ESPResSo
Extensible Simulation Package for Research on Soft Matter Systems
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DiffusiveFluxKernel_double_precision.cpp
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1//======================================================================================================================
2//
3// This file is part of waLBerla. waLBerla is free software: you can
4// redistribute it and/or modify it under the terms of the GNU General Public
5// License as published by the Free Software Foundation, either version 3 of
6// the License, or (at your option) any later version.
7//
8// waLBerla is distributed in the hope that it will be useful, but WITHOUT
9// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
10// FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
11// for more details.
12//
13// You should have received a copy of the GNU General Public License along
14// with waLBerla (see COPYING.txt). If not, see <http://www.gnu.org/licenses/>.
15//
16//! \\file DiffusiveFluxKernel_double_precision.cpp
17//! \\author pystencils
18//======================================================================================================================
19
20// kernel generated with pystencils v1.3.7+13.gdfd203a, lbmpy v1.3.7+10.gd3f6236, sympy v1.12.1, lbmpy_walberla/pystencils_walberla from waLBerla commit c69cb11d6a95d32b2280544d3d9abde1fe5fdbb5
21
22#include <cmath>
23
24#include "DiffusiveFluxKernel_double_precision.h"
25#include "core/DataTypes.h"
26#include "core/Macros.h"
27
28#define FUNC_PREFIX
29
30#if (defined WALBERLA_CXX_COMPILER_IS_GNU) || (defined WALBERLA_CXX_COMPILER_IS_CLANG)
31#pragma GCC diagnostic push
32#pragma GCC diagnostic ignored "-Wfloat-equal"
33#pragma GCC diagnostic ignored "-Wshadow"
34#pragma GCC diagnostic ignored "-Wconversion"
35#pragma GCC diagnostic ignored "-Wunused-variable"
36#endif
37
38#if (defined WALBERLA_CXX_COMPILER_IS_INTEL)
39#pragma warning push
40#pragma warning(disable : 1599)
41#endif
42
43using namespace std;
44
45namespace walberla {
46namespace pystencils {
47
48namespace internal_e5e04d1215f19faa51f3c55db6d456a2 {
49static FUNC_PREFIX void diffusivefluxkernel_double_precision_diffusivefluxkernel_double_precision(double D, double *RESTRICT const _data_j, double *RESTRICT const _data_rho, int64_t const _size_j_0, int64_t const _size_j_1, int64_t const _size_j_2, int64_t const _stride_j_0, int64_t const _stride_j_1, int64_t const _stride_j_2, int64_t const _stride_j_3, int64_t const _stride_rho_0, int64_t const _stride_rho_1, int64_t const _stride_rho_2) {
50 for (int64_t ctr_2 = 0; ctr_2 < _size_j_2; ctr_2 += 1) {
51 for (int64_t ctr_1 = 0; ctr_1 < _size_j_1; ctr_1 += 1) {
52 for (int64_t ctr_0 = 1; ctr_0 < _size_j_0; ctr_0 += 1) {
53 if (ctr_1 > 0 && ctr_2 > 0 && ctr_1 < _size_j_1 - 1 && ctr_2 < _size_j_2 - 1) {
54 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 - _stride_rho_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2]) * 0.16292407789368385;
55 }
56 if (ctr_1 > 0 && ctr_2 > 0 && ctr_0 < _size_j_0 - 1 && ctr_2 < _size_j_2 - 1) {
57 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 - _stride_rho_1 + _stride_rho_2 * ctr_2]) * 0.16292407789368385;
58 }
59 if (ctr_1 > 0 && ctr_2 > 0 && ctr_0 < _size_j_0 - 1 && ctr_1 < _size_j_1 - 1) {
60 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 2 * _stride_j_3] = D * (-_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2 - _stride_rho_2] + _data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2]) * 0.16292407789368385;
61 }
62 if (ctr_1 > 0 && ctr_2 > 0 && ctr_2 < _size_j_2 - 1) {
63 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 3 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 - _stride_rho_0 + _stride_rho_1 * ctr_1 - _stride_rho_1 + _stride_rho_2 * ctr_2]) * 0.11520472029718914;
64 }
65 if (ctr_2 > 0 && ctr_1 < _size_j_1 - 1 && ctr_2 < _size_j_2 - 1) {
66 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 4 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 - _stride_rho_0 + _stride_rho_1 * ctr_1 + _stride_rho_1 + _stride_rho_2 * ctr_2]) * 0.11520472029718914;
67 }
68 if (ctr_1 > 0 && ctr_2 > 0 && ctr_1 < _size_j_1 - 1) {
69 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 5 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 - _stride_rho_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2 - _stride_rho_2]) * 0.11520472029718914;
70 }
71 if (ctr_1 > 0 && ctr_1 < _size_j_1 - 1 && ctr_2 < _size_j_2 - 1) {
72 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 6 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 - _stride_rho_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2 + _stride_rho_2]) * 0.11520472029718914;
73 }
74 if (ctr_1 > 0 && ctr_2 > 0 && ctr_0 < _size_j_0 - 1) {
75 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 7 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 - _stride_rho_1 + _stride_rho_2 * ctr_2 - _stride_rho_2]) * 0.11520472029718914;
76 }
77 if (ctr_1 > 0 && ctr_0 < _size_j_0 - 1 && ctr_2 < _size_j_2 - 1) {
78 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 8 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 - _stride_rho_1 + _stride_rho_2 * ctr_2 + _stride_rho_2]) * 0.11520472029718914;
79 }
80 if (ctr_1 > 0 && ctr_2 > 0) {
81 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 9 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 - _stride_rho_0 + _stride_rho_1 * ctr_1 - _stride_rho_1 + _stride_rho_2 * ctr_2 - _stride_rho_2]) * 0.09406426022938992;
82 }
83 if (ctr_1 > 0 && ctr_2 < _size_j_2 - 1) {
84 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 10 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 - _stride_rho_0 + _stride_rho_1 * ctr_1 - _stride_rho_1 + _stride_rho_2 * ctr_2 + _stride_rho_2]) * 0.09406426022938992;
85 }
86 if (ctr_2 > 0 && ctr_1 < _size_j_1 - 1) {
87 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 11 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 - _stride_rho_0 + _stride_rho_1 * ctr_1 + _stride_rho_1 + _stride_rho_2 * ctr_2 - _stride_rho_2]) * 0.09406426022938992;
88 }
89 if (ctr_1 < _size_j_1 - 1 && ctr_2 < _size_j_2 - 1) {
90 _data_j[_stride_j_0 * ctr_0 + _stride_j_1 * ctr_1 + _stride_j_2 * ctr_2 + 12 * _stride_j_3] = D * (_data_rho[_stride_rho_0 * ctr_0 + _stride_rho_1 * ctr_1 + _stride_rho_2 * ctr_2] - _data_rho[_stride_rho_0 * ctr_0 - _stride_rho_0 + _stride_rho_1 * ctr_1 + _stride_rho_1 + _stride_rho_2 * ctr_2 + _stride_rho_2]) * 0.09406426022938992;
91 }
92 }
93 }
94 }
95}
96} // namespace internal_e5e04d1215f19faa51f3c55db6d456a2
97
98void DiffusiveFluxKernel_double_precision::run(IBlock *block) {
99
100 auto j = block->getData<field::GhostLayerField<double, 13>>(jID);
101 auto rho = block->getData<field::GhostLayerField<double, 1>>(rhoID);
102
103 auto &D = this->D_;
104 WALBERLA_ASSERT_GREATER_EQUAL(-1, -int_c(j->nrOfGhostLayers()))
105 double *RESTRICT const _data_j = j->dataAt(-1, -1, -1, 0);
106 WALBERLA_ASSERT_GREATER_EQUAL(-1, -int_c(rho->nrOfGhostLayers()))
107 double *RESTRICT const _data_rho = rho->dataAt(-1, -1, -1, 0);
108 WALBERLA_ASSERT_GREATER_EQUAL(j->xSizeWithGhostLayer(), int64_t(int64_c(j->xSize()) + 2))
109 const int64_t _size_j_0 = int64_t(int64_c(j->xSize()) + 2);
110 WALBERLA_ASSERT_GREATER_EQUAL(j->ySizeWithGhostLayer(), int64_t(int64_c(j->ySize()) + 2))
111 const int64_t _size_j_1 = int64_t(int64_c(j->ySize()) + 2);
112 WALBERLA_ASSERT_GREATER_EQUAL(j->zSizeWithGhostLayer(), int64_t(int64_c(j->zSize()) + 2))
113 const int64_t _size_j_2 = int64_t(int64_c(j->zSize()) + 2);
114 const int64_t _stride_j_0 = int64_t(j->xStride());
115 const int64_t _stride_j_1 = int64_t(j->yStride());
116 const int64_t _stride_j_2 = int64_t(j->zStride());
117 const int64_t _stride_j_3 = int64_t(1 * int64_t(j->fStride()));
118 const int64_t _stride_rho_0 = int64_t(rho->xStride());
119 const int64_t _stride_rho_1 = int64_t(rho->yStride());
120 const int64_t _stride_rho_2 = int64_t(rho->zStride());
121 internal_e5e04d1215f19faa51f3c55db6d456a2::diffusivefluxkernel_double_precision_diffusivefluxkernel_double_precision(D, _data_j, _data_rho, _size_j_0, _size_j_1, _size_j_2, _stride_j_0, _stride_j_1, _stride_j_2, _stride_j_3, _stride_rho_0, _stride_rho_1, _stride_rho_2);
122}
123
124void DiffusiveFluxKernel_double_precision::runOnCellInterval(const shared_ptr<StructuredBlockStorage> &blocks, const CellInterval &globalCellInterval, cell_idx_t ghostLayers, IBlock *block) {
125
126 CellInterval ci = globalCellInterval;
127 CellInterval blockBB = blocks->getBlockCellBB(*block);
128 blockBB.expand(ghostLayers);
129 ci.intersect(blockBB);
130 blocks->transformGlobalToBlockLocalCellInterval(ci, *block);
131 if (ci.empty())
132 return;
133
134 auto j = block->getData<field::GhostLayerField<double, 13>>(jID);
135 auto rho = block->getData<field::GhostLayerField<double, 1>>(rhoID);
136
137 auto &D = this->D_;
138 WALBERLA_ASSERT_GREATER_EQUAL(ci.xMin() - 1, -int_c(j->nrOfGhostLayers()))
139 WALBERLA_ASSERT_GREATER_EQUAL(ci.yMin() - 1, -int_c(j->nrOfGhostLayers()))
140 WALBERLA_ASSERT_GREATER_EQUAL(ci.zMin() - 1, -int_c(j->nrOfGhostLayers()))
141 double *RESTRICT const _data_j = j->dataAt(ci.xMin() - 1, ci.yMin() - 1, ci.zMin() - 1, 0);
142 WALBERLA_ASSERT_GREATER_EQUAL(ci.xMin() - 1, -int_c(rho->nrOfGhostLayers()))
143 WALBERLA_ASSERT_GREATER_EQUAL(ci.yMin() - 1, -int_c(rho->nrOfGhostLayers()))
144 WALBERLA_ASSERT_GREATER_EQUAL(ci.zMin() - 1, -int_c(rho->nrOfGhostLayers()))
145 double *RESTRICT const _data_rho = rho->dataAt(ci.xMin() - 1, ci.yMin() - 1, ci.zMin() - 1, 0);
146 WALBERLA_ASSERT_GREATER_EQUAL(j->xSizeWithGhostLayer(), int64_t(int64_c(ci.xSize()) + 2))
147 const int64_t _size_j_0 = int64_t(int64_c(ci.xSize()) + 2);
148 WALBERLA_ASSERT_GREATER_EQUAL(j->ySizeWithGhostLayer(), int64_t(int64_c(ci.ySize()) + 2))
149 const int64_t _size_j_1 = int64_t(int64_c(ci.ySize()) + 2);
150 WALBERLA_ASSERT_GREATER_EQUAL(j->zSizeWithGhostLayer(), int64_t(int64_c(ci.zSize()) + 2))
151 const int64_t _size_j_2 = int64_t(int64_c(ci.zSize()) + 2);
152 const int64_t _stride_j_0 = int64_t(j->xStride());
153 const int64_t _stride_j_1 = int64_t(j->yStride());
154 const int64_t _stride_j_2 = int64_t(j->zStride());
155 const int64_t _stride_j_3 = int64_t(1 * int64_t(j->fStride()));
156 const int64_t _stride_rho_0 = int64_t(rho->xStride());
157 const int64_t _stride_rho_1 = int64_t(rho->yStride());
158 const int64_t _stride_rho_2 = int64_t(rho->zStride());
159 internal_e5e04d1215f19faa51f3c55db6d456a2::diffusivefluxkernel_double_precision_diffusivefluxkernel_double_precision(D, _data_j, _data_rho, _size_j_0, _size_j_1, _size_j_2, _stride_j_0, _stride_j_1, _stride_j_2, _stride_j_3, _stride_rho_0, _stride_rho_1, _stride_rho_2);
160}
161
162} // namespace pystencils
163} // namespace walberla
164
165#if (defined WALBERLA_CXX_COMPILER_IS_GNU) || (defined WALBERLA_CXX_COMPILER_IS_CLANG)
166#pragma GCC diagnostic pop
167#endif
168
169#if (defined WALBERLA_CXX_COMPILER_IS_INTEL)
170#pragma warning pop
171#endif
FUNC_PREFIX
#define FUNC_PREFIX
\file AdvectiveFluxKernel_double_precision.cpp \author pystencils
Definition AdvectiveFluxKernel_double_precision.cpp:28
RESTRICT
#define RESTRICT
\file AdvectiveFluxKernel_double_precision.h \author pystencils
Definition AdvectiveFluxKernel_double_precision.h:42
DiffusiveFluxKernel_double_precision.h
walberla::pystencils::DiffusiveFluxKernel_double_precision::runOnCellInterval
void runOnCellInterval(const shared_ptr< StructuredBlockStorage > &blocks, const CellInterval &globalCellInterval, cell_idx_t ghostLayers, IBlock *block)
Definition DiffusiveFluxKernel_double_precision.cpp:124
walberla::pystencils::DiffusiveFluxKernel_double_precision::run
void run(IBlock *block)
Definition DiffusiveFluxKernel_double_precision.cpp:98
block
static double * block(double *p, std::size_t index, std::size_t size)
Definition elc.cpp:176
std
STL namespace.
walberla::pystencils::internal_e5e04d1215f19faa51f3c55db6d456a2::diffusivefluxkernel_double_precision_diffusivefluxkernel_double_precision
static FUNC_PREFIX void diffusivefluxkernel_double_precision_diffusivefluxkernel_double_precision(double D, double *RESTRICT const _data_j, double *RESTRICT const _data_rho, int64_t const _size_j_0, int64_t const _size_j_1, int64_t const _size_j_2, int64_t const _stride_j_0, int64_t const _stride_j_1, int64_t const _stride_j_2, int64_t const _stride_j_3, int64_t const _stride_rho_0, int64_t const _stride_rho_1, int64_t const _stride_rho_2)
Definition DiffusiveFluxKernel_double_precision.cpp:49
walberla
\file PackInfoPdfDoublePrecision.cpp \author pystencils
Definition EKWalberla.hpp:36