DiffusiveFluxKernel_single_precision.cpp

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//======================================================================================================================
//
//  This file is part of waLBerla. waLBerla is free software: you can
//  redistribute it and/or modify it under the terms of the GNU General Public
//  License as published by the Free Software Foundation, either version 3 of
//  the License, or (at your option) any later version.
//
//  waLBerla is distributed in the hope that it will be useful, but WITHOUT
//  ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
//  FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
//  for more details.
//
//  You should have received a copy of the GNU General Public License along
//  with waLBerla (see COPYING.txt). If not, see <http://www.gnu.org/licenses/>.
//
//! \\file DiffusiveFluxKernel_single_precision.cpp
//! \\author pystencils
//======================================================================================================================
 
// kernel generated with pystencils v1.4+1.ge851f4e, lbmpy v1.4+1.ge9efe34, sympy v1.12.1, lbmpy_walberla/pystencils_walberla from waLBerla commit 007e77e077ad9d22b5eed6f3d3118240993e553c
 
#include <cmath>
 
#include "DiffusiveFluxKernel_single_precision.h"
#include "core/DataTypes.h"
#include "core/Macros.h"
 
#define FUNC_PREFIX
 
#if (defined WALBERLA_CXX_COMPILER_IS_GNU) || (defined WALBERLA_CXX_COMPILER_IS_CLANG)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wfloat-equal"
#pragma GCC diagnostic ignored "-Wshadow"
#pragma GCC diagnostic ignored "-Wconversion"
#pragma GCC diagnostic ignored "-Wunused-variable"
#endif
 
#if (defined WALBERLA_CXX_COMPILER_IS_INTEL)
#pragma warning push
#pragma warning(disable : 1599)
#endif
 
using namespace std;
 
namespace walberla {
namespace pystencils {
 
namespace internal_2fab63cfdbacde4ac630f257442231a8 {
static FUNC_PREFIX void diffusivefluxkernel_single_precision_diffusivefluxkernel_single_precision(float D, float *RESTRICT const _data_j, float *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) {
  for (int64_t ctr_2 = 0; ctr_2 < _size_j_2; ctr_2 += 1) {
    for (int64_t ctr_1 = 0; ctr_1 < _size_j_1; ctr_1 += 1) {
      for (int64_t ctr_0 = 1; ctr_0 < _size_j_0; ctr_0 += 1) {
        if (ctr_1 > 0 && ctr_2 > 0 && ctr_1 < _size_j_1 - 1 && ctr_2 < _size_j_2 - 1) {
          _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]) * 0.16292407789368385f;
        }
        if (ctr_1 > 0 && ctr_2 > 0 && ctr_0 < _size_j_0 - 1 && ctr_2 < _size_j_2 - 1) {
          _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]) * 0.16292407789368385f;
        }
        if (ctr_1 > 0 && ctr_2 > 0 && ctr_0 < _size_j_0 - 1 && ctr_1 < _size_j_1 - 1) {
          _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]) * 0.16292407789368385f;
        }
        if (ctr_1 > 0 && ctr_2 > 0 && ctr_2 < _size_j_2 - 1) {
          _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]) * 0.11520472029718914f;
        }
        if (ctr_2 > 0 && ctr_1 < _size_j_1 - 1 && ctr_2 < _size_j_2 - 1) {
          _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]) * 0.11520472029718914f;
        }
        if (ctr_1 > 0 && ctr_2 > 0 && ctr_1 < _size_j_1 - 1) {
          _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]) * 0.11520472029718914f;
        }
        if (ctr_1 > 0 && ctr_1 < _size_j_1 - 1 && ctr_2 < _size_j_2 - 1) {
          _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]) * 0.11520472029718914f;
        }
        if (ctr_1 > 0 && ctr_2 > 0 && ctr_0 < _size_j_0 - 1) {
          _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]) * 0.11520472029718914f;
        }
        if (ctr_1 > 0 && ctr_0 < _size_j_0 - 1 && ctr_2 < _size_j_2 - 1) {
          _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]) * 0.11520472029718914f;
        }
        if (ctr_1 > 0 && ctr_2 > 0) {
          _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]) * 0.09406426022938992f;
        }
        if (ctr_1 > 0 && ctr_2 < _size_j_2 - 1) {
          _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]) * 0.09406426022938992f;
        }
        if (ctr_2 > 0 && ctr_1 < _size_j_1 - 1) {
          _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]) * 0.09406426022938992f;
        }
        if (ctr_1 < _size_j_1 - 1 && ctr_2 < _size_j_2 - 1) {
          _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]) * 0.09406426022938992f;
        }
      }
    }
  }
}
} // namespace internal_2fab63cfdbacde4ac630f257442231a8
 
void DiffusiveFluxKernel_single_precision::run(IBlock *block) {
 
  auto j = block->getData<field::GhostLayerField<float, 13>>(jID);
  auto rho = block->getData<field::GhostLayerField<float, 1>>(rhoID);
 
  auto &D = this->D_;
  WALBERLA_ASSERT_GREATER_EQUAL(-1, -int_c(j->nrOfGhostLayers()))
  float *RESTRICT const _data_j = j->dataAt(-1, -1, -1, 0);
  WALBERLA_ASSERT_GREATER_EQUAL(-1, -int_c(rho->nrOfGhostLayers()))
  float *RESTRICT const _data_rho = rho->dataAt(-1, -1, -1, 0);
  WALBERLA_ASSERT_GREATER_EQUAL(j->xSizeWithGhostLayer(), int64_t(int64_c(j->xSize()) + 2))
  const int64_t _size_j_0 = int64_t(int64_c(j->xSize()) + 2);
  WALBERLA_ASSERT_GREATER_EQUAL(j->ySizeWithGhostLayer(), int64_t(int64_c(j->ySize()) + 2))
  const int64_t _size_j_1 = int64_t(int64_c(j->ySize()) + 2);
  WALBERLA_ASSERT_GREATER_EQUAL(j->zSizeWithGhostLayer(), int64_t(int64_c(j->zSize()) + 2))
  const int64_t _size_j_2 = int64_t(int64_c(j->zSize()) + 2);
  const int64_t _stride_j_0 = int64_t(j->xStride());
  const int64_t _stride_j_1 = int64_t(j->yStride());
  const int64_t _stride_j_2 = int64_t(j->zStride());
  const int64_t _stride_j_3 = int64_t(1 * int64_t(j->fStride()));
  const int64_t _stride_rho_0 = int64_t(rho->xStride());
  const int64_t _stride_rho_1 = int64_t(rho->yStride());
  const int64_t _stride_rho_2 = int64_t(rho->zStride());
  internal_2fab63cfdbacde4ac630f257442231a8::diffusivefluxkernel_single_precision_diffusivefluxkernel_single_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);
}
 
void DiffusiveFluxKernel_single_precision::runOnCellInterval(const shared_ptr<StructuredBlockStorage> &blocks, const CellInterval &globalCellInterval, cell_idx_t ghostLayers, IBlock *block) {
 
  CellInterval ci = globalCellInterval;
  CellInterval blockBB = blocks->getBlockCellBB(*block);
  blockBB.expand(ghostLayers);
  ci.intersect(blockBB);
  blocks->transformGlobalToBlockLocalCellInterval(ci, *block);
  if (ci.empty())
    return;
 
  auto j = block->getData<field::GhostLayerField<float, 13>>(jID);
  auto rho = block->getData<field::GhostLayerField<float, 1>>(rhoID);
 
  auto &D = this->D_;
  WALBERLA_ASSERT_GREATER_EQUAL(ci.xMin() - 1, -int_c(j->nrOfGhostLayers()))
  WALBERLA_ASSERT_GREATER_EQUAL(ci.yMin() - 1, -int_c(j->nrOfGhostLayers()))
  WALBERLA_ASSERT_GREATER_EQUAL(ci.zMin() - 1, -int_c(j->nrOfGhostLayers()))
  float *RESTRICT const _data_j = j->dataAt(ci.xMin() - 1, ci.yMin() - 1, ci.zMin() - 1, 0);
  WALBERLA_ASSERT_GREATER_EQUAL(ci.xMin() - 1, -int_c(rho->nrOfGhostLayers()))
  WALBERLA_ASSERT_GREATER_EQUAL(ci.yMin() - 1, -int_c(rho->nrOfGhostLayers()))
  WALBERLA_ASSERT_GREATER_EQUAL(ci.zMin() - 1, -int_c(rho->nrOfGhostLayers()))
  float *RESTRICT const _data_rho = rho->dataAt(ci.xMin() - 1, ci.yMin() - 1, ci.zMin() - 1, 0);
  WALBERLA_ASSERT_GREATER_EQUAL(j->xSizeWithGhostLayer(), int64_t(int64_c(ci.xSize()) + 2))
  const int64_t _size_j_0 = int64_t(int64_c(ci.xSize()) + 2);
  WALBERLA_ASSERT_GREATER_EQUAL(j->ySizeWithGhostLayer(), int64_t(int64_c(ci.ySize()) + 2))
  const int64_t _size_j_1 = int64_t(int64_c(ci.ySize()) + 2);
  WALBERLA_ASSERT_GREATER_EQUAL(j->zSizeWithGhostLayer(), int64_t(int64_c(ci.zSize()) + 2))
  const int64_t _size_j_2 = int64_t(int64_c(ci.zSize()) + 2);
  const int64_t _stride_j_0 = int64_t(j->xStride());
  const int64_t _stride_j_1 = int64_t(j->yStride());
  const int64_t _stride_j_2 = int64_t(j->zStride());
  const int64_t _stride_j_3 = int64_t(1 * int64_t(j->fStride()));
  const int64_t _stride_rho_0 = int64_t(rho->xStride());
  const int64_t _stride_rho_1 = int64_t(rho->yStride());
  const int64_t _stride_rho_2 = int64_t(rho->zStride());
  internal_2fab63cfdbacde4ac630f257442231a8::diffusivefluxkernel_single_precision_diffusivefluxkernel_single_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);
}
 
} // namespace pystencils
} // namespace walberla
 
#if (defined WALBERLA_CXX_COMPILER_IS_GNU) || (defined WALBERLA_CXX_COMPILER_IS_CLANG)
#pragma GCC diagnostic pop
#endif
 
#if (defined WALBERLA_CXX_COMPILER_IS_INTEL)
#pragma warning pop
#endif