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Document parallel build

This commit is contained in:
Simon Giraudot 2020-03-05 10:41:13 +01:00
parent 3f28ea958f
commit ab3f7140a9
2 changed files with 17 additions and 0 deletions
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10
Spatial_searching/doc/Spatial_searching/CGAL/Kd_tree.h
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@ -115,7 +115,17 @@ at the first call to a query or removal member function. You can call
`build()` explicitly to ensure that the next call to `build()` explicitly to ensure that the next call to
query functions will not trigger the reconstruction of the query functions will not trigger the reconstruction of the
data structure. data structure.
\tparam ConcurrencyTag enables sequential versus parallel
algorithm. Possible values are `Sequential_tag`, `Parallel_tag`, and
`Parallel_if_available_tag`. This template parameter is optional:
calling `build()` without specifying the concurrency tag will result
in `Sequential_tag` being used. If `build()` is not called by the user
but called implicitly at the first call to a query or removal member
function, `Sequential_tag` is also used.
*/ */
template <typename ConcurrencyTag>
void build(); void build();
/*! /*!

7
Spatial_searching/doc/Spatial_searching/Spatial_searching.txt
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@ -480,6 +480,13 @@ to the nearest nodes exceeds the distance to the nearest point found
with a factor 1/ (1+\f$ \epsilon\f$). Priority search supports next with a factor 1/ (1+\f$ \epsilon\f$). Priority search supports next
neighbor search, standard search does not. neighbor search, standard search does not.
In order to speed-up the construction of the `kd` tree, the child
branches of each internal node can be computed in parallel, by calling
`Kd_tree::build<CGAL::Parallel_tag>()`. On a quad-core processor, the
parallel construction is experimentally 2 to 3 times faster than the
sequential version, depending on the point cloud. The parallel version
requires the executable to be linked against the <a href="https://www.threadingbuildingblocks.org">Intel TBB library</a>.
In order to speed-up the internal distance computations in nearest In order to speed-up the internal distance computations in nearest
neighbor searching in high dimensional space, the approximate neighbor searching in high dimensional space, the approximate
searching package supports orthogonal distance computation. Orthogonal distance searching package supports orthogonal distance computation. Orthogonal distance