CGAL 6.2 - Spatial Sorting

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User Manual

Authors
  Christophe Delage and Olivier Devillers

# Introduction

Many geometric algorithms implemented in CGAL are incremental, and thus their speed is dependent on the order of insertion. This package provides sorting algorithms that may considerably improve running times of such algorithms.

The rationale is to sort objects along a space-filling curve so that two objects close geometrically will be close in the insertion order with high probability. That way, parts of a data structure that will be looked at during an insertion will probably have been looked at in a recent insertion, and thus probably will be in cache memory instead of main memory. As another side-effect, these sorting functions usually improve memory locality of the data structures produced by incremental algorithms, sometimes leading to speed ups in other algorithm using these data structures.

Some algorithms have a good complexity under randomized hypotheses which contradicts the idea of sorting the input using any sorting criterion. In such a case, it is possible to introduce just a bit of randomness to be able to combine the good randomized complexity and the good effects of locality [1].

The predicates used by this package are comparisons between coordinates, thus there is no robustness issue involved here, for example to choose the arithmetic of the kernel.

# Hilbert Sorting

In 2D, one can construct a space filling curve, that is a mapping \( f\) of \( [0,1]\) to the unit square \( [0,1]^2\), such that \( f(0)=(0,0)\) and \( f(1)=(1,0)\) in the following way: the unit square is subdivided in four such that

  \( f([0,\frac{1}{4}])=[0,\frac{1}{2}]^2\), \( f([\frac{1}{4},\frac{1}{2}])=[0,\frac{1}{2}]\times[\frac{1}{2},1]\), \( f([\frac{1}{2},\frac{3}{4}])=[\frac{1}{2},1]^2\), and \( f([\frac{3}{4},1])=[\frac{1}{2},1]\times[0,\frac{1}{2}].\)
  \( f(\frac{1}{4})=(0,\frac{1}{2})\), \( f(\frac{1}{2})=(\frac{1}{2},\frac{1}{2})\), and \( f(\frac{3}{4})=(1,\frac{1}{2})\).

Then each square is subdivided in the same way recursively. Figure 105.1 illustrates this process.

Figure 105.1 Hilbert mapping

Now given a set of 2D points, they can be sorted in the order they have on such a space filling curve. Note that at each step, we split a square exactly at its center; we call this subdivision policy: middle policy (see Figure 105.2).

Figure 105.2 Hilbert sort with middle policy

If instead of subdividing the square in a fixed way at its center, as above, we subdivide it by splitting at the median point (in \( x\) or \( y\) directions alternating), we construct a 2-d tree adapted to the point set. This tree can be visited in a similar manner and we get also a suitable ordering of the points; we call this subdivision policy: median policy (see Figure 105.3).

Figure 105.3 Hilbert sort with median policy

The middle policy is easier to analyze, and is interesting in practice for well distributed set of points in small dimension (if the number of points is really larger than \( 2^d\)). The median policy should be preferred for high dimension or if the point set distribution is not regular (or unknown). Since the median policy cannot be much worse than the middle policy, while the converse can happen, the median policy is the default behavior. Most theoretical results are using the middle policy [1], [2], [3], [4].

CGAL provides Hilbert sorting for points in 2D, 3D and higher dimensions, in the middle and the median policies.

We also consider space filling curves on a given sphere. The method is described for the unit sphere below; it works on any sphere by an affine transformation. The points to be sorted are supposed to be close to the sphere.

Actually, we approximate a space filling curve on the unit sphere by a space filling curve on a cube (with facets at \(x, y, z = \pm 1/\sqrt{3}\)). Roughly speaking, we split the original set of points in six subsets corresponding to the six facets of the cube. The subset corresponding to a facet \(f\) is the set of points that lie in the halfspace defined by the supporting plane of \(f\) that does not contain the origin. And then we basically use the 2D Hilbert sort with its corresponding policy, as explained above for the projection of the points in each subset on its corresponding facet of the cube. The axes orientation on each facet is chosen so that the space filling curve covers the whole cube without any jump; see Figure 105.4. A point can lie in more than one such halfplane, so, we give a priority for each facet of the cube. The priority order is: first, the facet of the cube at \(x = 1/\sqrt{3}\); second, the facet of the cube at \(y = 1/\sqrt{3}\); third, the facet of the cube at \( x = -1/\sqrt{3}\); fourth, the facet of the cube at \( z = 1/\sqrt{3}\); fifth, the facet of the cube at \( y = -1/\sqrt{3}\); and, sixth, the facet of the cube at \( z = -1/\sqrt{3}\).

If points are not close to the sphere, they are still sorted the same way, however there is no guarantee that such an order is good anymore.

Figure 105.4 A 2D Hilbert sort for each facet of the cube

Points sorted on the sphere according to the description above are depicted in Figure 105.5.

Figure 105.5 Hilbert sort on the sphere

## Examples

The code to use Hilbert sort is as simple as the following example:

Example: Spatial_sorting/hilbert.cpp

Show / Hide
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/point_generators_2.h>
#include <CGAL/hilbert_sort.h>
#include <iostream>
#include <vector>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef K::Point_2 Point;
typedef CGAL::Creator_uniform_2<double,Point> Creator;
int main ()
{
std::size_t size = 16;
std::vector<Point> v; v.reserve(size);
CGAL::points_on_square_grid_2(3.0, size, // generate points
std::back_inserter(v), Creator());
CGAL::hilbert_sort (v.begin(), v.end()); // sort
for(std::size_t i=0; i<size; ++i)std::cout<<v[i]<<std::endl;//output
return 0;
}
CGAL::Creator_uniform_2
CGAL::Exact_predicates_inexact_constructions_kernel
CGAL::hilbert_sort
void hilbert_sort(InputPointIterator begin, InputPointIterator end, const Traits &traits=Default_traits, PolicyTag policy=Default_policy)
The function hilbert_sort() sorts an iterator range of points along a Hilbert curve.

The following example shows how to perform a Hilbert sort on a sphere.

Example: Spatial_sorting/hilbert_sort_on_sphere.cpp

Show / Hide
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/point_generators_3.h>
#include <CGAL/hilbert_sort_on_sphere.h>
#include <iostream>
#include <vector>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef K::Point_3 Point;
typedef K::Vector_3 Vector;
typedef K::Sphere_3 Sphere;
typedef CGAL::Creator_uniform_3<double,Point> Creator_3;
int main ()
{
std::size_t size = 32;
CGAL::Random random (42);
std::vector<Point> v;
// unit sphere
std::cout << "UNIT SPHERE: " << std::endl;
v.reserve(size);
CGAL::Random_points_on_sphere_3<Point> unit_sphere(1.0, random); // generate points
for (std::size_t i = 0; i < size; ++i) v.push_back(*unit_sphere++);
CGAL::hilbert_sort_on_sphere(v.begin(), v.end()); // sort
for(std::size_t i=0; i<size; ++i) std::cout << v[i] << std::endl; //output
v.clear();
// given sphere
std::cout << "GIVEN SPHERE: " << std::endl;
v.reserve(size);
Vector trans = Vector(3,4,5);
Sphere sphere = Sphere(CGAL::ORIGIN + trans, 4);
CGAL::Random_points_on_sphere_3<Point> given_sphere(2.0, random); // generate points
for (std::size_t i = 0; i < size; ++i) v.push_back(*given_sphere++ + trans);
CGAL::hilbert_sort_on_sphere(v.begin(), v.end(), // sort
sphere.squared_radius(), sphere.center());
for(std::size_t i=0; i<size; ++i) std::cout << v[i] << std::endl; //output
return 0;
}
CGAL::Creator_uniform_3
CGAL::hilbert_sort_on_sphere
void hilbert_sort_on_sphere(InputPointIterator begin, InputPointIterator end, const Traits &traits=Default_traits, PolicyTag policy=Default_policy, double sqr_radius=1.0, const Traits::Point_3 &center=Default_center)
The function hilbert_sort_on_sphere() sorts an iterator range of points that are supposed to be close...
CGAL::ORIGIN
const CGAL::Origin ORIGIN

This other example illustrates the use of the two different policies

Example: Spatial_sorting/hilbert_policies.cpp

Show / Hide
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/hilbert_sort.h>
#include <iostream>
#include <vector>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef K::Point_2 Point;
int main ()
{
std::vector<Point> v; v.reserve(4);
v.push_back( Point(0.0,0.0)) ;
v.push_back( Point(1.0,1.0)) ;
v.push_back( Point(0.1,0.1)) ;
v.push_back( Point(0.2,0.8)) ;
std::cout << "Hilbert sort (middle policy)." << std::endl;
CGAL::hilbert_sort (v.begin(), v.end(), K(), CGAL::Hilbert_sort_middle_policy());
std::cout<<v[0]<<"; "<<v[1]<<"; "<<v[2]<<"; "<<v[3]<<"; "<<std::endl;
std::cout << "Hilbert sort (median policy)." << std::endl;
CGAL::hilbert_sort (v.begin(), v.end(), K(), CGAL::Hilbert_sort_median_policy());
std::cout<<v[0]<<"; "<<v[1]<<"; "<<v[2]<<"; "<<v[3]<<"; "<<std::endl;
return 0;
}
CGAL::Hilbert_policy
Hilbert_policy is a policy class which can be used to parameterize a strategy policy in order to spec...
Definition: Hilbert_policy_tags.h:56

# Spatial Sorting

Hilbert sort cannot be used directly before feeding a randomized algorithm. Thus, the trick is to organize the point set in random buckets of increasing sizes, Hilbert sort being used only inside a bucket.

It has been proved, in the context of Delaunay triangulation, that such an order provides enough randomness to combine the advantages of a random order and a space filling curve order [1].

CGAL provides spatial sorting for points in 2D, 3D and higher dimensions, with the middle and the median policies for Hilbert sort in the buckets.

## Basic Example

The following example shows that, on particular input, spatial sort runs much faster than a bad order or than Hilbert sort (below results with release mode compilation on a 1.8GHz processor).

$ ./small_example_delaunay_2
10000 points on a parabola
Delaunay without spatial sort... done in 6.33443 seconds.
Delaunay with median hilbert sort... done in 0.822975 seconds.
Delaunay with median spatial sort... done in 0.022415 seconds.

Example: Spatial_sorting/small_example_delaunay_2.cpp

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#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Delaunay_triangulation_2.h>
#include <CGAL/spatial_sort.h>
#include <CGAL/Timer.h>
#include <iostream>
#include <vector>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef CGAL::Delaunay_triangulation_2<K> DT;
void compute_delaunay(std::vector<K::Point_2>::iterator it,
std::vector<K::Point_2>::iterator e){
DT dt;
DT::Face_handle hint;
for( ;it!=e; ++it) hint = dt.insert(*it, hint)->face();
}
int main ()
{ int size = 1000;
std::vector<K::Point_2> v;
v.reserve(size);
CGAL::Timer cost;
std::cout <<size<< " points on a parabola" << std::endl;
for (int i=0; i< size; ++i) {
double x= -size +i;
v.push_back( K::Point_2( x, x*x ));
}
cost.reset();cost.start();
std::cout << " Delaunay without spatial sort... "<< std::flush;
compute_delaunay(v.begin(),v.end());cost.stop();
std::cout << "done in "<<cost.time()<<" seconds." << std::endl;
cost.reset();cost.start();
std::cout << " Delaunay with Hilbert sort... " << std::flush;
CGAL::hilbert_sort(v.begin(),v.end());
compute_delaunay(v.begin(),v.end());cost.stop();
std::cout << "done in "<<cost.time()<<" seconds." << std::endl;
cost.reset();cost.start();
std::cout << " Delaunay with spatial sort... " << std::flush;
CGAL::spatial_sort(v.begin(),v.end());
compute_delaunay(v.begin(),v.end());cost.stop();
std::cout << "done in "<<cost.time()<<" seconds." << std::endl;
return 0;
}
CGAL::spatial_sort
void spatial_sort(InputPointIterator begin, InputPointIterator end, const Traits &traits=Default_traits, PolicyTag policy=Default_policy, std::ptrdiff_t threshold_hilbert=default, std::ptrdiff_t threshold_multiscale=default, double ratio=default)
The function spatial_sort() sorts an iterator range of points in a way that improves space locality.

Spatial sort can be performed on the sphere as well, as shown in the following example.

Example: Spatial_sorting/spatial_sort_on_sphere.cpp

Show / Hide
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/point_generators_3.h>
#include <CGAL/spatial_sort_on_sphere.h>
#include <iostream>
#include <vector>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef K::Point_3 Point;
typedef K::Vector_3 Vector;
typedef K::Sphere_3 Sphere;
typedef CGAL::Creator_uniform_3<double,Point> Creator_3;
int main ()
{
std::size_t size = 32;
CGAL::Random random (42);
std::vector<Point> v;
// unit sphere
std::cout << "UNIT SPHERE: " << std::endl;
v.reserve(size);
CGAL::Random_points_on_sphere_3<Point> unit_sphere(1.0, random); // generate points
for (std::size_t i = 0; i < size; ++i) v.push_back(*unit_sphere++);
CGAL::spatial_sort_on_sphere(v.begin(), v.end()); // sort
for(std::size_t i=0; i<size; ++i) std::cout << v[i] << std::endl; //output
v.clear();
// given sphere
std::cout << "GIVEN SPHERE: " << std::endl;
v.reserve(size);
Vector trans = Vector(3,4,5);
Sphere sphere = Sphere(CGAL::ORIGIN + trans, 4);
CGAL::Random_points_on_sphere_3<Point> given_sphere(2.0, random); // generate points
for (std::size_t i = 0; i < size; ++i) v.push_back(*given_sphere++ + trans);
CGAL::spatial_sort_on_sphere(v.begin(), v.end(), // sort
sphere.squared_radius(), sphere.center());
for(std::size_t i=0; i<size; ++i) std::cout << v[i] << std::endl; //output
return 0;
}
CGAL::spatial_sort_on_sphere
void spatial_sort_on_sphere(InputPointIterator begin, InputPointIterator end, const Traits &traits=Default_traits, PolicyTag policy=Default_policy, double sqr_radius=1.0, const Traits::Point_3 &center=Default_center, std::ptrdiff_t threshold_hilbert=default, std::ptrdiff_t threshold_multiscale=default, double ratio=default)
The function spatial_sort_on_sphere() sorts an iterator range of points in a way that improves space ...

## Using Your Own Point Type

If you want to sort points of your own point type, you only have to provide functors that compare the `x` and `y` coordinates of your points. Note that in case you simply want to associate an extra information to your point you might consider the example of Section Sorting Using Pairs of Points and Integers as an alternative.

Example: Spatial_sorting/myPoint.cpp

Show / Hide
#include <CGAL/spatial_sort.h>
struct MyPoint {
double x,y;
int color;
MyPoint()
: x(0), y(0),color(0)
{}
MyPoint(double x, double y, int color=0)
: x(x), y(y), color(color)
{}
};
struct MyLessX {
bool operator()(const MyPoint& p, const MyPoint& q) const
{
return p.x < q.x;
}
};
struct MyLessY {
bool operator()(const MyPoint& p, const MyPoint& q) const
{
return p.y < q.y;
}
};
struct MySpatialSortingTraits {
typedef MyPoint Point_2;
typedef MyLessX Less_x_2;
typedef MyLessY Less_y_2;
Less_x_2 less_x_2_object() const
{
return Less_x_2();
}
Less_y_2 less_y_2_object() const
{
return Less_y_2();
}
};
int main()
{
std::vector< MyPoint > points;
points.push_back(MyPoint(14,12, 3));
points.push_back(MyPoint(1,2 , 0));
points.push_back(MyPoint(414,2, 5));
points.push_back(MyPoint(4,21 , 1));
points.push_back(MyPoint(7,74 , 2));
points.push_back(MyPoint(74,4 , 4));
MySpatialSortingTraits sst;
CGAL::spatial_sort(points.begin(), points.end(), sst);
for (std::vector< MyPoint >::iterator it=points.begin();it!=points.end();++it)
std::cout << it->color << " ";
std::cout << "\n";
std::cerr << "done" << std::endl;
return 0;
}

## Sorting Arbitrary Types

The spatial sorting traits class provides a point type and functors for comparing, for example, the `x`-coordinates of two points. If you want to sort something else than just points, for example a sequence of tuples containing a point, or a sequence of indices in a vector of points, you need another level of indirection. We provide the spatial sorting traits class adapters which are templated by another spatial sorting traits class, and a property map. which allows to obtain a point from whatever you want to sort.

The following examples illustrate the usage of these traits class adapters.

## Sorting Using Pairs of Points and Integers

In this example program, the sorted sequence of points is retrieved using a vector of pairs of points and integers. Example: Spatial_sorting/sp_sort_using_property_map_2.cpp

Show / Hide
#include <CGAL/Simple_cartesian.h>
#include <CGAL/spatial_sort.h>
#include <CGAL/Spatial_sort_traits_adapter_2.h>
#include <CGAL/property_map.h>
#include <vector>
typedef CGAL::Simple_cartesian<double> Kernel;
typedef Kernel::Point_2 Point_2;
typedef std::pair<Point_2,int> Point_with_info;
typedef std::vector< Point_with_info > Data_vector;
typedef CGAL::Spatial_sort_traits_adapter_2<Kernel,
CGAL::First_of_pair_property_map<Point_with_info>
> Search_traits_2;
int main()
{
Data_vector points;
points.push_back(std::make_pair(Point_2(14,12) , 3));
points.push_back(std::make_pair(Point_2(1,2) , 0));
points.push_back(std::make_pair(Point_2(414,2) , 5));
points.push_back(std::make_pair(Point_2(4,21) , 1));
points.push_back(std::make_pair(Point_2(7,74) , 2));
points.push_back(std::make_pair(Point_2(74,4) , 4));
Search_traits_2 traits;
std::cout << "Order using default policy (median)\n";
CGAL::spatial_sort(points.begin(), points.end(), traits);
for (Data_vector::iterator it=points.begin();it!=points.end();++it)
std::cout << it->second << " ";
std::cout << "\n";
std::cout << "Order using middle policy\n";
CGAL::spatial_sort(points.begin(), points.end(), traits, CGAL::Hilbert_sort_middle_policy());
for (Data_vector::iterator it=points.begin();it!=points.end();++it)
std::cout << it->second << " ";
std::cout << "\n";
std::cout << "done" << std::endl;
return 0;
}
CGAL::Spatial_sort_traits_adapter_2
Given a property map associating a key to a point, the class Spatial_sort_traits_adapter_2 induces a ...
Definition: Spatial_sort_traits_adapter_2.h:19
Kernel::Point_2
Kernel
CGAL::Simple_cartesian

## Sorting Using Indices of Points

In this example program, the sorted sequence of points is retrieved using the indices of the points in a vector of points. Example: Spatial_sorting/sp_sort_using_property_map_3.cpp

Show / Hide
#include <CGAL/Simple_cartesian.h>
#include <CGAL/spatial_sort.h>
#include <CGAL/Spatial_sort_traits_adapter_3.h>
#include <vector>
#include <CGAL/boost/iterator/counting_iterator.hpp>
typedef CGAL::Simple_cartesian<double> Kernel;
typedef Kernel::Point_3 Point_3;
typedef CGAL::Spatial_sort_traits_adapter_3<Kernel,
CGAL::Pointer_property_map<Point_3>::type > Search_traits_3;
int main()
{
std::vector<Point_3> points;
points.push_back(Point_3(1,3,11));
points.push_back(Point_3(14,34,46));
points.push_back(Point_3(414,34,4));
points.push_back(Point_3(4,2,56));
points.push_back(Point_3(744,4154,43));
points.push_back(Point_3(74,44,1));
std::vector<std::size_t> indices;
indices.reserve(points.size());
std::copy(boost::counting_iterator<std::size_t>(0),
boost::counting_iterator<std::size_t>(points.size()),
std::back_inserter(indices));
std::cout << "Order using default policy (median)\n";
CGAL::spatial_sort( indices.begin(),
indices.end(),
Search_traits_3(CGAL::make_property_map(points)) );
for (std::size_t i : indices)
std::cout << points[i] << "\n";
std::cout << "Order using middle policy\n";
CGAL::spatial_sort( indices.begin(),
indices.end(),
Search_traits_3(CGAL::make_property_map(points)),
CGAL::Hilbert_sort_middle_policy());
for (std::size_t i : indices)
std::cout << points[i] << "\n";
std::cout << "done" << std::endl;
return 0;
}
CGAL::Spatial_sort_traits_adapter_3
Given a property map associating a key to a point, the class Spatial_sort_traits_adapter_3 induces a ...
Definition: Spatial_sort_traits_adapter_3.h:20
Kernel::Point_3

## Sorting Using Indices of Pairs of Points and Integers

In this example program, the sorted sequence of points is retrieved using the indices of the points in a vector of pairs of points and integers. Example: Spatial_sorting/sp_sort_using_property_map_d.cpp

Show / Hide
#include <CGAL/Cartesian_d.h>
#include <CGAL/spatial_sort.h>
#include <CGAL/Spatial_sort_traits_adapter_d.h>
#include <CGAL/boost/iterator/counting_iterator.hpp>
#include <vector>
typedef CGAL::Cartesian_d<double> Kernel;
typedef Kernel::Point_d Point_d;
typedef std::pair<Point_d,int> Point_with_info;
typedef std::vector< Point_with_info > Data_vector;
//property map and get as friend
// to be allowed to use private member
class Vect_ppmap{
const Data_vector& points;
public:
//classical typedefs
typedef Data_vector::size_type key_type;
typedef Point_d value_type;
typedef const value_type& reference;
typedef boost::readable_property_map_tag category;
Vect_ppmap(const Data_vector& points_):points(points_){}
friend reference get(const Vect_ppmap& vmap, key_type i) {
return vmap.points[i].first;
}
};
typedef CGAL::Spatial_sort_traits_adapter_d<Kernel,Vect_ppmap> Search_traits_d;
int main()
{
double coords[] ={ 1.0, 1.0, 1.0, 1.0,
2.0, 2.0, 2.0, 2.0 };
Data_vector points;
points.push_back(std::make_pair(Point_d(4,coords ,coords+4) , 1));
points.push_back(std::make_pair(Point_d(4,coords+4,coords+8) , 2));
std::vector<Vect_ppmap::key_type> indices;
indices.reserve(points.size());
std::copy(
boost::counting_iterator<Vect_ppmap::key_type>(0),
boost::counting_iterator<Vect_ppmap::key_type>(points.size()),
std::back_inserter(indices) );
std::cout << "Order using default policy (median)\n";
CGAL::spatial_sort(
indices.begin(),
indices.end(),
Search_traits_d(Vect_ppmap(points))
);
for (auto i : indices)
std::cout << points[i].second << " ";
std::cout << std::endl;
std::cout << "Order using middle policy\n";
CGAL::spatial_sort(
indices.begin(),
indices.end(),
Search_traits_d(Vect_ppmap(points)),
CGAL::Hilbert_sort_middle_policy()
);
for (auto i : indices)
std::cout << points[i].second << " ";
std::cout << std::endl;
std::cout << "done" << std::endl;
return 0;
}
CGAL::Spatial_sort_traits_adapter_d
Given a property map associating a key to a point, the class Spatial_sort_traits_adapter_d induces a ...
Definition: Spatial_sort_traits_adapter_d.h:20

# Parallel Spatial Sorting

In 2D (3D), Hilbert or spatial sorting recursively subdivides the input range in four (eight) subranges. Therefore, a natural way to parallelize the sorting algorithm is to split the initial range in four (eight) subranges, and let a single thread handle any further subdivision and sorting for a given subrange. This parallel algorithm is only available when the median strategy policy is being used (this is the case by default), as this policy ensures balance between all subranges. This is not necessarily the case with the middle strategy, where the subrange sizes can greatly vary.

The parallel version of the algorithm is enabled by specifying the template parameter `CGAL::Parallel_tag`. In case it is not sure whether TBB is available and linked with CGAL, `CGAL::Parallel_if_available_tag` can be used. By default, the sequential version is used.

Example: Spatial_sorting/parallel_spatial_sort_3.cpp

Show / Hide
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/point_generators_3.h>
#include <CGAL/Random.h>
#include <CGAL/spatial_sort.h>
#include <iostream>
#include <vector>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef K::Point_3 Point_3;
typedef CGAL::Creator_uniform_3<double,Point_3> Creator_3;
int main()
{
std::size_t pt_nb = 10000;
std::vector<Point_3> points;
points.reserve(pt_nb);
CGAL::Random_points_in_cube_3<Point_3> gen(1.0);
for(std::size_t i=0; i<pt_nb; ++i)
points.push_back(*gen++);
// By default sequential
CGAL::spatial_sort(points.begin(),points.end());
// Add the template argument to switch on parallelism if available
// You can also use Parallel_tag if you know that TBB is enabled
CGAL::spatial_sort<CGAL::Parallel_if_available_tag>(points.begin(), points.end());
return 0;
}

# Design and Implementation History

The first implementation of Hilbert and spatial sorting (2D and 3D) in CGAL was done by Cristophe Delage. Olivier Devillers improved its design, and implemented its multidimensional version.

Pedro Machado Manhaes de Castro and Olivier Devillers added Hilbert sorting on the sphere.

Andreas Fabri added a parallel version of Hilbert and spatial sorting.