = scalfmm::matrix_kernels::debug::one_over_r_modified; + run<value_type, dimension, matrix_kernel_type>(parser); + } +#endif + + // One over r (classical) + { + using matrix_kernel_type = scalfmm::matrix_kernels::laplace::one_over_r; + run<value_type, dimension, matrix_kernel_type>(parser); + } + return 0; +} diff --git a/data_smmooth_direct.fma b/data_smmooth_direct.fma new file mode 100644 index 0000000000000000000000000000000000000000..b04907e656263e093b58536f0b986b4f6555e685 --- /dev/null +++ b/data_smmooth_direct.fma @@ -0,0 +1,10002 @@ +8 4 2 1 +10000 1 1 1 +1.42591064316056 0.856941850057868 1.38176970287545 17583.1683558948 +1.43830061776159 0.982237865552059 1.56005552104819 17614.1146505939 +0.821848736135645 1.15938859511392 0.279901526626633 17161.3726180801 +0.80203510839125 1.25463400797135 0.648301792204869 17877.6975690883 +0.489518559742054 1.38951035032965 1.18780479598699 16290.6935167866 +1.26358404091466 0.880514354995681 0.167452970601363 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1.16546286650973 17831.6334599494 +1.58172169724153 1.98717854934139 0.721985914415696 11487.7701229935 +1.10343776864601 1.49577641804514 1.25739871283056 17746.6731991247 +1.20577459048118 1.70233850340152 0.941352066848036 15496.2974424403 +0.32223303427027 0.3162301255123 1.43717788382092 14127.493290145 +1.64809743985086 0.875630026938491 0.0478373055664426 15856.1316771463 +0.29543956320341 1.32753412429443 0.207789056139579 15577.3237946897 +0.991905810627461 1.81903466425586 1.92894256272938 14532.8732979899 +1.90751907009308 1.28885667700193 0.396819083122217 13889.8743637662 +0.342008373642487 1.18249435404028 0.459785163393275 15917.9157268975 +1.93769882578493 1.27028327614396 0.949742787754973 12812.9334953441 diff --git a/examples/fmm_source_target.cpp b/examples/fmm_source_target.cpp index 9cf7354cbea7e8a947ad364a29ff8ca21db7f93d..a5d261818ffbdc8dfae1a4b1abd35a4f9081408c 100644 --- a/examples/fmm_source_target.cpp +++ b/examples/fmm_source_target.cpp @@ -330,7 +330,7 @@ auto fmm_run(const std::string& input_source_file, const std::string& input_targ fmm_operators_type fmm_operator(near_field, far_field); auto neighbour_separation = fmm_operator.near_field().separation_criterion(); // - scalfmm::list::sequential::build_interaction_lists(tree_source, tree_target, neighbour_separation, mutual); + scalfmm::list::sequential::build_interaction_lists(tree_source, tree_target, fmm_operator); auto operator_to_proceed = scalfmm::algorithms::all; #ifndef ST_USE_OMP scalfmm::algorithms::omp::task_dep(tree_source, tree_target, fmm_operator, operator_to_proceed); diff --git a/examples/test_dimension.cpp b/examples/test_dimension.cpp index 196e14cfa83b7ac9eebd36be1867121bb9a1b3f9..e943396c3a263394e91666177d24e41a987067ee 100644 --- a/examples/test_dimension.cpp +++ b/examples/test_dimension.cpp @@ -205,7 +205,7 @@ auto run(const std::string& input_file, const int& tree_height, const int& group auto const separation_criterion = fmm_operator.near_field().separation_criterion(); time.tic(); - scalfmm::list::sequential::build_interaction_lists(tree, tree, separation_criterion, mutual); + scalfmm::list::sequential::build_interaction_lists(tree, tree, fmm_operator); time.tac(); std::cout << cpp_tools::colors::yellow << "Interaction list built in " << time.elapsed() << " ms\n" << cpp_tools::colors::reset; diff --git a/examples/test_dimension_low_rank.cpp b/examples/test_dimension_low_rank.cpp index 602b6709f8795103a22d325ba1925c2f05fbf9d1..54e59abcb9d6fdce04ec2f420d96558b4925f612 100644 --- a/examples/test_dimension_low_rank.cpp +++ b/examples/test_dimension_low_rank.cpp @@ -210,7 +210,7 @@ auto run(const std::string& input_file, const int& tree_height, const int& group auto const separation_criterion = fmm_operator.near_field().separation_criterion(); time.tic(); - scalfmm::list::sequential::build_interaction_lists(tree, tree, separation_criterion, mutual); + scalfmm::list::sequential::build_interaction_lists(tree, tree, fmm_operator); time.tac(); std::cout << cpp_tools::colors::yellow << "Interaction list built in " << time.elapsed() << " ms\n" << cpp_tools::colors::reset; diff --git a/examples/test_dimension_omp.cpp b/examples/test_dimension_omp.cpp index 2a86413c3868e7a8fb33c6166205951982cdb945..cd3e740fa10f831fc612646dccc11be61637c1a4 100644 --- a/examples/test_dimension_omp.cpp +++ b/examples/test_dimension_omp.cpp @@ -204,7 +204,7 @@ auto run(const std::string& input_file, const int& tree_height, const int& group auto const separation_criterion = fmm_operator.near_field().separation_criterion(); time.tic(); - scalfmm::list::sequential::build_interaction_lists(tree, tree, separation_criterion, mutual); + scalfmm::list::sequential::build_interaction_lists(tree, tree, fmm_operator); time.tac(); std::cout << cpp_tools::colors::yellow << "Interaction list built in " << time.elapsed() << " ms\n" << cpp_tools::colors::reset; diff --git a/examples/test_laplace_kernels.cpp b/examples/test_laplace_kernels.cpp index 1d29391d67b307487f97b5d4d8883ccbe1c755c3..571c6115c10c130388c4e06753a82ca986573488 100644 --- a/examples/test_laplace_kernels.cpp +++ b/examples/test_laplace_kernels.cpp @@ -131,7 +131,7 @@ auto run(const std::string& input_file, const std::string& output_file, const in auto const separation_criterion = fmm_operator.near_field().separation_criterion(); time.tic(); - scalfmm::list::sequential::build_interaction_lists(gtree, gtree, separation_criterion, near_field.mutual()); + scalfmm::list::sequential::build_interaction_lists(gtree, gtree, fmm_operator); time.tac(); std::cout << cpp_tools::colors::yellow << "Interaction list built in " << time.elapsed() << " ms\n" << cpp_tools::colors::reset; diff --git a/examples/test_mpi_algo.cpp b/examples/test_mpi_algo.cpp index 9501fb2cfb564246f50a2fd58b18d19a4875c82c..97de772f15814b49edeb6d254e237b43f128f60e 100644 --- a/examples/test_mpi_algo.cpp +++ b/examples/test_mpi_algo.cpp @@ -174,7 +174,7 @@ auto run(cpp_tools::parallel_manager::parallel_manager& para, const std::string& std::cout << cpp_tools::colors::reset; } - scalfmm::list::sequential::build_interaction_lists(letGroupTree, letGroupTree, separation_criterion, mutual); + scalfmm::list::sequential::build_interaction_lists(letGroupTree, letGroupTree, fmm_operator); #ifdef SCALFMM_DEBUG_MPI { diff --git a/include/scalfmm/algorithms/full_direct.hpp b/include/scalfmm/algorithms/full_direct.hpp index ef33cee9b9ccc851a425a995451e409e148475a2..1a95acc6e0ce59ffbe307d64edcd848b271d03b9 100644 --- a/include/scalfmm/algorithms/full_direct.hpp +++ b/include/scalfmm/algorithms/full_direct.hpp @@ -5,13 +5,13 @@ #ifndef SCALFMM_ALGORITHMS_FULL_DIRECT_HPP #define SCALFMM_ALGORITHMS_FULL_DIRECT_HPP +#include "scalfmm/container/iterator.hpp" +#include "scalfmm/container/point.hpp" +#include "scalfmm/matrix_kernels/laplace.hpp" #include "scalfmm/meta/traits.hpp" #include "scalfmm/meta/utils.hpp" -#include <scalfmm/container/iterator.hpp> -#include <scalfmm/container/point.hpp> -#include <scalfmm/matrix_kernels/laplace.hpp> -#include <scalfmm/utils/io_helpers.hpp> -#include <scalfmm/utils/static_assert_as_exception.hpp> +#include "scalfmm/utils/io_helpers.hpp" +#include "scalfmm/utils/static_assert_as_exception.hpp" #include <array> #include <cstddef> @@ -20,9 +20,9 @@ #include <omp.h> using namespace scalfmm::io; + namespace scalfmm::algorithms { - /** * @brief compute the direct particle interactions for the built-in particle container. * @@ -43,6 +43,8 @@ namespace scalfmm::algorithms using matrix_kernel_type = MatrixKernelType; using particle_type = typename particle_container_type::value_type; using value_type = typename particle_type::position_type::value_type; + using position_type = typename particle_type::position_type; + static constexpr std::size_t dimension = position_type::dimension; using matrix_type = typename matrix_kernel_type::template matrix_type<value_type>; static_assert(matrix_kernel_type::km == particle_type::inputs_size, @@ -82,6 +84,21 @@ namespace scalfmm::algorithms compute(0, idx); compute(idx + 1, particles.size()); + + // Treat the case of the self-interaction only if we deal with a smooth kernel + if constexpr(meta::has_self_interaction_v<matrix_kernel_type, position_type>) + { + auto q = particles.at(idx).inputs(); + auto pt_x = particles.at(idx).position(); + val_mat = matrix_kernel.evaluate(pt_x); + for(std::size_t j = 0; j < outputs_size; ++j) + { + for(std::size_t i = 0; i < inputs_size; ++i) + { + val[j] += val_mat.at(j * inputs_size + i) * q[i]; + } + } + } } } @@ -101,6 +118,8 @@ namespace scalfmm::algorithms using matrix_kernel_type = MatrixKernelType; using value_type = typename particle_type::position_type::value_type; using matrix_type = typename matrix_kernel_type::template matrix_type<value_type>; + using position_type = typename particle_type::position_type; + static constexpr std::size_t dimension = position_type::dimension; static_assert(matrix_kernel_type::km == particle_type::inputs_size, "Different input size between Matrix kernel and container!"); @@ -139,6 +158,21 @@ namespace scalfmm::algorithms compute(0, idx); compute(idx + 1, particles.size()); + + // Treat the case of the self-interaction only if we deal with a smooth kernel + if constexpr(meta::has_self_interaction_v<matrix_kernel_type, position_type>) + { + auto q = particles.at(idx).inputs(); + auto pt_x = particles.at(idx).position(); + val_mat = matrix_kernel.evaluate(pt_x); + for(std::size_t j = 0; j < outputs_size; ++j) + { + for(std::size_t i = 0; i < inputs_size; ++i) + { + val[j] += val_mat.at(j * inputs_size + i) * q[i]; + } + } + } } } @@ -294,7 +328,6 @@ namespace scalfmm::algorithms compute(0, source_particles.size()); } } - } // namespace scalfmm::algorithms #endif // SCALFMM_ALGORITHMS_FULL_DIRECT_HPP diff --git a/include/scalfmm/algorithms/mpi/proc_task.hpp b/include/scalfmm/algorithms/mpi/proc_task.hpp index 798cc5ead69f54bea23c27c9e8f3c5c186b20ab5..b7c06d5757a20bdb2a29901971712c9538c36d09 100644 --- a/include/scalfmm/algorithms/mpi/proc_task.hpp +++ b/include/scalfmm/algorithms/mpi/proc_task.hpp @@ -100,7 +100,8 @@ namespace scalfmm::algorithms::mpi if(tree_target.is_interaction_m2l_lists_built() == false) { - list::omp::build_m2l_interaction_list(tree_source, tree_target, separation_criterion); + list::omp::build_m2l_interaction_list(tree_source, tree_target, separation_criterion, + separation_criterion); } if((op & operators_to_proceed::p2m) == operators_to_proceed::p2m) diff --git a/include/scalfmm/algorithms/omp/task_dep.hpp b/include/scalfmm/algorithms/omp/task_dep.hpp index 50605b70276e3362dc2ab61c412f016bbfbebd2c..a115ad74bae68f3ad759ff2c1b6bf0f159330e8f 100644 --- a/include/scalfmm/algorithms/omp/task_dep.hpp +++ b/include/scalfmm/algorithms/omp/task_dep.hpp @@ -92,7 +92,7 @@ namespace scalfmm::algorithms::omp // #pragma omp parallel default(none) \ shared(source_tree, target_tree, far_field, approximation, near_field, buffers, op, near_field_separation_criterion, \ - far_field_separation_criterion, mutual, same_tree) + far_field_separation_criterion, mutual, same_tree) { #pragma omp single nowait { @@ -107,7 +107,8 @@ namespace scalfmm::algorithms::omp } if(target_tree.is_interaction_m2l_lists_built() == false) { - list::omp::build_m2l_interaction_list(source_tree, target_tree, far_field_separation_criterion); + list::omp::build_m2l_interaction_list(source_tree, target_tree, far_field_separation_criterion, + far_field_separation_criterion); } if((op & operators_to_proceed::p2m) == operators_to_proceed::p2m) { @@ -141,7 +142,7 @@ namespace scalfmm::algorithms::omp pass::cell_to_leaf(target_tree, far_field); } } // end single - } // end parallel + } // end parallel delete_buffers(buffers); diff --git a/include/scalfmm/algorithms/sequential/sequential.hpp b/include/scalfmm/algorithms/sequential/sequential.hpp index b002c930499ed743fbe6da8a74d2db803e602853..301af5218aeeccd05f49daf7e9474a3cc64c9e82 100644 --- a/include/scalfmm/algorithms/sequential/sequential.hpp +++ b/include/scalfmm/algorithms/sequential/sequential.hpp @@ -83,8 +83,10 @@ namespace scalfmm::algorithms::sequential { timers["m2l-list"].tic(); } - scalfmm::list::sequential::build_m2l_interaction_list(source_tree, target_tree, - far_field_separation_criterion); + + scalfmm::list::sequential::build_m2l_interaction_list( + source_tree, target_tree, far_field_separation_criterion, far_field_separation_criterion); + if constexpr(options::has(s, options::timit)) { timers["m2l-list"].tac(); diff --git a/include/scalfmm/interpolation/barycentric/barycentric_interpolator.hpp b/include/scalfmm/interpolation/barycentric/barycentric_interpolator.hpp index d631bb50b06f821049b3053483c0e4504afe2585..a122c01d4652ce2e08b24d4edb3b5c58d6c05c09 100644 --- a/include/scalfmm/interpolation/barycentric/barycentric_interpolator.hpp +++ b/include/scalfmm/interpolation/barycentric/barycentric_interpolator.hpp @@ -359,6 +359,11 @@ namespace scalfmm::interpolation { return base_m2l_handler_type::memory_usage(); } + [[nodiscard]] inline auto name() const noexcept -> std::string const + { + settings s{}; + return "barycentric " + std::string(s.value()); + } private: /** diff --git a/include/scalfmm/interpolation/chebyshev/chebyshev_interpolator.hpp b/include/scalfmm/interpolation/chebyshev/chebyshev_interpolator.hpp index 5d45d38d60d2a526c7c92814d2dfbd9d3d250a48..6e210e9c0dd4574db53614e8b3e038876d020955 100644 --- a/include/scalfmm/interpolation/chebyshev/chebyshev_interpolator.hpp +++ b/include/scalfmm/interpolation/chebyshev/chebyshev_interpolator.hpp @@ -374,6 +374,11 @@ namespace scalfmm::interpolation { return base_m2l_handler_type::memory_usage(); } + [[nodiscard]] inline auto name() const noexcept -> std::string const + { + settings s{}; + return "chebyshev " + std::string(s.value()); + } private: /** diff --git a/include/scalfmm/interpolation/interpolator.hpp b/include/scalfmm/interpolation/interpolator.hpp index 0f4b1b1af22c825260f050fd31a032e6aa476cb1..8c905fb695af6ec81ceb30ab997962d9fb99eac0 100644 --- a/include/scalfmm/interpolation/interpolator.hpp +++ b/include/scalfmm/interpolation/interpolator.hpp @@ -306,6 +306,17 @@ namespace scalfmm::interpolation return m_grid_permutations; } + /** + * @brief ompute he memory in bytes used by the interpolator + * + * @return the memory used by the interpolator + */ + [[nodiscard]] inline auto memory_usage() const noexcept -> std::size_t const + { + return this->derived_cast().memory_usage(); + } + [[nodiscard]] inline auto name() const noexcept -> std::string const { return this->derived_cast().name(); } + private: /** * @brief This function pre-calculates all the child-parent interpolators required for M2M and L2L passes. @@ -521,16 +532,6 @@ namespace scalfmm::interpolation return *static_cast<derived_type*>(this); } - /** - * @brief ompute he memory in bytes used by the interpolator - * - * @return the memory used by the interpolator - */ - [[nodiscard]] inline auto memory_usage() const noexcept -> std::size_t const - { - return this->derived_cast().memory_usage(); - } - private: const size_type m_order{}; ///< number of terms of the expansion (1d) diff --git a/include/scalfmm/interpolation/uniform/uniform_interpolator.hpp b/include/scalfmm/interpolation/uniform/uniform_interpolator.hpp index 635d23eb2a2cc80237e0749943959a869e898b58..e4d325c64d3a11dc6b2dd7ba5c2777a0024ad8df 100644 --- a/include/scalfmm/interpolation/uniform/uniform_interpolator.hpp +++ b/include/scalfmm/interpolation/uniform/uniform_interpolator.hpp @@ -304,6 +304,11 @@ namespace scalfmm::interpolation { return base_m2l_handler_type::memory_usage(); } + [[nodiscard]] inline auto name() const noexcept -> std::string const + { + settings s{}; + return "uniform " + std::string(s.value()); + } }; /** @@ -687,6 +692,11 @@ namespace scalfmm::interpolation * * @return buffer_type */ + [[nodiscard]] inline auto name() const noexcept -> std::string const + { + settings s{}; + return "uniform " + std::string(s.value()); + } [[nodiscard]] inline auto buffer_initialization_impl() const -> buffer_type { // shape for the output fft is [2*order-1, ..., order] @@ -775,8 +785,8 @@ namespace scalfmm::interpolation inline auto apply_m2l_impl(Cell const& source_cell, [[maybe_unused]] Cell& target_cell, [[maybe_unused]] buffer_type& products, interaction_matrix_type const& k, ArrayScaleFactor scale_factor, [[maybe_unused]] std::size_t n, - [[maybe_unused]] std::size_t m, - [[maybe_unused]] size_type thread_id = 0) const -> void + [[maybe_unused]] std::size_t m, [[maybe_unused]] size_type thread_id = 0) const + -> void { // get the transformed multipoles (only available for uniform approximation) auto const& transformed_multipoles = source_cell.ctransformed_multipoles(); @@ -837,9 +847,9 @@ namespace scalfmm::interpolation * @return k_tensor_type */ template<typename TensorViewX, typename TensorViewY> - [[nodiscard]] inline auto - generate_matrix_k_impl(TensorViewX&& X, TensorViewY&& Y, std::size_t n, std::size_t m, - [[maybe_unused]] size_type thread_id = 0) const -> k_tensor_type + [[nodiscard]] inline auto generate_matrix_k_impl(TensorViewX&& X, TensorViewY&& Y, std::size_t n, std::size_t m, + [[maybe_unused]] size_type thread_id = 0) const + -> k_tensor_type { build<value_type, dimension, dimension> build_c{}; // generate the circulant tensor diff --git a/include/scalfmm/lists/omp.hpp b/include/scalfmm/lists/omp.hpp index d89fe2fb494789108d2ee8ea873cc7b6f2e938ed..1c4988f0180fe2553e386332c9f71d4fc066595e 100644 --- a/include/scalfmm/lists/omp.hpp +++ b/include/scalfmm/lists/omp.hpp @@ -93,7 +93,7 @@ namespace scalfmm::list::omp */ template<typename SourceTreeType, typename TargetTreeType> inline auto build_m2l_interaction_list(SourceTreeType& source_tree, TargetTreeType& target_tree, - const int& neighbour_separation) -> void + const int& neighbour_separation, const int leaf_neighbour_separation) -> void { // Iterate on the group of leaves // here we get the first level of cells (leaf_level up to the top_level) @@ -105,6 +105,8 @@ namespace scalfmm::list::omp // auto cell_source_level_it = std::get<1>(source_tree.begin()) + leaf_level; for(int level = leaf_level; level >= top_level; --level) { + auto separation_criterion = level == leaf_level ? leaf_neighbour_separation : neighbour_separation; + // target auto group_of_cell_begin = target_tree.begin_mine_cells(level); auto group_of_cell_end = target_tree.end_mine_cells(level); @@ -117,23 +119,23 @@ namespace scalfmm::list::omp component::for_each(group_of_cell_begin, group_of_cell_end, [begin_of_source_cell_groups, end_of_source_cell_groups, level, &period, - &neighbour_separation](auto& group_target) + &separation_criterion](auto& group_target) { static constexpr int prio{scalfmm::algorithms::omp::priorities::max}; #pragma omp task untied default(none) \ firstprivate(group_target, begin_of_source_cell_groups, end_of_source_cell_groups, level) \ - shared(period, neighbour_separation) priority(prio) + shared(period, separation_criterion) priority(prio) { // loop on target cell group component::for_each( std::begin(*group_target), std::end(*group_target), [&group_target, begin_of_source_cell_groups, end_of_source_cell_groups, level, - &period, &neighbour_separation](auto& cell) + &period, &separation_criterion](auto& cell) { list::build_m2l_interaction_list_for_group( *group_target, cell, begin_of_source_cell_groups, - end_of_source_cell_groups, level, period, neighbour_separation); + end_of_source_cell_groups, level, period, separation_criterion); }); } }); @@ -171,5 +173,53 @@ namespace scalfmm::list::omp } } + /** + * @brief Construct the P2P and the M2L interaction lists for the target tree + * + * @tparam SourceTreeType + * @tparam TargetTreeType + * @tparam FmmOperatorType + * @param[in] source_tree the tree containing the sources + * @param[inout] target_tree the tree containing the targets. + * @param[in] fmm_operator The FMM operator used to get separtion criterion and mutual compultation + * @param[in] verbose boolean to specify if the direct pass use a symmetric algorithm (mutual interactions) + */ + template<typename SourceTreeType, typename TargetTreeType, + typename FmmOperatorType> //NearFieldType, typename NearFieldType> + inline auto build_interaction_lists(SourceTreeType& source_tree, TargetTreeType& target_tree, + FmmOperatorType const& fmm_operator, bool verbose = false) -> void + { + int leaf_neighbour_separation{fmm_operator.near_field().separation_criterion()}; + int neighbour_separation{fmm_operator.near_field().separation_criterion()}; + // if constexpr(scalfmm::meta::is_smooth_v<typename FmmOperatorType::near_field_type::matrix_kernel_type>) + // { + // leaf_neighbour_separation = fmm_operator.near_field().matrix_kernel().leaf_separation_criterion; + // } + bool const& mutual = fmm_operator.near_field().mutual(); + + if(verbose) + { + std::cout << "list::neighbour_separation " << neighbour_separation + << std::endl + // << "list::leaf_neighbour_separation " << leaf_neighbour_separation << std::endl + << "list::mutual " << std::boolalpha << mutual << std::endl + << std::flush; + } + + // std::cout << " build_p2p_interaction_list start \n" << std::flush; + try + { + build_p2p_interaction_list(source_tree, target_tree, leaf_neighbour_separation, mutual); + // std::cout << " build_m2l_interaction_list start \n" << std::flush; + build_m2l_interaction_list(source_tree, target_tree, neighbour_separation, leaf_neighbour_separation); + + // std::cout << " build_interaction_lists end \n" << std::flush; + } + catch(const std::out_of_range& e) + { + std::cerr << e.what() << std::endl; + } + } + } // namespace scalfmm::list::omp #endif // SCALFMM_LISTS_OMP_HPP diff --git a/include/scalfmm/lists/sequential.hpp b/include/scalfmm/lists/sequential.hpp index 6f54967cd71b73bba2fd6b9bb0c36200ad9367a5..484d398c032dd049e1165d826ccd578c1e7b258d 100644 --- a/include/scalfmm/lists/sequential.hpp +++ b/include/scalfmm/lists/sequential.hpp @@ -6,8 +6,8 @@ #define SCALFMM_LISTS_SEQUENTIAL_HPP #include "scalfmm/lists/utils.hpp" +#include "scalfmm/operators/fmm_operators.hpp" #include "scalfmm/utils/io_helpers.hpp" - namespace scalfmm::list::sequential { /** @@ -88,7 +88,7 @@ namespace scalfmm::list::sequential */ template<typename SourceTreeType, typename TargetTreeType> inline auto build_m2l_interaction_list(SourceTreeType& source_tree, TargetTreeType& target_tree, - const int& neighbour_separation) -> void + const int& neighbour_separation, const int leaf_neighbour_separation) -> void { // Iterate on the group of leaves // here we get the first level of cells (leaf_level up to the top_level) @@ -100,6 +100,7 @@ namespace scalfmm::list::sequential for(int level = leaf_level; level >= top_level; --level) { // target + auto separation_criterion = level == leaf_level ? leaf_neighbour_separation : neighbour_separation; auto group_of_cell_begin = target_tree.begin_mine_cells(level); auto group_of_cell_end = target_tree.end_mine_cells(level); // source @@ -108,17 +109,17 @@ namespace scalfmm::list::sequential // loop on target group component::for_each(group_of_cell_begin, group_of_cell_end, [begin_of_source_cell_groups, end_of_source_cell_groups, level, &period, - &neighbour_separation](auto& group_target) + &separation_criterion](auto& group_target) { // loop on target cell group component::for_each( std::begin(*group_target), std::end(*group_target), [&group_target, begin_of_source_cell_groups, end_of_source_cell_groups, level, - &period, &neighbour_separation](auto& cell_target) + &period, &separation_criterion](auto& cell_target) { list::build_m2l_interaction_list_for_group( *group_target, cell_target, begin_of_source_cell_groups, - end_of_source_cell_groups, level, period, neighbour_separation); + end_of_source_cell_groups, level, period, separation_criterion); }); }); } @@ -137,10 +138,66 @@ namespace scalfmm::list::sequential */ template<typename SourceTreeType, typename TargetTreeType> inline auto build_interaction_lists(SourceTreeType& source_tree, TargetTreeType& target_tree, - const int& neighbour_separation, const bool mutual) -> void + const int& neighbour_separation, const bool mutual, + int leaf_neighbour_separation = -1) -> void { + if(leaf_neighbour_separation == -1) + { + leaf_neighbour_separation = neighbour_separation; + } + std::cout << "list::neighbour_separation " << neighbour_separation << std::endl + << "list::leaf_neighbour_separation " << leaf_neighbour_separation << std::endl + << "list::mutual " << std::boolalpha << mutual << std::endl; build_p2p_interaction_list(source_tree, target_tree, neighbour_separation, mutual); - build_m2l_interaction_list(source_tree, target_tree, neighbour_separation); + + build_m2l_interaction_list(source_tree, target_tree, neighbour_separation, leaf_neighbour_separation); + } + /** + * @brief Construct the P2P and the M2L interaction lists for the target tree + * + * @tparam SourceTreeType + * @tparam TargetTreeType + * @tparam FmmOperatorType + * @param[in] source_tree the tree containing the sources + * @param[inout] target_tree the tree containing the targets. + * @param[in] fmm_operator The FMM operator used to get separtion criterion and mutual compultation + * @param[in] verbose boolean to specify if the direct pass use a symmetric algorithm (mutual interactions) + */ + template<typename SourceTreeType, typename TargetTreeType, + typename FmmOperatorType> //NearFieldType, typename NearFieldType> + inline auto build_interaction_lists(SourceTreeType& source_tree, TargetTreeType& target_tree, + FmmOperatorType const& fmm_operator, bool verbose = false) -> void + { + int leaf_neighbour_separation{fmm_operator.near_field().separation_criterion()}; + int neighbour_separation{fmm_operator.near_field().separation_criterion()}; + // if constexpr(scalfmm::meta::is_smooth_v<typename FmmOperatorType::near_field_type::matrix_kernel_type>) + // { + // leaf_neighbour_separation = fmm_operator.near_field().matrix_kernel().leaf_separation_criterion; + // } + bool const& mutual = fmm_operator.near_field().mutual(); + + if(verbose) + { + std::cout << "list::neighbour_separation " << neighbour_separation + << std::endl + // << "list::leaf_neighbour_separation " << leaf_neighbour_separation << std::endl + << "list::mutual " << std::boolalpha << mutual << std::endl + << std::flush; + } + + // std::cout << " build_p2p_interaction_list start \n" << std::flush; + try + { + build_p2p_interaction_list(source_tree, target_tree, leaf_neighbour_separation, mutual); + // std::cout << " build_m2l_interaction_list start \n" << std::flush; + build_m2l_interaction_list(source_tree, target_tree, neighbour_separation, leaf_neighbour_separation); + + // std::cout << " build_interaction_lists end \n" << std::flush; + } + catch(const std::out_of_range& e) + { + std::cerr << e.what() << std::endl; + } } } // namespace scalfmm::list::sequential diff --git a/include/scalfmm/matrix_kernels/debug.hpp b/include/scalfmm/matrix_kernels/debug.hpp index 96854e0e78446b4c40116a0edd43239b9a4f5170..4569dc13c6094657c27a274104a7bb2da74a090f 100644 --- a/include/scalfmm/matrix_kernels/debug.hpp +++ b/include/scalfmm/matrix_kernels/debug.hpp @@ -406,4 +406,100 @@ namespace scalfmm::matrix_kernels::debug static constexpr int separation_criterion{1}; // Criterion used to separate near and far field. }; + + /** + * @brief The one_over_r struct corresponds to the \f$1/r\f$ kernel for x != y and 1 if x == y. + * + * This matrix kernel enables the computation of self-interactions. + * + * The kernel is defined as \f$k(x,y): R^{km} -> R^{kn}\f$ with \f$ kn = km = 1\f$ + * \f$k(x,y) = | x - y |^{-1}\f$ if $x != y$ and 1 otherwise. + */ + struct one_over_r_modified : public one_over_r_non_homogenous + { + /** + * @brief Returns the name of the kernel. + * + * @return A string representing the kernel's name. + */ + const std::string name() const { return std::string("one_over_r_modified"); } + + /** + * @brief Overload of the `()` operator to evaluate the kernel at point \f$x\f$ (self-interaction). + * + * @tparam ValueType Type of the elements in the point. + * @tparam Dimension The dimension of the point. + * + * @param x Multidimensional point (e.g., a 2D point). + * + * @return The matrix \f$K(x, x)\f$ in a vector (row-major storage). + */ + template<typename ValueType, std::size_t Dimension> + [[nodiscard]] inline auto operator()(container::point<ValueType, Dimension> const& x) const noexcept + -> matrix_type<std::decay_t<ValueType>> + { + return evaluate(x); + } + + /** + * @brief Evaluates the kernel at points \f$x\f$ (self-interaction). + * + * @tparam ValueType Type of the elements in the point. + * @tparam Dimension The dimension of the point. + * + * @param x Multidimensional point (e.g., a 2D point). + * + * @return The matrix \f$K(x, x)\f$ in a vector (row-major storage). + */ + template<typename ValueType, std::size_t Dimension> + [[nodiscard]] inline auto evaluate(container::point<ValueType, Dimension> const& x) const noexcept + -> matrix_type<std::decay_t<ValueType>> + { + using decayed_type = std::decay_t<ValueType>; + matrix_type<decayed_type> tmp; + std::fill(std::begin(tmp), std::end(tmp), decayed_type(1.)); + return tmp; + } + + /** + * @brief Overload of the `()` operator to evaluate the kernel at points \f$x\f$ and \f$y\f$. + * + * @tparam ValueType1 Type of the elements in the first point. + * @tparam ValueType2 Type of the elements in the second point. + * + * @param x Multidimensional point (e.g., a 2D point). + * @param y Multidimensional point (e.g., a 2D point). + * + * @return The matrix \f$K(x, y)\f$ in a vector (row-major storage). + */ + template<typename ValueType1, typename ValueType2, std::size_t Dimension> + [[nodiscard]] inline auto operator()(container::point<ValueType1, Dimension> const& x, + container::point<ValueType2, Dimension> const& y) const noexcept + -> std::enable_if_t<std::is_same_v<std::decay_t<ValueType1>, std::decay_t<ValueType2>>, + matrix_type<std::decay_t<ValueType1>>> + { + return evaluate(x, y); + } + + /** + * @brief Evaluates the kernel at points \f$x\f$ and \f$y\f$. + * + * @tparam ValueType1 Type of the elements in the first point. + * @tparam ValueType2 Type of the elements in the second point. + * + * @param x Multidimensional point (e.g., a 2D point). + * @param y Multidimensional point (e.g., a 2D point). + * + * @return The matrix \f$K(x, y)\f$ in a vector (row-major storage). + */ + template<typename ValueType1, typename ValueType2, std::size_t Dimension> + [[nodiscard]] inline auto evaluate(container::point<ValueType1, Dimension> const& x, + container::point<ValueType2, Dimension> const& y) const noexcept + -> std::enable_if_t<std::is_same_v<std::decay_t<ValueType1>, std::decay_t<ValueType2>>, + matrix_type<std::decay_t<ValueType1>>> + { + return one_over_r_non_homogenous::evaluate(x, y); + } + }; + } // namespace scalfmm::matrix_kernels::debug diff --git a/include/scalfmm/matrix_kernels/gaussian.hpp b/include/scalfmm/matrix_kernels/gaussian.hpp index 9822548428d92b9a2eb25fe3198ca8f1f60c0749..d2dbb60c012fdc7b303b5396f7592c74f344fdb6 100644 --- a/include/scalfmm/matrix_kernels/gaussian.hpp +++ b/include/scalfmm/matrix_kernels/gaussian.hpp @@ -13,12 +13,14 @@ namespace scalfmm::matrix_kernels * @brief This structure corresponds to the Gaussian kernel. * * The kernel is defined as \f$k(x,y): R^{km} -> R^{kn}\f$ with \f$ kn = km = 1\f$ - * \f$k(x,y) = exp(-| x - y |/(\sigma^2))\f$ + * \f$k(x,y) = exp(-| x - y |^2 / (\sigma))\f$ * * - The kernel is not homogeneous. - * - The kernel is not symmetric. + * - The kernel is symmetric (depends only on the norm). + * - The kernel is smooth (and allows self-interactions to be evaluated). * - * @GlobalValueType The value type of the coefficient \f$\sigma\f$. + * default \f$ \sigma = 2\f$ + * @GlobalValueType The value type of the coefficient \f$\sigma\f$. */ template<typename GlobalValueType> struct gaussian @@ -28,8 +30,11 @@ namespace scalfmm::matrix_kernels // Mandatory constants static constexpr auto homogeneity_tag{homogeneity::non_homogenous}; // Specify the homogeneity of the kernel. static constexpr auto symmetry_tag{symmetry::non_symmetric}; // Specify the symmetry of the kernel. - static constexpr std::size_t km{1}; // The number of inputs of the kernel. - static constexpr std::size_t kn{1}; // The number of outputs of the kernel. + + static constexpr std::size_t km{1}; // The number of inputs of the kernel. + static constexpr std::size_t kn{1}; // The number of outputs of the kernel. + + static constexpr bool is_smooth{true}; // Specify that the kernel is smooth. // Mandatory type template<typename ValueType> @@ -37,7 +42,8 @@ namespace scalfmm::matrix_kernels template<typename ValueType> using vector_type = std::array<ValueType, kn>; // Vector type that is used in the kernel. - value_type m_coeff{value_type(1.)}; // parameter/coefficient of the kernel. + value_type m_coeff{value_type(1.)}; // parameter/coefficient of the kernel. + value_type m_epsilon{value_type(0.)}; // epsilon value for the evaluation at point x (self-interaction). /** * @brief Set the value of the coefficient of the kernel. @@ -46,12 +52,23 @@ namespace scalfmm::matrix_kernels */ void set_coeff(value_type in_coeff) { m_coeff = in_coeff; } + /** + * @brief Set the value of epsilon (to compute self-interaction). + * + * @param in_epsilon value of epsilon to be set. + */ + void set_epsilon(value_type in_epsilon) { m_epsilon = in_epsilon; } + /** * @brief Returns the name of the kernel. * * @return A string representing the kernel's name. */ - const std::string name() const { return std::string("gaussian ") + " coeff = " + std::to_string(m_coeff); } + const std::string name() const + { + return std::string("gaussian") + ", coeff = " + std::to_string(m_coeff) + + ", m_epsilon = " + std::to_string(m_epsilon); + } /** * @brief Returns the mutual coefficient of size \f$kn\f$. @@ -66,7 +83,44 @@ namespace scalfmm::matrix_kernels template<typename ValueType> [[nodiscard]] inline constexpr auto mutual_coefficient() const { - return vector_type<ValueType>{ValueType(1.)}; + return vector_type<ValueType>{1.0}; + } + + /** + * @brief Overload of the `()` operator to evaluate the kernel at point \f$x\f$ (self-interaction). + * + * @tparam ValueType Type of the elements in the point. + * @tparam Dimension The dimension of the point. + * + * @param x Multidimensional point (e.g., a 2D point). + * + * @return The matrix \f$K(x, x)\f$ in a vector (row-major storage). + */ + template<typename ValueType, std::size_t Dimension> + [[nodiscard]] inline auto operator()(container::point<ValueType, Dimension> const& x) const noexcept + -> matrix_type<std::decay_t<ValueType>> + { + return evaluate(x); + } + + /** + * @brief Evaluates the kernel at points \f$x\f$ (self-interaction). + * + * @tparam ValueType Type of the elements in the point. + * @tparam Dimension The dimension of the point. + * + * @param x Multidimensional point (e.g., a 2D point). + * + * @return The matrix \f$K(x, x)\f$ in a vector (row-major storage). + */ + template<typename ValueType, std::size_t Dimension> + [[nodiscard]] inline auto evaluate(container::point<ValueType, Dimension> const& x) const noexcept + -> matrix_type<std::decay_t<ValueType>> + { + using decayed_type = std::decay_t<ValueType>; + matrix_type<decayed_type> tmp; + std::fill(std::begin(tmp), std::end(tmp), decayed_type(1. + m_epsilon)); + return tmp; } /** @@ -133,11 +187,14 @@ namespace scalfmm::matrix_kernels matrix_type<std::decay_t<ValueType1>>> { using decayed_type = std::decay_t<ValueType1>; - auto l2 = decayed_type(-1.0) / (m_coeff * m_coeff); + auto l2 = decayed_type(-1.0) / (m_coeff); decayed_type r2 = (((xs.at(Is) - ys.at(Is)) * (xs.at(Is) - ys.at(Is))) + ...); return matrix_type<decayed_type>{xsimd::exp(r2 * l2)}; } - static constexpr int separation_criterion{1}; // Criterion used to separate near and far field. + static constexpr int separation_criterion{ + 1}; // Criterion used to separate near and far field at all level exepct leaf + static constexpr int leaf_separation_criterion{ + 0}; // Criterion used to separate near and far field at leaf level }; } // namespace scalfmm::matrix_kernels diff --git a/include/scalfmm/matrix_kernels/mk_common.hpp b/include/scalfmm/matrix_kernels/mk_common.hpp index 0682211afd317b06b679a4d734ec18e7b8055458..3d14c1f54d1456f10e71f4790aeef8343aa29ae8 100644 --- a/include/scalfmm/matrix_kernels/mk_common.hpp +++ b/include/scalfmm/matrix_kernels/mk_common.hpp @@ -35,4 +35,11 @@ namespace scalfmm::matrix_kernels condionally //< conditionally convergent series }; + /** + * @brief specifies if the kernel can be evaluated at x == y (compile-time) + */ + struct smooth_tag + { + }; + } // namespace scalfmm::matrix_kernels diff --git a/include/scalfmm/meta/traits.hpp b/include/scalfmm/meta/traits.hpp index df1ce1720decf227a9699f6a69ce4e345cc77229..7efc29e9af02aaba3c243c0557dcb77bafe44c29 100644 --- a/include/scalfmm/meta/traits.hpp +++ b/include/scalfmm/meta/traits.hpp @@ -6,7 +6,7 @@ // -------------------------------- // See LICENCE file at project root // File : scalfmm/meta/traits.hpp -// -------------------------------- +// ------------------------------- #ifndef SCALFMM_META_TRAITS_HPP #define SCALFMM_META_TRAITS_HPP @@ -920,6 +920,64 @@ namespace scalfmm::meta template<typename T> inline static constexpr bool is_particle_v = is_particle<T>::value; + /** + * @brief + * + * @tparam T + */ + template<typename MatrixKernelType, typename = void> + struct is_smooth : std::false_type + { + }; + + /** + * @brief + * + * @tparam T + */ + template<typename MatrixKernelType> + struct is_smooth<MatrixKernelType, std::void_t<decltype(MatrixKernelType::is_smooth)>> : std::true_type + { + }; + + /** + * @brief + * + * @tparam T + */ + template<typename MatrixKernelType> + inline static constexpr bool is_smooth_v = is_smooth<MatrixKernelType>::value; + + /** + * @brief + * + * @tparam T + */ + template<typename MatrixKernelType, typename PointType, typename = void> + struct has_self_interaction : std::false_type + { + }; + + /** + * @brief + * + * @tparam T + */ + template<typename MatrixKernelType, typename PointType> + struct has_self_interaction< + MatrixKernelType, PointType, + std::void_t<decltype(std::declval<MatrixKernelType>().evaluate(std::decay_t<PointType>{}))>> : std::true_type + { + }; + + /** + * @brief + * + * @tparam T + */ + template<typename MatrixKernelType, typename PointType> + inline static constexpr bool has_self_interaction_v = has_self_interaction<MatrixKernelType, PointType>::value; + /** * @brief * diff --git a/include/scalfmm/meta/utils.hpp b/include/scalfmm/meta/utils.hpp index 194b6a0ae87f217822bae2751bd2dc19eabaf438..1c11fea7e359002f601a4e3c45e60a76583efd0a 100644 --- a/include/scalfmm/meta/utils.hpp +++ b/include/scalfmm/meta/utils.hpp @@ -152,7 +152,7 @@ namespace scalfmm::meta /** * @brief Aliases to simplify use - * + * * How to use if * @code * // Define a tuple of values @@ -209,7 +209,7 @@ namespace scalfmm::meta }; /** - * @brief + * @brief return the size of the tuple * * @tparam T */ @@ -335,6 +335,24 @@ namespace scalfmm::meta { }; + /** + * @brief contains the size of a patricle type without the vatiables + * + * use particle_size_without_variables<ParticleType>::size ; + * @tparam PositionType + * @tparam PositionDim + * @tparam InputsType + * @tparam NInputs + * @tparam OutputsType + * @tparam MOutputs + * @tparam Variables + */ + template<typename ParticleType> + struct particle_size_without_variables + { + static constexpr size_t size = ParticleType::dimension + ParticleType::inputs_size + ParticleType::outputs_size; + }; + /** * @brief * diff --git a/include/scalfmm/operators/fmm_operators.hpp b/include/scalfmm/operators/fmm_operators.hpp index 0f626e6182a38eaba20da989b34161f3922dd6a6..e22ae01700cfd81ccd94451c7d4723a4d2f882c5 100644 --- a/include/scalfmm/operators/fmm_operators.hpp +++ b/include/scalfmm/operators/fmm_operators.hpp @@ -120,7 +120,7 @@ namespace scalfmm::operators auto matrix_kernel() const -> matrix_kernel_type const& { return m_matrix_kernel; } /** - * @brief + * @brief Get the matrix kernel * * @return matrix_kernel_type& */ @@ -157,23 +157,12 @@ namespace scalfmm::operators auto mutual() -> bool& { return m_mutual; } private: - /** - * @brief The matrix kernel used in the near field. - * - */ - matrix_kernel_type m_matrix_kernel; + matrix_kernel_type m_matrix_kernel; ///< he matrix kernel used in the near field. - /** - * @brief The separation criterion used in the near field. - * - */ - int m_separation_criterion{matrix_kernel_type::separation_criterion}; + int m_separation_criterion{ + matrix_kernel_type::separation_criterion}; ///< The separation criterion used in the near field. - /** - * @brief To specify if we use symmetric algorithm for the p2p (mutual interactions). - * - */ - bool m_mutual{true}; + bool m_mutual{true}; ///< To specify if we use symmetric algorithm for the p2p (mutual interactions). }; /** @@ -200,8 +189,9 @@ namespace scalfmm::operators public: using approximation_type = ApproximationType; static constexpr bool compute_gradient = ComputeGradient; - // precise the homogeneous type of the kernel + ///< precise the homogeneous type of the kernel static constexpr auto homogeneity_tag = approximation_type::homogeneity_tag; + static constexpr auto is_smooth = meta::is_smooth_v<typename approximation_type::matrix_kernel_type>; /** * @brief @@ -269,17 +259,9 @@ namespace scalfmm::operators private: // Here, be careful with this reference with the lifetime of matrix_kernel. - /** - * @brief the approximation method used in the near field. - * - */ - approximation_type const& m_approximation; + approximation_type const& m_approximation; ///< the approximation method used in the near field. - /** - * @brief - * - */ - int m_separation_criterion{approximation_type::separation_criterion}; + int m_separation_criterion{approximation_type::separation_criterion}; ///< the separation criterion. }; /** @@ -376,31 +358,37 @@ namespace scalfmm::operators } /** - * @brief + * @brief get the near field of the FMM operator * * @return near_field_type const& */ auto near_field() const -> near_field_type const& { return m_near_field; } /** - * @brief + * @brief get the far field of the FMM operator * * @return far_field_type const& */ auto far_field() const -> far_field_type const& { return m_far_field; } + /// @brief display the settings on a stream + /// @param os the stram to display the settings + inline auto settings(std::ostream& os = std::cout) const noexcept -> void + { + std::cout << cpp_tools::colors::blue << "Fmm operators with kernels: " << std::endl + << " near field\n" + << " kernel: " << m_near_field.matrix_kernel().name() << "\n" + << " mutual " << std::boolalpha << m_near_field.mutual() << std::endl + << " far field\n" + << " approximation: " << m_far_field.approximation().name() << std::endl + << std::flush << " kernel: " << m_far_field.approximation().matrix_kernel().name() + << std::endl + << std::flush; + } private: - /** - * @brief the near field used in the fmm operator. - * - */ - near_field_type const& m_near_field; + near_field_type const& m_near_field; ///< the near field used in the fmm operator. - /** - * @brief the far field used in the fmm operator. - * - */ - far_field_type const& m_far_field; + far_field_type const& m_far_field; ///< the far field used in the fmm operator. }; } // namespace scalfmm::operators diff --git a/include/scalfmm/operators/p2p.hpp b/include/scalfmm/operators/p2p.hpp index 21ed4de9e710bfa1628303921312ffd0cbfd7d41..9b52df88184df39ac1a18742dff7477395fde0ac 100644 --- a/include/scalfmm/operators/p2p.hpp +++ b/include/scalfmm/operators/p2p.hpp @@ -7,6 +7,7 @@ #include "scalfmm/container/particle.hpp" #include "scalfmm/container/point.hpp" +#include "scalfmm/meta/traits.hpp" #include "scalfmm/meta/type_pack.hpp" #include "scalfmm/meta/utils.hpp" #include "scalfmm/simd/memory.hpp" @@ -41,9 +42,9 @@ namespace scalfmm::operators */ template<typename MatrixKernelType, typename LeafType, typename ContainerOfLeafIteratorType, typename ArrayType, typename ValueType> - inline void p2p_full_mutual(MatrixKernelType const& matrix_kernel, LeafType& target_leaf, + inline auto p2p_full_mutual(MatrixKernelType const& matrix_kernel, LeafType& target_leaf, ContainerOfLeafIteratorType const& neighbors, const int& size, - [[maybe_unused]] ArrayType const& pbc, ValueType const& box_width) + [[maybe_unused]] ArrayType const& pbc, ValueType const& box_width) -> void { using leaf_type = LeafType; using matrix_kernel_type = MatrixKernelType; @@ -221,6 +222,223 @@ namespace scalfmm::operators std::for_each(std::begin(neighbors), std::begin(neighbors) + size, apply_full_mutual); } + /** + * @brief Computes the field due to the particles the source container on the target particles + * + * compute the field due to the particles the source container on the target particles by + * + * \f$ out(pt) = \sum_{ps\in source} { matrix\_kernel(pt, ps) input(ps) } \f$ + * + * We don't check if | pt - ps| =0 + * + * @tparam MatrixKernelType + * @tparam TargetLeafType + * @tparam SourceLeafType + * @tparam ArrayType + * @param matrix_kernel the kernel used to compute the interaction + * @param[inout] target_leaf the container of particles where the field is evaluated + * @param[in] source_leaf the container of particles which generate the field + * @param[in] shift the shift to apply on the particle in periodic system. + */ + template<typename MatrixKernelType, typename TargetLeafType, typename SourceLeafType, typename ArrayType> + inline auto p2p_outer(MatrixKernelType const& matrix_kernel, TargetLeafType& target_leaf, + SourceLeafType const& source_leaf, [[maybe_unused]] ArrayType const& shift) -> void + { +#ifndef NDEBUG + if constexpr(std::is_same_v<TargetLeafType, SourceLeafType>) + { + if(&target_leaf.symbolics() == &source_leaf.symbolics()) + { + throw std::runtime_error("In p2p_outer the two containers must be different."); + } + } +#endif + using target_leaf_type = TargetLeafType; + using source_leaf_type = SourceLeafType; + using matrix_kernel_type = MatrixKernelType; + using particle_type = typename target_leaf_type::particle_type; + using position_type = typename particle_type::position_type; + using outputs_type = typename particle_type::outputs_type; + using inputs_value_type = typename source_leaf_type::particle_type::inputs_value_type; + + using value_type = inputs_value_type; + using simd_type = xsimd::simd_type<value_type>; + using simd_position_type = container::point<simd_type, position_type::dimension>; + using simd_outputs_type = std::array<simd_type, particle_type::outputs_size>; + static constexpr std::size_t kn = matrix_kernel_type::kn; + static constexpr std::size_t km = matrix_kernel_type::km; + + static constexpr std::size_t inc = simd_type::size; + const std::size_t target_size{target_leaf.size()}; + const std::size_t neighbor_size{source_leaf.size()}; + const std::size_t vec_size = neighbor_size - neighbor_size % inc; + + auto target_position_iterator = container::position_begin(target_leaf.cparticles()); + auto target_outputs_iterator = container::outputs_begin(target_leaf.particles()); + +#ifdef scalfmm_BUILD_PBC + simd_position_type simd_shift(shift); +#endif + + for(std::size_t target_idx = 0; target_idx < target_size; ++target_idx) + { + auto neighbor_position_iterator = container::position_begin(source_leaf.cparticles()); + auto neighbor_inputs_iterator = container::inputs_begin(source_leaf.cparticles()); + + // Just need the position and the output for the target + auto target_position = simd::load_position<simd_position_type>(target_position_iterator, simd::splated{}); + + simd_outputs_type target_outputs{}; + meta::for_each(target_outputs, [](auto& v) { v = value_type(0.); }); + + { + // loop on the simd part of the neighbor particles + for(std::size_t neighbor_idx = 0; neighbor_idx < vec_size; neighbor_idx += inc) + { + // load simd position + auto neighbor_position = + simd::load_position<simd_position_type>(neighbor_position_iterator, simd::unaligned{}); + // load simd input values + auto neighbor_inputs_values = + meta::to_array(simd::load_tuple<simd_type>(neighbor_inputs_iterator, simd::unaligned{})); + +#ifdef scalfmm_BUILD_PBC + neighbor_position += simd_shift; +#endif + // get the matrix kernel evaluation from the particles positions + auto kxy_ = matrix_kernel.evaluate(target_position, neighbor_position); + + // loop on the n:m in/out matrix_kernel + for(std::size_t m = 0; m < km; ++m) + { + for(std::size_t n = 0; n < kn; ++n) + { + auto kxy_nm = kxy_.at(n * km + m); + // target_out(n) = K(n,m)*neighbor_in(m) + target_outputs.at(n) += kxy_nm * neighbor_inputs_values.at(m); + } + } + // increment iterators to the next simd sized values + neighbor_position_iterator += inc; + neighbor_inputs_iterator += inc; + } + + // reduce the target simd outputs vector + auto reduced_target_outputs = meta::apply(target_outputs, [](auto f) { return xsimd::reduce_add(f); }); + // update scalar target output + meta::tuple_sum_update(*target_outputs_iterator, reduced_target_outputs); + } + // same steps as before but for the scalar end of the neighbors particles vector + { + auto target_position = simd::load_position<position_type>(target_position_iterator); + outputs_type target_outputs{}; + meta::for_each(target_outputs, [](auto& p) { p = value_type(0.); }); + + for(std::size_t scal_idx = vec_size; scal_idx < neighbor_size; ++scal_idx) + { + auto neighbor_position = simd::load_position<position_type>(neighbor_position_iterator); + auto neighbor_inputs_values = + meta::to_array(simd::load_tuple<value_type>(neighbor_inputs_iterator)); + +#ifdef scalfmm_BUILD_PBC + neighbor_position += shift; +#endif + auto kxy_ = matrix_kernel.evaluate(target_position, neighbor_position); + for(std::size_t m = 0; m < km; ++m) + { + for(std::size_t n = 0; n < kn; ++n) + { + auto kxy_nm = kxy_.at(n * km + m); + auto tmp_ = kxy_nm * neighbor_inputs_values.at(m); + target_outputs.at(n) += tmp_; + } + } + + ++neighbor_position_iterator; + ++neighbor_inputs_iterator; + } + + meta::tuple_sum_update(*target_outputs_iterator, target_outputs); + } + ++target_position_iterator; + ++target_outputs_iterator; + } + } + + /** + * @brief p2p_outer compute the particle-to-particle interactions between the target leaf + * and its neighbors. + * + * For each particle, x, in target_leaf we compute for each leaf, neighbor_leaf, + * in neighbors the interactions between particle y in + * $\f[ x.outputs = matrix_kernel(x,y) * y.inputs \f]$ + * + * @warning{The neighbors are not modified.} + * @todo{To be implemented} + * + * @tparam MatrixKernelType + * @tparam LeafType + * @tparam ContainerOfLeafIteratorType + * @tparam ArrayType + * @tparam ValueType + * @param matrix_kernel the kernel used to compute the interactions. + * @param target_leaf the current leaf where we compute the p2p. + * @param neighbors all neighboring leaves. + * @param number_of_neighbors number of neighbors. + * @param pbc + * @param box_width + */ + template<typename MatrixKernelType, typename LeafType, typename ContainerOfLeafIteratorType, typename ArrayType, + typename ValueType> + inline auto p2p_outer(MatrixKernelType const& matrix_kernel, LeafType& target_leaf, + ContainerOfLeafIteratorType const& neighbors, const int number_of_neighbors, + [[maybe_unused]] ArrayType const& pbc, ValueType const& box_width) -> void + { + using leaf_type = LeafType; + using position_type = typename leaf_type::position_type; + // // value type + using value_type = typename leaf_type::value_type; + // + // compute the center of the target_leaf + auto const& target_center = target_leaf.center(); + + auto apply_outer = [&target_leaf, &matrix_kernel, &target_center, &pbc, &box_width](auto const& neighbor) + { +#ifdef scalfmm_BUILD_PBC + auto const& neighbor_center = neighbor->center(); + auto shift = index::get_shift(target_center, neighbor_center, pbc, box_width); +#else + position_type shift(value_type(0.0)); +#endif + + p2p_outer(matrix_kernel, target_leaf, *neighbor, shift); + }; + std::for_each(std::begin(neighbors), std::begin(neighbors) + number_of_neighbors, apply_outer); + } + + /** + * @brief P2P function between the target leaf to update and the list of neighbors. + * + * @tparam MatrixKernelType + * @tparam LeafType + * @tparam ContainerOfLeafIteratorType + * @tparam ArrayType + * @tparam ValueType + * @param[in] matrix_kernel The kernel used to compute the interactions. + * @param[inout] target_leaf the target leaf (the output field will be updated by the source leaf). + * @param[in] neighbors the array of neighbors. + * @param[in] pbc the array of periodic boundary condition. + * @param[in] box_width the width of the simulation box. + */ + template<typename MatrixKernelType, typename LeafType, typename ContainerOfLeafIteratorType, typename ArrayType, + typename ValueType> + inline auto p2p_outer(MatrixKernelType const& matrix_kernel, LeafType& target_leaf, + ContainerOfLeafIteratorType const& neighbors, [[maybe_unused]] ArrayType const& pbc, + ValueType const& box_width) -> void + { + p2p_outer(matrix_kernel, target_leaf, neighbors, target_leaf.csymbolics().number_of_neighbors, pbc, box_width); + } + /** * @brief Compute the particle-to-particle interactions inside of a target leaf with mutual computation. * @@ -238,7 +456,7 @@ namespace scalfmm::operators * @param target_leaf the current leaf where we compute the particle-to-particle interactions. */ template<typename MatrixKernelType, typename LeafType> - inline void p2p_inner_mutual(MatrixKernelType const& matrix_kernel, LeafType& target_leaf) + inline auto p2p_inner_mutual(MatrixKernelType const& matrix_kernel, LeafType& target_leaf) -> void { using leaf_type = LeafType; using matrix_kernel_type = MatrixKernelType; @@ -247,7 +465,9 @@ namespace scalfmm::operators using outputs_type = typename particle_type::outputs_type; using value_type = typename leaf_type::value_type; using simd_type = xsimd::simd_type<value_type>; - using simd_position_type = container::point<simd_type, particle_type::dimension>; + + static constexpr std::size_t dimension = particle_type::dimension; + using simd_position_type = container::point<simd_type, dimension>; using simd_outputs_type = std::array<simd_type, particle_type::outputs_size>; static constexpr std::size_t kn = matrix_kernel_type::kn; @@ -269,6 +489,7 @@ namespace scalfmm::operators auto neighbor_outputs_iterator = container::outputs_begin(target_leaf.particles()) + neighbor_idx; const auto mutual_coefficient = matrix_kernel.template mutual_coefficient<simd_type>(); + // first part: simd computation { auto target_position = simd::load_position<simd_position_type>(target_position_iterator, simd::splated{}); @@ -364,6 +585,38 @@ namespace scalfmm::operators meta::tuple_sum_update(*target_outputs_iterator, target_outputs); } + + // third part: self-interaction (only if the kernel allows it) + { + if constexpr(meta::has_self_interaction_v<matrix_kernel_type, position_type>) + { + // load scalar target position + auto target_position = + simd::load_position<position_type>(target_position_iterator, simd::splated{}); + outputs_type target_outputs{}; + meta::for_each(target_outputs, [](auto& p) { p = value_type(0.); }); + + // load scalar target input + auto target_inputs = meta::to_array( + simd::load_tuple<value_type>(container::inputs_begin(target_leaf.cparticles()) + target_idx)); + + // get the matrix kernel evaluation from the particles' positions + auto kx_ = matrix_kernel.evaluate(target_position); + + for(std::size_t m = 0; m < km; ++m) + { + for(std::size_t n = 0; n < kn; ++n) + { + auto kx_nm = kx_.at(n * km + m); + target_outputs.at(n) += kx_nm * target_inputs.at(m); + } + } + + // update scalar target output + meta::tuple_sum_update(*target_outputs_iterator, target_outputs); + } + } + ++target_position_iterator; ++target_inputs_iterator; ++target_outputs_iterator; @@ -386,7 +639,7 @@ namespace scalfmm::operators * @param target_leaf The current leaf where we compute the particle-to-particle interactions. */ template<typename MatrixKernelType, typename LeafType> - inline void p2p_inner_non_mutual(MatrixKernelType const& matrix_kernel, LeafType& target_leaf) + inline auto p2p_inner_non_mutual(MatrixKernelType const& matrix_kernel, LeafType& target_leaf) -> void { using leaf_type = LeafType; using matrix_kernel_type = MatrixKernelType; @@ -395,7 +648,8 @@ namespace scalfmm::operators using outputs_type = typename particle_type::outputs_type; using value_type = typename leaf_type::value_type; using simd_type = xsimd::simd_type<value_type>; - using simd_position_type = container::point<simd_type, particle_type::dimension>; + static constexpr std::size_t dimension = particle_type::dimension; + using simd_position_type = container::point<simd_type, dimension>; using simd_outputs_type = std::array<simd_type, particle_type::outputs_size>; static constexpr std::size_t kn = matrix_kernel_type::kn; static constexpr std::size_t km = matrix_kernel_type::km; @@ -467,10 +721,11 @@ namespace scalfmm::operators // update scalar target output meta::tuple_sum_update(*target_outputs_iterator, reduced_target_outputs); } + // second part: scalar computation (end of the vector) // we make sure that the iteration target_idx == source_idx is not treated { - auto target_position = simd::load_position<position_type>(target_position_iterator); + auto target_position = simd::load_position<position_type>(target_position_iterator, simd::splated{}); outputs_type target_outputs{}; meta::for_each(target_outputs, [](auto& p) { p = value_type(0.); }); @@ -504,252 +759,73 @@ namespace scalfmm::operators // update scalar target output meta::tuple_sum_update(*target_outputs_iterator, target_outputs); } - ++target_position_iterator; - ++target_outputs_iterator; - } - } - - /** - * @brief Compute the particle-to-particle interactions inside of a target leaf. - * - * This function acts as an interface that either calls the mutual p2p inner operator or the non-mutual p2P inner - * operator depending on the boolean parameter 'mutual'. - * - * @tparam MatrixKernelType The type of the matrix kernel that is used in the p2p computation. - * @tparam LeafType The type of the leaf on which the operation will be performed. - * - * @param matrix_kernel A constant reference to the kernel object used to compute the interactions. - * @param target_leaf The current leaf where we compute the particle-to-particle interactions. - * @param mutual An optional boolean flag that determines whether the computation is mutual or not. - */ - template<typename MatrixKernelType, typename LeafType> - inline void p2p_inner(MatrixKernelType const& matrix_kernel, LeafType& target_leaf, bool mutual = true) - { - if(mutual) - { - p2p_inner_mutual(matrix_kernel, target_leaf); - } - else - { - p2p_inner_non_mutual(matrix_kernel, target_leaf); - } - } - /** - * @brief Computes the field due to the particles the source container on the target particles - * - * compute the field due to the particles the source container on the target particles by - * - * \f$ out(pt) = \sum_{ps\in source} { matrix\_kernel(pt, ps) input(ps) } \f$ - * - * We don't check if | pt - ps| =0 - * - * @tparam MatrixKernelType - * @tparam TargetLeafType - * @tparam SourceLeafType - * @tparam ArrayType - * @param matrix_kernel the kernel used to compute the interaction - * @param[inout] target_leaf the container of particles where the field is evaluated - * @param[in] source_leaf the container of particles which generate the field - * @param[in] shift the shift to apply on the particle in periodic system. - */ - template<typename MatrixKernelType, typename TargetLeafType, typename SourceLeafType, typename ArrayType> - inline void p2p_outer(MatrixKernelType const& matrix_kernel, TargetLeafType& target_leaf, - SourceLeafType const& source_leaf, [[maybe_unused]] ArrayType const& shift) - { -#ifndef NDEBUG - if constexpr(std::is_same_v<TargetLeafType, SourceLeafType>) - { - if(&target_leaf.symbolics() == &source_leaf.symbolics()) + // third part: self-interaction (only if the kernel allows it) { - throw std::runtime_error("In p2p_outer the two containers must be different."); - } - } -#endif - using target_leaf_type = TargetLeafType; - using source_leaf_type = SourceLeafType; - using matrix_kernel_type = MatrixKernelType; - using particle_type = typename target_leaf_type::particle_type; - using position_type = typename particle_type::position_type; - using outputs_type = typename particle_type::outputs_type; - using inputs_value_type = typename source_leaf_type::particle_type::inputs_value_type; - - using value_type = inputs_value_type; - using simd_type = xsimd::simd_type<value_type>; - using simd_position_type = container::point<simd_type, position_type::dimension>; - using simd_outputs_type = std::array<simd_type, particle_type::outputs_size>; - static constexpr std::size_t kn = matrix_kernel_type::kn; - static constexpr std::size_t km = matrix_kernel_type::km; - - static constexpr std::size_t inc = simd_type::size; - const std::size_t target_size{target_leaf.size()}; - const std::size_t neighbor_size{source_leaf.size()}; - const std::size_t vec_size = neighbor_size - neighbor_size % inc; - - auto target_position_iterator = container::position_begin(target_leaf.cparticles()); - auto target_outputs_iterator = container::outputs_begin(target_leaf.particles()); - -#ifdef scalfmm_BUILD_PBC - simd_position_type simd_shift(shift); -#endif - - for(std::size_t target_idx = 0; target_idx < target_size; ++target_idx) - { - auto neighbor_position_iterator = container::position_begin(source_leaf.cparticles()); - auto neighbor_inputs_iterator = container::inputs_begin(source_leaf.cparticles()); - - // Just need the position and the output for the target - auto target_position = simd::load_position<simd_position_type>(target_position_iterator, simd::splated{}); - - simd_outputs_type target_outputs{}; - meta::for_each(target_outputs, [](auto& v) { v = value_type(0.); }); - - { - // loop on the simd part of the neighbor particles - for(std::size_t neighbor_idx = 0; neighbor_idx < vec_size; neighbor_idx += inc) + if constexpr(meta::has_self_interaction_v<matrix_kernel_type, position_type>) { - // load simd position - auto neighbor_position = - simd::load_position<simd_position_type>(neighbor_position_iterator, simd::unaligned{}); - // load simd input values - auto neighbor_inputs_values = - meta::to_array(simd::load_tuple<simd_type>(neighbor_inputs_iterator, simd::unaligned{})); - -#ifdef scalfmm_BUILD_PBC - neighbor_position += simd_shift; -#endif - // get the matrix kernel evaluation from the particles positions - auto kxy_ = matrix_kernel.evaluate(target_position, neighbor_position); - - // loop on the n:m in/out matrix_kernel - for(std::size_t m = 0; m < km; ++m) - { - for(std::size_t n = 0; n < kn; ++n) - { - auto kxy_nm = kxy_.at(n * km + m); - // target_out(n) = K(n,m)*neighbor_in(m) - target_outputs.at(n) += kxy_nm * neighbor_inputs_values.at(m); - } - } - // increment iterators to the next simd sized values - neighbor_position_iterator += inc; - neighbor_inputs_iterator += inc; - } + // load scalar target position + auto target_position = + simd::load_position<position_type>(target_position_iterator, simd::splated{}); + outputs_type target_outputs{}; + meta::for_each(target_outputs, [](auto& p) { p = value_type(0.); }); - // reduce the target simd outputs vector - auto reduced_target_outputs = meta::apply(target_outputs, [](auto f) { return xsimd::reduce_add(f); }); - // update scalar target output - meta::tuple_sum_update(*target_outputs_iterator, reduced_target_outputs); - } - // same steps as before but for the scalar end of the neighbors particles vector - { - auto target_position = simd::load_position<position_type>(target_position_iterator); - outputs_type target_outputs{}; - meta::for_each(target_outputs, [](auto& p) { p = value_type(0.); }); + // load scalar target input + auto target_inputs = meta::to_array( + simd::load_tuple<value_type>(container::inputs_begin(target_leaf.cparticles()) + target_idx)); - for(std::size_t scal_idx = vec_size; scal_idx < neighbor_size; ++scal_idx) - { - auto neighbor_position = simd::load_position<position_type>(neighbor_position_iterator); - auto neighbor_inputs_values = - meta::to_array(simd::load_tuple<value_type>(neighbor_inputs_iterator)); + // get the matrix kernel evaluation from the particles' positions + auto kx_ = matrix_kernel.evaluate(target_position); -#ifdef scalfmm_BUILD_PBC - neighbor_position += shift; -#endif - auto kxy_ = matrix_kernel.evaluate(target_position, neighbor_position); for(std::size_t m = 0; m < km; ++m) { for(std::size_t n = 0; n < kn; ++n) { - auto kxy_nm = kxy_.at(n * km + m); - auto tmp_ = kxy_nm * neighbor_inputs_values.at(m); - target_outputs.at(n) += tmp_; + auto kx_nm = kx_.at(n * km + m); + target_outputs.at(n) += kx_nm * target_inputs.at(m); } } - ++neighbor_position_iterator; - ++neighbor_inputs_iterator; + // update scalar target output + meta::tuple_sum_update(*target_outputs_iterator, target_outputs); } - - meta::tuple_sum_update(*target_outputs_iterator, target_outputs); } + ++target_position_iterator; ++target_outputs_iterator; } } /** - * @brief p2p_outer compute the particle-to-particle interactions between the target leaf - * and its neighbors. + * @brief Compute the particle-to-particle interactions inside of a target leaf. * - * For each particle, x, in target_leaf we compute for each leaf, neighbor_leaf, - * in neighbors the interactions between particle y in - * $\f[ x.outputs = matrix_kernel(x,y) * y.inputs \f]$ + * This function acts as an interface that either calls the mutual p2p inner operator or the non-mutual p2P inner + * operator depending on the boolean parameter 'mutual'. * - * @warning{The neighbors are not modified.} - * @todo{To be implemented} + * @tparam MatrixKernelType The type of the matrix kernel that is used in the p2p computation. + * @tparam LeafType The type of the leaf on which the operation will be performed. * - * @tparam MatrixKernelType - * @tparam LeafType - * @tparam ContainerOfLeafIteratorType - * @tparam ArrayType - * @tparam ValueType - * @param matrix_kernel the kernel used to compute the interactions. - * @param target_leaf the current leaf where we compute the p2p. - * @param neighbors all neighboring leaves. - * @param number_of_neighbors number of neighbors. - * @param pbc - * @param box_width + * @param matrix_kernel A constant reference to the kernel object used to compute the interactions. + * @param target_leaf The current leaf where we compute the particle-to-particle interactions. + * @param mutual An optional boolean flag that determines whether the computation is mutual or not. */ - template<typename MatrixKernelType, typename LeafType, typename ContainerOfLeafIteratorType, typename ArrayType, - typename ValueType> - inline void p2p_outer(MatrixKernelType const& matrix_kernel, LeafType& target_leaf, - ContainerOfLeafIteratorType const& neighbors, const int number_of_neighbors, - [[maybe_unused]] ArrayType const& pbc, ValueType const& box_width) + template<typename MatrixKernelType, typename LeafType> + inline auto p2p_inner(MatrixKernelType const& matrix_kernel, LeafType& target_leaf, bool mutual = true) -> void { + using matrix_kernel_type = MatrixKernelType; using leaf_type = LeafType; - using position_type = typename leaf_type::position_type; - // // value type - using value_type = typename leaf_type::value_type; - // - // compute the center of the target_leaf - auto const& target_center = target_leaf.center(); - auto apply_outer = [&target_leaf, &matrix_kernel, &target_center, &pbc, &box_width](auto const& neighbor) - { -#ifdef scalfmm_BUILD_PBC - auto const& neighbor_center = neighbor->center(); - auto shift = index::get_shift(target_center, neighbor_center, pbc, box_width); -#else - position_type shift(value_type(0.0)); -#endif + static constexpr std::size_t dimension = leaf_type::dimension; + using array_type = std::array<bool, dimension>; - p2p_outer(matrix_kernel, target_leaf, *neighbor, shift); - }; - std::for_each(std::begin(neighbors), std::begin(neighbors) + number_of_neighbors, apply_outer); - } - - /** - * @brief P2P function between the target leaf to update and the list of neighbors. - * - * @tparam MatrixKernelType - * @tparam LeafType - * @tparam ContainerOfLeafIteratorType - * @tparam ArrayType - * @tparam ValueType - * @param[in] matrix_kernel The kernel used to compute the interactions. - * @param[inout] target_leaf the target leaf (the output field will be updated by the source leaf). - * @param[in] neighbors the array of neighbors. - * @param[in] pbc the array of periodic boundary condition. - * @param[in] box_width the width of the simulation box. - */ - template<typename MatrixKernelType, typename LeafType, typename ContainerOfLeafIteratorType, typename ArrayType, - typename ValueType> - inline void p2p_outer(MatrixKernelType const& matrix_kernel, LeafType& target_leaf, - ContainerOfLeafIteratorType const& neighbors, [[maybe_unused]] ArrayType const& pbc, - ValueType const& box_width) - { - p2p_outer(matrix_kernel, target_leaf, neighbors, target_leaf.csymbolics().number_of_neighbors, pbc, box_width); + if(mutual) + { + p2p_inner_mutual(matrix_kernel, target_leaf); + } + else + { + p2p_inner_non_mutual(matrix_kernel, target_leaf); + } } } // namespace scalfmm::operators diff --git a/include/scalfmm/tools/fma_loader.hpp b/include/scalfmm/tools/fma_loader.hpp index 6021822b1a0af9494da72d394c66d65ee1fdc339..02821e56a052c37da05638ecbbf532928b1985b4 100644 --- a/include/scalfmm/tools/fma_loader.hpp +++ b/include/scalfmm/tools/fma_loader.hpp @@ -557,17 +557,11 @@ namespace scalfmm::io class FFmaGenericWriter { protected: - /** - * @brief the stream used to write the file. - * - */ - std::fstream* m_file; + std::fstream* m_file; ///< the stream used to write the file. - /** - * @brief if true the file is in binary mode. - * - */ - bool m_binaryFile; + bool m_binaryFile; ///< if true the file is in binary mode. + + bool m_verbose; ///< verbose mode. public: /** @@ -582,8 +576,9 @@ namespace scalfmm::io */ FFmaGenericWriter(std::string const& filename, const bool verbose = true) : m_binaryFile(false) + , m_verbose{verbose} { - if(verbose) + if(m_verbose) { std::cout << "FFmaGenericWriter filename " << filename << std::endl; } @@ -612,39 +607,12 @@ namespace scalfmm::io std::cerr << "File " << filename << " not opened! " << std::endl; std::exit(EXIT_FAILURE); } - if(verbose) + if(m_verbose) { std::cout << "FFmaGenericWriter file " << filename << " opened" << std::endl; } } - // /** - // * This constructor opens a file to be written to. - // * - // * @param filename the name of the file to open. - // * @param binary true if the file to open is in binary mode - // */ - // FFmaGenericWriter(const std::string& filename, const bool binary) - // : m_file(nullptr) - // , m_binaryFile(binary) - // { - // if(binary) - // { - // this->m_file = new std::fstream(filename.c_str(), std::ifstream::out | std::ios::binary); - // } - // else - // { - // this->m_file = new std::fstream(filename.c_str(), std::ifstream::out); - // this->m_file->precision(std::numeric_limits<FReal>::digits10); - // } - // // test if open - // if(!this->m_file->is_open()) - // { - // std::cerr << "File " << filename << " not opened! " << std::endl; - // std::exit(EXIT_FAILURE); - // } - // std::cout << "FFmaGenericWriter file " << filename << " opened" << std::endl; - // } void close() { m_file->close(); @@ -715,20 +683,23 @@ namespace scalfmm::io { this->writerAscciHeader(centerOfBox, x, nbParticles, typeFReal.data(), 4); } - std::cout << " Datatype "; - for(int i = 0; i < 4; ++i) - { - std::cout << " " << typeFReal[i]; - } - std::cout << '\n'; - std::cout << " nbParticles: " << nbParticles << std::endl - << " Box width: " << boxWidth << std::endl - << " Center: "; - for(auto e: centerOfBox) + if(m_verbose) { - std::cout << e << " "; + std::cout << " Datatype "; + for(int i = 0; i < 4; ++i) + { + std::cout << " " << typeFReal[i]; + } + std::cout << '\n'; + std::cout << " nbParticles: " << nbParticles << std::endl + << " Box width: " << boxWidth << std::endl + << " Center: "; + for(auto e: centerOfBox) + { + std::cout << e << " "; + } + std::cout << std::endl << std::flush; } - std::cout << std::endl << std::flush; } template<typename IntType1, typename IntType2> auto writeHeader(const FReal* centerOfBox, const FReal& boxWidth, const IntType1& nbParticles, @@ -748,20 +719,23 @@ namespace scalfmm::io { this->writerAscciHeader(centerOfBox, x, nbParticles, typeFReal.data(), 4); } - std::cout << " Datatype "; - for(int i = 0; i < 4; ++i) - { - std::cout << " " << typeFReal[i]; - } - std::cout << '\n'; - std::cout << " nbParticles: " << nbParticles << std::endl - << " Box width: " << boxWidth << std::endl - << " Center: "; - for(int i = 0; i < typeFReal[2]; ++i) + if(m_verbose) { - std::cout << centerOfBox[i] << " "; + std::cout << " Datatype "; + for(int i = 0; i < 4; ++i) + { + std::cout << " " << typeFReal[i]; + } + std::cout << '\n'; + std::cout << " nbParticles: " << nbParticles << std::endl + << " Box width: " << boxWidth << std::endl + << " Center: "; + for(int i = 0; i < typeFReal[2]; ++i) + { + std::cout << centerOfBox[i] << " "; + } + std::cout << std::endl << std::flush; } - std::cout << std::endl << std::flush; } /** * @brief @@ -861,10 +835,13 @@ namespace scalfmm::io constexpr std::size_t nb_input_elements = TreeType::leaf_type::particle_type::inputs_size; constexpr std::size_t nb_output_elements = TreeType::leaf_type::particle_type::outputs_size; constexpr std::size_t nb_elt_per_par = dimension + nb_input_elements + nb_output_elements; - std::cout << "Dimension: " << dimension << std::endl; - std::cout << "Number of input values: " << nb_input_elements << std::endl; - std::cout << "Number of output values: " << nb_output_elements << std::endl; - std::cout << "nb_elt_per_par " << nb_elt_per_par << std::endl; + if(m_verbose) + { + std::cout << "Dimension: " << dimension << std::endl; + std::cout << "Number of input values: " << nb_input_elements << std::endl; + std::cout << "Number of output values: " << nb_output_elements << std::endl; + std::cout << "nb_elt_per_par " << nb_elt_per_par << std::endl; + } // using value_type = typename TreeType::leaf_type::value_type; using particles_t = std::array<value_type, nb_elt_per_par>; @@ -916,8 +893,8 @@ namespace scalfmm::io * @tparam ContainerType * @tparam PointType * @tparam ValueType - * @param values - * @param number_particles + * @param values the contnaire of particles + * @param number_particles. number of particles to write * @param center * @param box_width */ @@ -955,6 +932,75 @@ namespace scalfmm::io nb_elt_per_par, dimension, nb_input_per_par); this->writeArrayOfReal(particles.data()->data(), nb_elt_per_par, number_particles); } + /** + * @brief + * + * writeDataFrom write all data from the std::vector of particles in fma format + * + * How to get automatically double from container + * + * @tparam ContainerType + * @tparam PointType + * @tparam ValueType + * @param values the vector of particles + * @param center the center of the box + * @param box_width th width of the box + */ + template<class ParticleType, class PointType, typename ValueType> + auto writeDataFrom(std::vector<ParticleType>& values, const PointType& center, const ValueType box_width) + -> void + { + // get the number of elements per particles in the container build with tuples. + using particle_type = ParticleType; + static constexpr std::size_t dimension = particle_type::dimension_size; + static constexpr std::size_t outputs_size = particle_type::outputs_size; + static constexpr std::size_t inputs_size = particle_type::inputs_size; + constexpr std::size_t nb_elt_per_par = scalfmm::meta ::particle_size_without_variables<particle_type>::size; + /// @todo check for different input and output types (double versus complexe) + using data_type = typename particle_type::outputs_value_type; + using particles_t = std::array<data_type, nb_elt_per_par>; + // Not good output_values are put in input_values + constexpr std::size_t nb_input_per_par = particle_type::inputs_size; + + auto number_particles = values.size(); + // if constexpr(nb_elt_per_par < scalfmm::meta ::tuple_size_v<particle_type>) + if constexpr(sizeof(particle_type) > nb_elt_per_par * sizeof(ValueType)) + // + { + // std::cout << " I have variables\n"; + std::vector<particles_t> particles(number_particles); +#pragma omp parallel for shared(particles, values) + for(std::size_t i = 0; i < values.size(); ++i) + { + auto ptr_data = &(values[i].position()[0]); + std::copy(ptr_data, ptr_data + nb_elt_per_par, particles[i].data()); + } + // write the particles + // Here we need to separate input from output variables - no tools yet + this->writeHeader(center, box_width, number_particles, static_cast<std::size_t>(sizeof(data_type)), + nb_elt_per_par, dimension, nb_input_per_par); + this->writeArrayOfReal(particles.data()->data(), nb_elt_per_par, number_particles); + } + else + { + // std::cout << nb_elt_per_par << " . " << scalfmm::meta ::tuple_size_v<particle_type> << std::endl; + this->writeHeader(center, box_width, number_particles, static_cast<std::size_t>(sizeof(data_type)), + nb_elt_per_par, dimension, nb_input_per_par); + // std::cout << " " << &(values[0].position()[0]) << " " << values.data() << std::endl; + auto ptr_data = &(values[0].position()[0]); + // int k{0}; + // for(int i = 0; i < 3; ++i) + // { + // std::cout << i << " part " << values[i] << " array "; + // for(int j = 0; j < nb_elt_per_par; ++j, ++k) + // { + // std::cout << ptr_data[k] << " "; + // } + // std::cout << std::endl; + // } + this->writeArrayOfReal(ptr_data, nb_elt_per_par, number_particles); + } + } protected: /** diff --git a/include/scalfmm/utils/low_rank.hpp b/include/scalfmm/utils/low_rank.hpp index 0b40a83787b64fea3e8b52cc04464e2123adf29f..a54fd7371d0b6af7330ca1df34309b90074bfb67 100644 --- a/include/scalfmm/utils/low_rank.hpp +++ b/include/scalfmm/utils/low_rank.hpp @@ -5,30 +5,38 @@ #ifndef SCALFMM_UTILS_LOW_RANK_HPP #define SCALFMM_UTILS_LOW_RANK_HPP +#include <array> +#include <cpp_tools/colors/colorized.hpp> #include <xtensor/xtensor.hpp> namespace scalfmm::low_rank { /** - * @brief + * @brief Partial Adaptive cross approximation + * + * The partially pivoted adaptive cross approximation (pACA) compresses a + * matrix kernel interaction as \f$K\sim UV^\top\f$. The pACA computes the matrix + * entries on the fly, as they are needed. The compression follows in + * \f$\mathcal{O}(2\ell^3k)\f$ operations based on the required accuracy + * \f$\varepsilon\f$. * * @tparam ValueType * @tparam MatrixKernelType * @tparam TensorViewX * @tparam TensorViewY - * @param mk - * @param X - * @param Y - * @param weights - * @param epsilon - * @param kn - * @param km - * @return std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> + * @param[in] mk the matrix kernel to be approximated + * @param[in] X the points involved in the rows + * @param[in] Y the points involved in the column + * @param[in] weights the weights + * @param[in] epsilon the prescribed accuracy for low-rank approximation + * @param[in] kn the number of row of the matrix kernel + * @param[in] km the number of column of the matrix kernel + * @return std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> containing \a k column vectors and k row vectors */ template<typename ValueType, typename MatrixKernelType, typename TensorViewX, typename TensorViewY> inline auto paca(MatrixKernelType const& mk, TensorViewX&& X, TensorViewY&& Y, xt::xarray<ValueType> const& weights, - ValueType epsilon, std::size_t kn, - std::size_t km) -> std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> + ValueType epsilon, std::size_t kn, std::size_t km) + -> std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> { const std::size_t nnodes{X.size()}; std::vector<bool> row_bools(nnodes, true); @@ -130,37 +138,182 @@ namespace scalfmm::low_rank auto VV = xt::eval(xt::view(V, xt::all(), xt::range(0, r))); return std::make_tuple(UU, VV); } + template<typename MatrixType, typename ValueType, typename IntType> + static IntType get_numerical_rank(MatrixType const& S, const IntType size, const ValueType eps) + { + const ValueType nrm2 = xt::linalg::vdot(S, S); + const ValueType neta2 = eps * eps * nrm2; + + ValueType cumulativeSum{nrm2}; + int rank = 0; + + for(int i = 0; i < S.size(); i++) + { + cumulativeSum -= S[i] * S[i]; + ++rank; + + if(cumulativeSum < neta2) + { + // std::cout << i << " threshold " << neta2 << " cumulativeSum " << cumulativeSum << std::endl; + // std::cout << " rank " << rank << " S.size() " << S.size() << std::endl; + break; + } + } + return rank; // std::min(rank , int(S.size())); + } + + template<typename MatrixType, typename ValueType> + void check_reconstruction(MatrixType const& K, const MatrixType& U, const MatrixType& V, const ValueType epsilon, + bool stop = false) + { + auto nnodes = K.shape()[0]; + auto normK{xt::linalg::norm(K)}; + xt::xarray<ValueType> prod(std::vector(2, nnodes)); + xt::blas::gemm(U, V, prod, false, true); + + auto error = K - prod; + auto norm{xt::linalg::norm(error)}; + auto error_recom{norm / normK}; + + if(error_recom > epsilon) + { + std::cout << cpp_tools::colors::red << "error rank " << U.shape()[1] << " rel error " << error_recom + << " epsilon " << epsilon << " ratio " << error_recom / epsilon << std::endl + << cpp_tools::colors::reset; + if(stop) + { + std::stringstream ss{}; + ss << error_recom << " > " << epsilon; + throw std::runtime_error("error in svd: " + ss.str()); + } + } + // else + // { + // std::cout << "error rank " << U.shape()[1] << " rel error " << error_recom << " epsilon " << epsilon + // << std::endl; + // } + } + /** + * @brief truncated SVD approximation + * + * The singular value decomposition) compresses a + * matrix kernel interaction as \f$K\sim UV^\top\f$ with the required accuracy + * \f$\varepsilon\f$. + * + * @tparam ValueType + * @tparam MatrixKernelType + * @tparam TensorViewX + * @tparam TensorViewY + * @param[in] mk the matrix kernel to be approximated + * @param[in] X the points involved in the rows + * @param[in] Y the points involved in the column + * @param[in] weights the weights + * @param[in] epsilon the prescribed accuracy for low-rank approximation + * @param[in] kn the number of row of the matrix kernel + * @param[in] km the number of column of the matrix kernel + * @return std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> containing \a k column vectors and k row vectors + */ + + template<typename ValueType, typename MatrixKernelType, typename TensorViewX, typename TensorViewY> + inline auto tsvd(MatrixKernelType const& mk, TensorViewX&& X, TensorViewY&& Y, xt::xarray<ValueType> const& weights, + ValueType epsilon, std::size_t kn, std::size_t km) + -> std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> + { + using matrix_tye = xt::xtensor<ValueType, 2, xt::layout_type::column_major>; + const std::size_t nnodes{X.size()}; + matrix_tye K; + K.resize({nnodes, nnodes}); + + // xt::xarray<ValueType> K(std::vector<int>(2, nnodes)); + ValueType normW{1}; + + for(std::size_t i{0}; i < nnodes; ++i) + { + for(std::size_t j{0}; j < nnodes; ++j) + { + // normW += weights[i] * weights[i]; + K.at(i, j) = weights[i] * weights[j] * mk.evaluate(X[i], Y[j]).at(kn * MatrixKernelType::km + km); + } + } + // std::cout << " normW " << normW << " epsilon " << epsilon << " new eps/normW2 " << epsilon / normW << std::endl; + auto eps{epsilon / normW}; + + // res_svd = (U, S, Vt) + auto res_svd = xt::linalg::svd(K, false, true); + auto& S = std::get<1>(res_svd); + // std::cout << "S " << S << std::endl; + auto rank = get_numerical_rank(S, nnodes, eps); + // + // std::cout << "numerical_rank: " << rank << std::endl; + auto& UU = std::get<0>(res_svd); + auto& VT = std::get<2>(res_svd); // is VT + + auto U = xt::eval(xt::view(UU, xt::all(), xt::range(0, rank))); + auto V = xt::eval(xt::view(VT, xt::range(0, rank), xt::all())); + for(std::size_t j = 0; j < rank; ++j) + { + for(std::size_t i = 0; i < nnodes; ++i) + { + U.at(i, j) *= S.at(j); + } + } + + // std::cout << " K with weights \n"; + // check_reconstruction(K, U, xt::eval(xt::transpose(V)), epsilon, false); + // + return std::make_tuple(U, xt::eval(xt::transpose(V))); + } /** - * @brief + * @brief Generate the low rank approximation of the matix kernel * + * use a partial adaptive cross approximation + * followed by QR+SVD recompression of the U and V returned by ACA. + * This allows to avoid potential redundancies in U and V, + * since orthogonality is not ensured by ACA. + * @tparam ValueType * @tparam MatrixKernelType * @tparam TensorViewX * @tparam TensorViewY - * @param mk - * @param X - * @param Y - * @param weights - * @param epsilon - * @param kn - * @param km - * @return std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> + * @param[in] mk the matrix kernel to be approximated + * @param[in] X the points involved in the rows + * @param[in] Y the points involved in the column + * @param[in] weights the weights + * @param[in] epsilon the prescribed accuracy for low-rank approximation + * @param[in] kn the number of row of the matrix kernel + * @param[in] km the number of column of the matrix kernel + + * @return std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> U, V the low rank approximation of mk */ template<typename ValueType, typename MatrixKernelType, typename TensorViewX, typename TensorViewY> inline auto generate(MatrixKernelType const& mk, TensorViewX&& X, TensorViewY&& Y, - xt::xarray<ValueType> const& weights, ValueType epsilon, std::size_t kn, - std::size_t km) -> std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> + xt::xarray<ValueType> const& weights, ValueType epsilon, std::size_t kn, std::size_t km) + -> std::tuple<xt::xtensor<ValueType, 2>, xt::xtensor<ValueType, 2>> { xt::xtensor<ValueType, 2, xt::layout_type::column_major> U; xt::xtensor<ValueType, 2, xt::layout_type::column_major> V; + // #define USE_SVD +#ifdef LOW_RANK_USE_SVD + + // return U and V such that K = U VT + std::tie(U, V) = tsvd(mk, std::forward<TensorViewX>(X), std::forward<TensorViewY>(Y), weights, epsilon, kn, km); + + auto nnodes = U.shape()[0]; + auto rank = U.shape()[1]; + +#else + // std::cout << " ACA " << std::endl; xt::xtensor<ValueType, 2, xt::layout_type::column_major> QU; xt::xtensor<ValueType, 2, xt::layout_type::column_major> QV; xt::xtensor<ValueType, 2, xt::layout_type::column_major> RU; xt::xtensor<ValueType, 2, xt::layout_type::column_major> RV; - std::tie(U, V) = paca(mk, std::forward<TensorViewX>(X), std::forward<TensorViewY>(Y), weights, epsilon, kn, km); + // Perform QR+SVD recompression of the U and V returned by ACA. + // This allows to avoid potential redundancies in U and V, + // since orthogonality is not ensured by ACA. + std::tie(QU, RU) = xt::linalg::qr(U); std::tie(QV, RV) = xt::linalg::qr(V); @@ -185,24 +338,10 @@ namespace scalfmm::low_rank U_.at(i, j) *= S.at(j); } } - xt::blas::gemm(QU, U_, U, false, false); - //xt::xarray<ValueType> K(std::vector(2, nnodes)); - //for(std::size_t i{0}; i < nnodes; ++i) - //{ - // for(std::size_t j{0}; j < nnodes; ++j) - // { - // K.at(i, j) = weights[i] * weights[j] * mk.evaluate(X[i], Y[j]).at(kn * MatrixKernelType::km + km); - // } - //} - - //xt::xarray<ValueType> prod(std::vector(2, nnodes)); - //xt::blas::gemm(U, V, prod, false, true); - //auto error = K-prod; - //std::cout << xt::linalg::norm(error) << '\n'; - - // unweightening +#endif + // std::cout << " compression rank " << rank << " epsilon " << epsilon << std::endl; // unweightening for(std::size_t j = 0; j < rank; ++j) { for(std::size_t i = 0; i < nnodes; ++i) @@ -214,7 +353,18 @@ namespace scalfmm::low_rank xt::xtensor<ValueType, 2> U_row(U); xt::xtensor<ValueType, 2> V_row(V); - +#ifndef LOW_RANK_CHECK + xt::xtensor<ValueType, 2, xt::layout_type::column_major> K; + K.resize({nnodes, nnodes}); + for(std::size_t i{0}; i < nnodes; ++i) + { + for(std::size_t j{0}; j < nnodes; ++j) + { + K.at(i, j) = mk.evaluate(X[i], Y[j]).at(kn * MatrixKernelType::km + km); + } + } + check_reconstruction(K, U, V, epsilon); +#endif return std::make_tuple(U_row, V_row); }