124 lines
4.1 KiB
ReStructuredText
124 lines
4.1 KiB
ReStructuredText
.. Copyright (C) 2004-2009 The Trustees of Indiana University.
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Use, modification and distribution is subject to the Boost Software
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License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
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http://www.boost.org/LICENSE_1_0.txt)
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===================================
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|Logo| Scalable R-MAT generator
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===================================
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::
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template<typename ProcessGroup, typename Distribution,
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typename RandomGenerator, typename Graph>
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class scalable_rmat_iterator
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{
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public:
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typedef std::input_iterator_tag iterator_category;
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typedef std::pair<vertices_size_type, vertices_size_type> value_type;
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typedef const value_type& reference;
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typedef const value_type* pointer;
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typedef void difference_type;
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scalable_rmat_iterator();
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scalable_rmat_iterator(ProcessGroup pg, Distribution distrib,
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RandomGenerator& gen, vertices_size_type n,
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edges_size_type m, double a, double b, double c,
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double d, bool permute_vertices = true);
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// Iterator operations
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reference operator*() const;
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pointer operator->() const;
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scalable_rmat_iterator& operator++();
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scalable_rmat_iterator operator++(int);
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bool operator==(const scalable_rmat_iterator& other) const;
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bool operator!=(const scalable_rmat_iterator& other) const;
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};
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This class template implements a generator for R-MAT graphs [CZF04]_,
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suitable for initializing an adjacency_list or other graph structure
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with iterator-based initialization. An R-MAT graph has a scale-free
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distribution w.r.t. vertex degree and is implemented using
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Recursive-MATrix partitioning.
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Where Defined
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-------------
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<``boost/graph/rmat_graph_generator.hpp``>
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Constructors
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------------
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::
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scalable_rmat_iterator();
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Constructs a past-the-end iterator.
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::
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scalable_rmat_iterator(ProcessGroup pg, Distribution distrib,
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RandomGenerator& gen, vertices_size_type n,
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edges_size_type m, double a, double b, double c,
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double d, bool permute_vertices = true);
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Constructs an R-MAT generator iterator that creates a graph with ``n``
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vertices and ``m`` edges. Inside the ``scalable_rmat_iterator``
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processes communicate using ``pg`` to generate their local edges as
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defined by ``distrib``. ``a``, ``b``, ``c``, and ``d`` represent the
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probability that a generated edge is placed of each of the 4 quadrants
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of the partitioned adjacency matrix. Probabilities are drawn from the
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random number generator ``gen``. Vertex indices are permuted to
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eliminate locality when ``permute_vertices`` is true.
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Example
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-------
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::
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#include <boost/graph/distributed/mpi_process_group.hpp>
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#include <boost/graph/compressed_sparse_row_graph.hpp>
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#include <boost/graph/rmat_graph_generator.hpp>
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#include <boost/random/linear_congruential.hpp>
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using boost::graph::distributed::mpi_process_group;
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typedef compressed_sparse_row_graph<directedS, no_property, no_property, no_property,
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distributedS<mpi_process_group> > Graph;
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typedef boost::scalable_rmat_iterator<boost::minstd_rand, Graph> RMATGen;
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int main()
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{
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boost::minstd_rand gen;
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mpi_process_group pg;
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int N = 100;
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boost::parallel::variant_distribution<ProcessGroup> distrib
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= boost::parallel::block(pg, N);
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// Create graph with 100 nodes and 400 edges
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Graph g(RMATGen(pg, distrib, gen, N, 400, 0.57, 0.19, 0.19, 0.05),
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RMATGen(), N, pg, distrib);
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return 0;
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}
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Bibliography
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------------
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.. [CZF04] D Chakrabarti, Y Zhan, and C Faloutsos. R-MAT: A Recursive
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Model for Graph Mining. In Proceedings of 4th International Conference
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on Data Mining, pages 442--446, 2004.
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-----------------------------------------------------------------------------
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Copyright (C) 2009 The Trustees of Indiana University.
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Authors: Nick Edmonds, Brian Barrett, and Andrew Lumsdaine
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.. |Logo| image:: pbgl-logo.png
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:align: middle
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:alt: Parallel BGL
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:target: http://www.osl.iu.edu/research/pbgl
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.. _Sequential connected components: http://www.boost.org/libs/graph/doc/connected_components.html
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