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https://gitlab.kitware.com/vtk/vtk-m
synced 2024-09-19 18:45:43 +00:00
4bf8bfb1fa
The implementation of the search in the k-d tree is problematic because it uses unbounded recursion. This is a problem for GPU devices, which have very short stacks set by how many calls the compiler determines. This is fixable, but the fix is not trivial. This class is not used anywhere in VTK-m other than a trivial test. Thus, I am just deprecating the class. I am also deleting the test, so the code is not run anymore.
78 lines
3.0 KiB
C++
78 lines
3.0 KiB
C++
//============================================================================
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// Copyright (c) Kitware, Inc.
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// All rights reserved.
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// See LICENSE.txt for details.
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//
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// This software is distributed WITHOUT ANY WARRANTY; without even
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// the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
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// PURPOSE. See the above copyright notice for more information.
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//============================================================================
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#ifndef vtkm_m_worklet_KdTree3D_h
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#define vtkm_m_worklet_KdTree3D_h
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#include <vtkm/worklet/spatialstructure/KdTree3DConstruction.h>
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#include <vtkm/worklet/spatialstructure/KdTree3DNNSearch.h>
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namespace vtkm
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{
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namespace worklet
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{
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class VTKM_DEPRECATED(1.7,
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"K-D tree recursive searches are not well supported on GPU devices.") KdTree3D
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{
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public:
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KdTree3D() = default;
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/// \brief Construct a 3D KD-tree for 3D point positions.
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///
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/// \tparam CoordType type of the x, y, z component of the point coordinates.
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/// \tparam CoordStorageTag
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/// \param coords An ArrayHandle of x, y, z coordinates of input points.
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///
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template <typename CoordType, typename CoordStorageTag>
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void Build(const vtkm::cont::ArrayHandle<vtkm::Vec<CoordType, 3>, CoordStorageTag>& coords)
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{
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vtkm::worklet::spatialstructure::KdTree3DConstruction().Run(
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coords, this->PointIds, this->SplitIds);
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}
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/// \brief Nearest neighbor search using KD-Tree
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///
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/// Parallel search of nearest neighbor for each point in the \c queryPoints in the the set of
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/// \c coords. Returns nearest neighbor in \c nearestNeighborId and distance to nearest neighbor
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/// in \c distances.
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///
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/// \tparam CoordType
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/// \tparam CoordStorageTag1
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/// \tparam CoordStorageTag2
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/// \tparam DeviceAdapter
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/// \param coords Point coordinates for training data set (haystack)
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/// \param queryPoints Point coordinates to query for nearest neighbor (needles).
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/// \param nearestNeighborIds Nearest neighbor in the traning data set for each points in the
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/// testing set
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/// \param distances Distances between query points and their nearest neighbors.
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/// \param deviceId Tag for selecting device adapter.
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template <typename CoordType,
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typename CoordStorageTag1,
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typename CoordStorageTag2,
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typename DeviceAdapter>
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void Run(const vtkm::cont::ArrayHandle<vtkm::Vec<CoordType, 3>, CoordStorageTag1>& coords,
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const vtkm::cont::ArrayHandle<vtkm::Vec<CoordType, 3>, CoordStorageTag2>& queryPoints,
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vtkm::cont::ArrayHandle<vtkm::Id>& nearestNeighborIds,
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vtkm::cont::ArrayHandle<CoordType>& distances,
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DeviceAdapter deviceId)
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{
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vtkm::worklet::spatialstructure::KdTree3DNNSearch().Run(
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coords, this->PointIds, this->SplitIds, queryPoints, nearestNeighborIds, distances, deviceId);
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}
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private:
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vtkm::cont::ArrayHandle<vtkm::Id> PointIds;
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vtkm::cont::ArrayHandle<vtkm::Id> SplitIds;
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};
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}
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} // namespace vtkm::worklet
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#endif // vtkm_m_worklet_Kdtree3D_h
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