Line data Source code
1 : ///////////////////////////////////////////////////////////////////////////////
2 : // Copyright 2019 Lakshman Anumolu, Raunak Bardia.
3 : //
4 : // Redistribution and use in source and binary forms, with or without
5 : // modification, are permitted provided that the following conditions are
6 : // met:
7 : //
8 : // 1. Redistributions of source code must retain the above copyright notice,
9 : // this list of conditions and the following disclaimer.
10 : //
11 : // 2. Redistributions in binary form must reproduce the above copyright notice,
12 : // this list of conditions and the following disclaimer in the documentation
13 : // and/or other materials provided with the distribution.
14 : //
15 : // 3. Neither the name of the copyright holder nor the names of its contributors
16 : // may be used to endorse or promote products derived from this software without
17 : // specific prior written permission.
18 : //
19 : // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
20 : // "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
21 : // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
22 : // A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
23 : // HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
24 : // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
25 : // LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
26 : // DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
27 : // THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
28 : // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
29 : // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
30 : ///////////////////////////////////////////////////////////////////////////////
31 :
32 : #include "gals/cpu/gradient/third-order.h"
33 :
34 : template <typename T, typename T_GRID>
35 6 : GALS::CPU::ThirdOrder<T, T_GRID>::ThirdOrder()
36 : {
37 6 : }
38 :
39 : template <typename T, typename T_GRID>
40 6 : GALS::CPU::ThirdOrder<T, T_GRID>::~ThirdOrder()
41 : {
42 6 : }
43 :
44 : template <typename T, typename T_GRID>
45 3 : void GALS::CPU::ThirdOrder<T, T_GRID>::compute(const Array<T_GRID, T> &alpha, Array<T_GRID, Vec3<T>> &grad_alpha)
46 : {
47 6 : const Vec3<int> num_cells = alpha.numCells();
48 3 : const T_GRID &grid = alpha.grid();
49 6 : const Vec3<typename T_GRID::value_type> dx = grid.dX();
50 : const auto &axis_vectors = GALS::CPU::Grid<typename T_GRID::value_type, T_GRID::dim>::axis_vectors;
51 :
52 63 : for (int i = 0; i < num_cells[0]; ++i)
53 450 : for (int j = 0; j < num_cells[1]; ++j)
54 2430 : for (int k = 0; k < num_cells[2]; ++k) {
55 7530 : for (int axis = 0; axis < T_GRID::dim; ++axis) {
56 3210 : typename T_GRID::value_type one_by_dx = static_cast<typename T_GRID::value_type>(1.) / (6.0 * dx[axis]);
57 :
58 6420 : grad_alpha(i, j, k)[axis] =
59 6420 : (2 * alpha(i + axis_vectors(axis, 0), j + axis_vectors(axis, 1), k + axis_vectors(axis, 2)) +
60 6420 : 3 * alpha(i, j, k) -
61 6420 : 6 * alpha(i - axis_vectors(axis, 0), j - axis_vectors(axis, 1), k - axis_vectors(axis, 2)) +
62 6420 : 1 * alpha(i - 2 * axis_vectors(axis, 0), j - 2 * axis_vectors(axis, 1), k - 2 * axis_vectors(axis, 2))) *
63 : one_by_dx;
64 : }
65 : }
66 3 : }
67 :
68 : template <typename T, typename T_GRID>
69 3 : void GALS::CPU::ThirdOrder<T, T_GRID>::compute(const Array<T_GRID, Vec3<T>> &alpha, Array<T_GRID, Mat3<T>> &grad_alpha)
70 : {
71 6 : const Vec3<int> num_cells = alpha.numCells();
72 3 : const T_GRID &grid = alpha.grid();
73 6 : const Vec3<typename T_GRID::value_type> dx = grid.dX();
74 : const auto &axis_vectors = GALS::CPU::Grid<typename T_GRID::value_type, T_GRID::dim>::axis_vectors;
75 :
76 63 : for (int i = 0; i < num_cells[0]; ++i)
77 450 : for (int j = 0; j < num_cells[1]; ++j)
78 2430 : for (int k = 0; k < num_cells[2]; ++k) {
79 7520 : for (int axis = 0; axis < T_GRID::dim; ++axis) {
80 22030 : for (int cmpt = 0; cmpt < T_GRID::dim; ++cmpt) {
81 9410 : typename T_GRID::value_type one_by_dx = static_cast<typename T_GRID::value_type>(1.) / (6 * dx[cmpt]);
82 :
83 18820 : grad_alpha(i, j, k)(axis, cmpt) =
84 18820 : (2 * alpha(i + axis_vectors(axis, 0), j + axis_vectors(axis, 1), k + axis_vectors(axis, 2))[axis] +
85 18820 : 3 * alpha(i, j, k)[axis] -
86 18820 : 6 * alpha(i - axis_vectors(axis, 0), j - axis_vectors(axis, 1), k - axis_vectors(axis, 2))[axis] +
87 9410 : 1 * alpha(i - 2 * axis_vectors(axis, 0), j - 2 * axis_vectors(axis, 1),
88 28230 : k - 2 * axis_vectors(axis, 2))[axis]) *
89 : one_by_dx;
90 : }
91 : }
92 : }
93 3 : }
94 :
95 : template class GALS::CPU::ThirdOrder<double, GALS::CPU::Grid<double, 1>>;
96 : template class GALS::CPU::ThirdOrder<double, GALS::CPU::Grid<double, 2>>;
97 3 : template class GALS::CPU::ThirdOrder<double, GALS::CPU::Grid<double, 3>>;
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