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domain::BlockZCurveProcDistribution< Dim > Struct Template Reference

Distribution strategy for assigning elements to CPUs using a Morton ('Z-order') space-filling curve to determine placement within each block. More...

#include <ElementDistribution.hpp>

Public Member Functions

 BlockZCurveProcDistribution (size_t number_of_procs, const std::vector< std::array< size_t, Dim >> &refinements_by_block) noexcept
size_t get_proc_for_element (const ElementId< Dim > &element_id) const noexcept
 Gets the suggested processor number for a particular element, determined by the greedy block assignment and Morton curve element assignment described in detail in the parent class documentation.

Detailed Description

template<size_t Dim>
struct domain::BlockZCurveProcDistribution< Dim >

Distribution strategy for assigning elements to CPUs using a Morton ('Z-order') space-filling curve to determine placement within each block.


The element distribution assigns a balanced number of elements to each processor. This distribution is computed by first greedily assigning to each processor an allowance of [total number of elements]/[number of processors] elements from one or more blocks, starting with the lowest number block that still has elements to contribute to an allowance. Then, once those allowances are determined, a separate Z-order curve is established for each block and the elements are assigned to processors within each block by greedily filling each processors' allowance by contiguous intervals along the Z-order curve. Some examples:

Morton curves are a simple and easily-computed space-filling curve that (unlike Hilbert curves) permit diagonal traversal. See, for instance, [90] for a discussion of mesh partitioning using space-filling curves. A concrete example of the use of a Morton curve in 2d is given below.

A sketch of a 2D block with 4x2 elements, with each element labeled according to the order on the Morton curve:

0 1 2 3
y 0 | 0 2 4 6
| | | / | / | / |
v 1 | 1 3 5 7

(forming a zig-zag path, that under some rotation/reflection has a 'Z' shape).

The Morton curve method is a quick way of getting acceptable spatial locality – usually, for approximately even distributions, it will ensure that elements are assigned in large volume chunks, and the structure of the Morton curve ensures that for a given processor and block, the elements will be assigned in no more than two orthogonally connected clusters. In principle, a Hilbert curve could potentially improve upon the gains obtained by this class by guaranteeing that all elements within each block form a single orthognally connected cluster.

The assignment of portions of blocks to processors may use partial blocks, and/or multiple blocks to ensure an even distribution of elements to processors. We currently make no distinction between dividing elements between processors within a node and dividing elements between processors across nodes. The current technique aims to have a simple method of reducing communication globally, though it would likely be more efficient to prioritize minimization of inter-node communication, because communication across interconnects is the primary cost of communication in charm++ runs.

The use of the Morton curve to generate a well-clustered element distribution currently assumes that the refinement is uniform over each block, with no internal structure that would be generated by, for instance AMR. This distribution method will need alteration to perform well for blocks with internal structure from h-refinement. Morton curves can be defined recursively, so a generalization of the present method is possible for blocks with internal refinement

The documentation for this struct was generated from the following file: