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// named parameter version template <typename Graph, typename P, typename T, typename R> void dijkstra_shortest_paths(Graph& g, typename graph_traits<Graph>::vertex_descriptor s, const bgl_named_params<P, T, R>& params); // non-named parameter version template <typename Graph, typename DijkstraVisitor, typename PredecessorMap, typename DistanceMap, typename WeightMap, typename VertexIndexMap, typename CompareFunction, typename CombineFunction, typename DistInf, typename DistZero> void dijkstra_shortest_paths (const Graph& g, typename graph_traits<Graph>::vertex_descriptor s, PredecessorMap predecessor, DistanceMap distance, WeightMap weight, VertexIndexMap index_map, CompareFunction compare, CombineFunction combine, DistInf inf, DistZero zero, DijkstraVisitor vis);

The `dijkstra_shortest_paths()` function solves the single-source
shortest paths problem on a weighted, undirected or directed
distributed graph. There are two implementations of distributed
Dijkstra's algorithm, which offer different performance
tradeoffs. Both are accessible via the `dijkstra_shortest_paths()`
function (for compatibility with sequential BGL programs). The
distributed Dijkstra algorithms are very similar to their sequential
counterparts. Only the differences are highlighted here; please refer
to the sequential Dijkstra implementation for additional
details. The best-performing implementation for most cases is the
Delta-Stepping algorithm; however, one can also employ the more
conservative Crauser et al.'s algorithm or the simlistic Eager
Dijkstra's algorithm.

Contents

<`boost/graph/dijkstra_shortest_paths.hpp`>

All parameters of the sequential Dijkstra implementation are supported and have essentially the same meaning. The distributed Dijkstra implementations introduce a new parameter that allows one to select Eager Dijkstra's algorithm and control the amount of work it performs. Only differences and new parameters are documented here.

- IN:
`Graph& g` - The graph type must be a model of Distributed Graph.
- IN:
`vertex_descriptor s` - The start vertex must be the same in every process.
- OUT:
`predecessor_map(PredecessorMap p_map)` The predecessor map must be a Distributed Property Map or

`dummy_property_map`, although only the local portions of the map will be written.**Default:**`dummy_property_map`- UTIL/OUT:
`distance_map(DistanceMap d_map)` - The distance map must be either a Distributed Property Map or
`dummy_property_map`. It will be given the`vertex_distance`role. - IN:
`visitor(DijkstraVisitor vis)` - The visitor must be a distributed Dijkstra visitor. The suble differences between sequential and distributed Dijkstra visitors are discussed in the section Visitor Event Points.
- UTIL/OUT:
`color_map(ColorMap color)` - The color map must be a Distributed Property Map with the same
process group as the graph
`g`whose colors must monotonically darken (white -> gray -> black). The default value is a distributed`iterator_property_map`created from a`std::vector`of`default_color_type`.

IN: `lookahead(distance_type look)`

When this parameter is supplied, the implementation will use the Eager Dijkstra's algorithm with the given lookahead value. Lookahead permits distributed Dijkstra's algorithm to speculatively process vertices whose shortest distance from the source may not have been found yet. When the distance found is the shortest distance, parallelism is improved and the algorithm may terminate more quickly. However, if the distance is not the shortest distance, the vertex will need to be reprocessed later, resulting in more work.

The type

distance_typeis the value type of theDistanceMapproperty map. It is a nonnegative value specifying how far ahead Dijkstra's algorithm may process values.

Default:no value (lookahead is not employed; uses Crauser et al.'s algorithm).

The Dijkstra Visitor concept defines 7 event points that will be triggered by the sequential Dijkstra implementation. The distributed Dijkstra retains these event points, but the sequence of events triggered and the process in which each event occurs will change depending on the distribution of the graph, lookahead, and edge weights.

`initialize_vertex(s, g)`- This will be invoked by every process for each local vertex.
`discover_vertex(u, g)`- This will be invoked each type a process discovers a new vertex
`u`. Due to incomplete information in distributed property maps, this event may be triggered many times for the same vertex`u`. `examine_vertex(u, g)`- This will be invoked by the process owning the vertex
`u`. This event may be invoked multiple times for the same vertex when the graph contains negative edges or lookahead is employed. `examine_edge(e, g)`- This will be invoked by the process owning the source vertex of
`e`. As with`examine_vertex`, this event may be invoked multiple times for the same edge. `edge_relaxed(e, g)`- Similar to
`examine_edge`, this will be invoked by the process owning the source vertex and may be invoked multiple times (even without lookahead or negative edges). `edge_not_relaxed(e, g)`- Similar to
`edge_relaxed`. Some`edge_not_relaxed`events that would be triggered by sequential Dijkstra's will become`edge_relaxed`events in distributed Dijkstra's algorithm. `finish_vertex(e, g)`- See documentation for
`examine_vertex`. Note that a "finished" vertex is not necessarily finished if lookahead is permitted or negative edges exist in the graph.

namespace graph { template<typename DistributedGraph, typename DijkstraVisitor, typename PredecessorMap, typename DistanceMap, typename WeightMap, typename IndexMap, typename ColorMap, typename Compare, typename Combine, typename DistInf, typename DistZero> void crauser_et_al_shortest_paths (const DistributedGraph& g, typename graph_traits<DistributedGraph>::vertex_descriptor s, PredecessorMap predecessor, DistanceMap distance, WeightMap weight, IndexMap index_map, ColorMap color_map, Compare compare, Combine combine, DistInf inf, DistZero zero, DijkstraVisitor vis); template<typename DistributedGraph, typename DijkstraVisitor, typename PredecessorMap, typename DistanceMap, typename WeightMap> void crauser_et_al_shortest_paths (const DistributedGraph& g, typename graph_traits<DistributedGraph>::vertex_descriptor s, PredecessorMap predecessor, DistanceMap distance, WeightMap weight); template<typename DistributedGraph, typename DijkstraVisitor, typename PredecessorMap, typename DistanceMap> void crauser_et_al_shortest_paths (const DistributedGraph& g, typename graph_traits<DistributedGraph>::vertex_descriptor s, PredecessorMap predecessor, DistanceMap distance); }

The formulation of Dijkstra's algorithm by Crauser, Mehlhorn, Meyer, and Sanders [CMMS98a] improves the scalability of parallel Dijkstra's algorithm by increasing the number of vertices that can be processed in a given superstep. This algorithm adapts well to various graph types, and is a simple algorithm to use, requiring no additional user input to achieve reasonable performance. The disadvantage of this algorithm is that the implementation is required to manage three priority queues, which creates a large amount of work at each node.

This algorithm is used by default in distributed
`dijkstra_shortest_paths()`.

<`boost/graph/distributed/crauser_et_al_shortest_paths.hpp`>

This algorithm performs *O(V log V)* work in *d + 1* BSP supersteps,
where *d* is at most *O(V)* but is generally much smaller. On directed
Erdos-Renyi graphs with edge weights in [0, 1), the expected number of
supersteps *d* is *O(n^(1/3))* with high probability.

The following charts illustrate the performance of the Parallel BGL implementation of Crauser et al.'s
algorithm on graphs with edge weights uniformly selected from the
range *[0, 1)*.

namespace graph { template<typename DistributedGraph, typename DijkstraVisitor, typename PredecessorMap, typename DistanceMap, typename WeightMap, typename IndexMap, typename ColorMap, typename Compare, typename Combine, typename DistInf, typename DistZero> void eager_dijkstra_shortest_paths (const DistributedGraph& g, typename graph_traits<DistributedGraph>::vertex_descriptor s, PredecessorMap predecessor, DistanceMap distance, typename property_traits<DistanceMap>::value_type lookahead, WeightMap weight, IndexMap index_map, ColorMap color_map, Compare compare, Combine combine, DistInf inf, DistZero zero, DijkstraVisitor vis); template<typename DistributedGraph, typename DijkstraVisitor, typename PredecessorMap, typename DistanceMap, typename WeightMap> void eager_dijkstra_shortest_paths (const DistributedGraph& g, typename graph_traits<DistributedGraph>::vertex_descriptor s, PredecessorMap predecessor, DistanceMap distance, typename property_traits<DistanceMap>::value_type lookahead, WeightMap weight); template<typename DistributedGraph, typename DijkstraVisitor, typename PredecessorMap, typename DistanceMap> void eager_dijkstra_shortest_paths (const DistributedGraph& g, typename graph_traits<DistributedGraph>::vertex_descriptor s, PredecessorMap predecessor, DistanceMap distance, typename property_traits<DistanceMap>::value_type lookahead); }

In each superstep, parallel Dijkstra's algorithm typically only processes nodes whose distances equivalent to the global minimum distance, because these distances are guaranteed to be correct. This variation on the algorithm allows the algorithm to process all vertices whose distances are within some constant value of the minimum distance. The value is called the "lookahead" value and is provided by the user as the fifth parameter to the function. Small values of the lookahead parameter will likely result in limited parallelization opportunities, whereas large values will expose more parallelism but may introduce (non-infinite) looping and result in extra work. The optimal value for the lookahead parameter depends on the input graph; see [CMMS98b] and [MS98].

This algorithm will be used by `dijkstra_shortest_paths()` when it
is provided with a lookahead value.

<`boost/graph/distributed/eager_dijkstra_shortest_paths.hpp`>

This algorithm performs *O(V log V)* work in *d
+ 1* BSP supersteps, where *d* is at most *O(V)* but may be smaller
depending on the lookahead value. the algorithm may perform more work
when a large lookahead is provided, because vertices will be
reprocessed.

The performance of the eager Dijkstra's algorithm varies greatly
depending on the lookahead value. The following charts illustrate the
performance of the Parallel BGL on graphs with edge weights uniformly
selected from the range *[0, 1)* and a constant lookahead of 0.1.

namespace boost { namespace graph { namespace distributed { template <typename Graph, typename PredecessorMap, typename DistanceMap, typename WeightMap> void delta_stepping_shortest_paths (const Graph& g, typename graph_traits<Graph>::vertex_descriptor s, PredecessorMap predecessor, DistanceMap distance, WeightMap weight, typename property_traits<WeightMap>::value_type delta) template <typename Graph, typename PredecessorMap, typename DistanceMap, typename WeightMap> void delta_stepping_shortest_paths (const Graph& g, typename graph_traits<Graph>::vertex_descriptor s, PredecessorMap predecessor, DistanceMap distance, WeightMap weight) } } } }

The delta-stepping algorithm [MS98] is another variant of the parallel
Dijkstra algorithm. Like the eager Dijkstra algorithm, it employs a
lookahead (`delta`) value that allows processors to process vertices
before we are guaranteed to find their minimum distances, permitting
more parallelism than a conservative strategy. Delta-stepping also
introduces a multi-level bucket data structure that provides more
relaxed ordering constraints than the priority queues employed by the
other Dijkstra variants, reducing the complexity of insertions,
relaxations, and removals from the central data structure. The
delta-stepping algorithm is the best-performing of the Dijkstra
variants.

The lookahead value `delta` determines how large each of the
"buckets" within the delta-stepping queue will be, where the ith
bucket contains edges within tentative distances between `delta``*i
and ``delta``*(i+1). ``delta` must be a positive value. When omitted,
`delta` will be set to the maximum edge weight divided by the
maximum degree.

<`boost/graph/distributed/delta_stepping_shortest_paths.hpp`>

See the separate Dijkstra example.

[CMMS98a] | Andreas Crauser, Kurt Mehlhorn, Ulrich Meyer, and Peter Sanders. A
Parallelization of Dijkstra's Shortest Path Algorithm. In
Mathematical Foundations of Computer Science (MFCS), volume 1450 of
Lecture Notes in Computer Science, pages 722--731, 1998. Springer. |

[CMMS98b] | Andreas Crauser, Kurt Mehlhorn, Ulrich Meyer, and Peter Sanders. Parallelizing Dijkstra's shortest path algorithm. Technical report, MPI-Informatik, 1998. |

[MS98] | (1, 2) Ulrich Meyer and Peter Sanders. Delta-stepping: A parallel
shortest path algorithm. In 6th ESA, LNCS. Springer, 1998. |

Copyright (C) 2004, 2005, 2006, 2007, 2008 The Trustees of Indiana University.

Authors: Douglas Gregor and Andrew Lumsdaine