fix: Grid splitting bug and stats calculation
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e8e09b0637
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@ -4,8 +4,8 @@
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#include <ostream>
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#include <utility>
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#include <vector>
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#define _USE_MATH_DEFINES /* enables math constants from cmath */
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#include <cmath>
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#include <assert.h>
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inline size_t mod(int num, size_t module) {
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while (num < 0) { num += module; };
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@ -87,36 +87,45 @@ public:
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}
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}
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/**
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* Distance of this Matrix to given matrix A as || this - A ||_N
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*
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* Note: It there is a dimension missmatch, only the number of elements
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* of the smaller matrix are considured.
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*
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* @param A matrix to compute the "distance" to
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* @param mar_b bottom margins
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* @param mar_l left margins
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* @param mar_t top margins
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* @param mar_r right margins
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*/
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template <enum Norm N = Norm::Frob>
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double dist(const Matrix<T>& A) const {
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T accum = static_cast<T>(0); /*< result accomulator */
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size_t nelem = size() < A.size() ? size() : A.size();
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double dist(const Matrix<T>& A,
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size_t mar_b = 0, size_t mar_l = 0, size_t mar_t = 0, size_t mar_r = 0
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) const {
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// Enforce valid norm types (at compile time)
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static_assert(N == Norm::Frob || N == Norm::Max);
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if constexpr (N == Norm::Frob) {
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// Sum of Squared differences
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for (size_t i = 0; i < nelem; ++i) {
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T diff = (*this)(i) - A(i);
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accum += diff * diff;
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}
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return std::sqrt(static_cast<double>(accum));
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} else { // Maximum Norm
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for (size_t i = 0; i < nelem; ++i) {
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T diff = std::abs((*this)(i) - A(i));
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if (accum < diff) {
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accum = diff;
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}
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}
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return static_cast<double>(accum);
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assert(this->_nrow == A._nrow);
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assert(this->_ncol == A._ncol);
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T accum = static_cast<T>(0); /*< result accomulator */
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for (size_t j = mar_l; j + mar_r < _ncol; ++j) {
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for (size_t i = mar_t; i + mar_b < _nrow; ++i) {
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if constexpr (N == Norm::Frob) {
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T diff = (*this)(i, j) - A(i, j);
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accum += diff * diff;
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} else {
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accum = std::max(std::abs((*this)(i, j) - A(i, j)), accum);
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}
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}
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}
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if constexpr (N == Norm::Frob) {
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return std::sqrt(static_cast<double>(accum));
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} else {
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return static_cast<double>(accum);
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}
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}
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@ -62,13 +62,13 @@ public:
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for (size_t y = 0; y < ny; ++y) { _sol(0, y) = _tmp(0, y) = gW(Y(y)); }
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};
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/* Maps x/y index to x/y position in the [xmin, xmax] x [ymin, ymax] domain */
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double X(size_t x) const {
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double ratio = static_cast<double>(x) / static_cast<double>(_nx - 1);
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/* Maps (i, j) indices to (x, y) position in the [xmin, xmax] x [ymin, ymax] domain */
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double X(size_t i) const {
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double ratio = static_cast<double>(i) / static_cast<double>(_nx - 1);
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return _xmin + ratio * (_xmax - _xmin);
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}
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double Y(size_t y) const {
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double ratio = static_cast<double>(y) / static_cast<double>(_ny - 1);
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double Y(size_t j) const {
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double ratio = static_cast<double>(j) / static_cast<double>(_ny - 1);
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return _ymin + ratio * (_ymax - _ymin);
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}
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@ -1,6 +1,7 @@
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/**
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*
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*/
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#define _USE_MATH_DEFINES /* enables math constants from cmath */
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#include <stddef.h>
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#include <iostream>
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@ -8,7 +9,6 @@
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#include <sstream>
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#include <functional>
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#include <chrono>
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#define _USE_MATH_DEFINES /* enables math constants from cmath */
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#include <cmath>
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#ifdef USE_MPI
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#include <mpi.h>
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@ -165,13 +165,13 @@ int main(int argn, char* argv[]) {
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// Compute local domain [xmin, xmax] x [ymin, ymax]
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double xmin = (mpi_neighbours.west == MPI_PROC_NULL) ? 0.0
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: h * partition_sum(resolution, mpi_dims[0], mpi_coords[0] - 1);
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: h * (partition_sum(resolution, mpi_dims[0], mpi_coords[0] - 1) - 1);
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double xmax = (mpi_neighbours.east == MPI_PROC_NULL) ? 1.0
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: h * (partition_sum(resolution, mpi_dims[0], mpi_coords[0]) + 1);
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: h * partition_sum(resolution, mpi_dims[0], mpi_coords[0]);
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double ymin = (mpi_neighbours.south == MPI_PROC_NULL) ? 0.0
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: h * partition_sum(resolution, mpi_dims[1], mpi_coords[1] - 1);
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: h * (partition_sum(resolution, mpi_dims[1], mpi_coords[1] - 1) - 1);
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double ymax = (mpi_neighbours.north == MPI_PROC_NULL) ? 1.0
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: h * (partition_sum(resolution, mpi_dims[1], mpi_coords[1]) + 1);
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: h * partition_sum(resolution, mpi_dims[1], mpi_coords[1]);
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// Create MPI vector Type (for boundary condition exchange)
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// Allows directly exchange of matrix rows (north/south bounds) since the
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@ -283,18 +283,16 @@ int main(int argn, char* argv[]) {
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// Compute analytic (true) solution
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Matrix<double> analytic(solution.nrow(), solution.ncol());
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double x = xmin;
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double y = ymin;
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for (size_t j = 0; j < analytic.ncol(); ++j, y += h) {
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for (size_t i = 0; i < analytic.nrow(); ++i, x += h) {
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analytic(i, j) = sin(2 * M_PI * x) * sinh(2 * M_PI * y);
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for (size_t j = 0; j < analytic.ncol(); ++j) {
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for (size_t i = 0; i < analytic.nrow(); ++i) {
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analytic(i, j) = sin(2 * M_PI * solver.X(i)) * sinh(2 * M_PI * solver.Y(j));
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}
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}
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#ifdef USE_MPI
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// Frobenius Norm Accumulator
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double frob_norm_sendbuf[2] = {
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solver.resid_norm(), solution.dist(analytic)
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solver.resid_norm(), solution.dist(analytic, 1, 1, 1, 1)
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};
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// Square the norms (Accumulation of partial results is the square root of
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// the sum of the squared partial norms)
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@ -302,7 +300,7 @@ int main(int argn, char* argv[]) {
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frob_norm_sendbuf[1] = frob_norm_sendbuf[1] * frob_norm_sendbuf[1];
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// Maximum Norm Accumulator
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double max_norm_sendbuf[2] = {
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solver.resid_norm<Max>(), solution.dist<Max>(analytic)
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solver.resid_norm<Max>(), solution.dist<Max>(analytic, 1, 1, 1, 1)
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};
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// Global result reduction buffers
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@ -320,10 +318,10 @@ int main(int argn, char* argv[]) {
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frob_norm_recvbuf[1] = std::sqrt(frob_norm_recvbuf[1]);
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#else
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double frob_norm_recvbuf[2] = {
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solver.resid_norm(), solution.dist(analytic)
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solver.resid_norm(), solution.dist(analytic, 1, 1, 1, 1)
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};
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double max_norm_recvbuf[2] = {
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solver.resid_norm<Max>(), solution.dist<Max>(analytic)
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solver.resid_norm<Max>(), solution.dist<Max>(analytic, 1, 1, 1, 1)
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};
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#endif
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