ceph/src/boost/libs/graph_parallel/doc/html/overview.html

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  11 <div class="document" id="an-overview-of-the-parallel-boost-graph-library">
  12 <h1 class="title">An Overview of the Parallel Boost Graph Library</h1>
  13
  14 <!-- Copyright (C) 2004-2008 The Trustees of Indiana University.
  15 Use, modification and distribution is subject to the Boost Software
  16 License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
  17 http://www.boost.org/LICENSE_1_0.txt) -->
  18 <img align="right" alt="An example graph" class="align-right" src="../graph.png" style="width: 206px; height: 184px;" />
  19 <p>The Parallel Boost Graph Library (Parallel BGL) is a C++ library for
  20 parallel, distributed computation on graphs. The Parallel BGL contains
  21 distributed graph data structures, distributed graph algorithms,
  22 abstractions over the communication medium (such as MPI), and
  23 supporting data structures. A graph (also called a <em>network</em>) consists
  24 of a set of <em>vertices</em> and a set of relationships between vertices,
  25 called <em>edges</em>. The edges may be <em>undirected</em>, meaning that the
  26 relationship between vertices is mutual, e.g., &quot;X is related to Y&quot;, or
  27 they can be <em>directed</em>, meaning that the relationship goes only one
  28 way, e.g., &quot;X is the child of Y&quot;. The following figure illustrates a
  29 typical directed graph, where <em>a-i</em> are the vertices and the arrows
  30 represent edges.</p>
  31 <img align="right" alt="A distributed graph" class="align-right" src="../distributed-graph.png" style="width: 229px; height: 199px;" />
  32 <p>The Parallel BGL is primarily concerned with <em>distributed</em>
  33 graphs. Distributed graphs are conceptually graphs, but their storage
  34 is spread across multiple processors. The following figure
  35 demonstrates a distributed version of the graph above, where the graph
  36 has been divided among three processors (represented by the grey
  37 rectangles). Edges in the graph may be either local (with both
  38 endpoints stored on the same processor) or remote (the target of the
  39 edge is stored on a different processor).</p>
  40 <p>The Parallel BGL is a generic library. At its core are <em>generic</em>
  41 distributed graph algorithms, which can operate on any distributed
  42 graph data structure provided that data structure meets certain
  43 requirements. For instance, the algorithm may need to enumerate the
  44 set of vertices stored on the current processor, enumerate the set of
  45 outgoing edges from a particular vertex, and determine on which
  46 processor the target of each edge resides. The graph algorithms in the
  47 Parallel BGL are also generic with respect to the <em>properties</em>
  48 attached to edges and vertices in a graph; for instance, the weight of
  49 each edge can be stored as part of the graph or allocated in a
  50 completely separate data structure.</p>
  51 <p>The genericity available in the algorithms of the Parallel BGL allows
  52 them to be applied to existing graph data structures. However, most
  53 users will instead be writing new code that takes advantage of the
  54 Parallel BGL. The Parallel BGL provides distributed graph data
  55 structures that meet the requirements of the Parallel BGL
  56 algorithms. The primary data structure is the <a class="reference external" href="distributed_adjacency_list.html">distributed adjacency
  57 list</a>, which allows storage and manipulation of a (distributed)
  58 graph. The vertices in the graph are divided among the various
  59 processors, and each of the edges outgoing from a vertex are stored on
  60 the processor that &quot;owns&quot; (stores) that vertex. The following figure
  61 illustrates the distributed adjacency list representation.</p>
  62 <div align="center" class="align-center"><img alt="A distributed adjacency list" class="align-center" src="../dist-adjlist.png" style="width: 446px; height: 154px;" /></div>
  63 <img align="right" alt="A distributed property map" class="align-right" src="../dist-pmap.png" style="width: 271px; height: 175px;" />
  64 <p>The <a class="reference external" href="distributed_adjacency_list.html">distributed adjacency list</a> distributes the structure of a graph
  65 over multiple processors. While graph structure is in important part
  66 of many graph problems, there are typically other properties attached
  67 to the vertices and edges, such as edge weights or the position of
  68 vertices within a grid. These properties are stored in <em>property
  69 maps</em>, which associate a single piece of data with each edge or vertex
  70 in a graph. Distributed property maps extend this notion to
  71 distributed computing, where properties are stored on the same
  72 processor as the vertex or edge. The following figure illustrates the
  73 distribution of a property map storing colors (white, gray, black) for
  74 each vertex. In addition to the storage for each vertex, the
  75 processors store some &quot;ghost cells&quot; that cache values actually stored
  76 on other processors, represented by the dashed boxes.</p>
  77 <p>Tying together all of the distributed data structures of the Parallel
  78 BGL are its process groups and distributed graph algorithms. Process
  79 groups coordinate the interactions between multiple processes and
  80 distributed data structures by abstracting the communication
  81 mechanism. The algorithms are typically written using the SPMD model
  82 (Single Program, Multiple Data) and interact with both the distributed
  83 data structures and the process group itself. At various points in the
  84 algorithm's execution, all processes execute a synchronization point,
  85 which allows all of the distributed data structures to ensure an
  86 appropriate degree of consistency across processes. The following
  87 diagram illustrates the communication patterns within the the
  88 execution of a distributed algorithm in the Parallel BGL. In
  89 particular, the diagram illustrates the distributed data structures
  90 used in a distributed breadth-first search, from the top-left and
  91 proceeding clockwise:</p>
  92 <blockquote>
  93 <ul class="simple">
  94 <li>a user-defined property map that tracks the distance from the
  95 source vertex to all other vertices,</li>
  96 <li>an automatically-generated property map that tracks the &quot;color&quot;
  97 of vertices in the (distributed) graph, to determine which
  98 vertices have been seen before,</li>
  99 <li>a distributed queue, which coordinates the breadth-first search
 100 and distributes new vertices to search, and</li>
 101 <li>a distributed graph, on which the breadth-first search is
 102 operating.</li>
 103 </ul>
 104 </blockquote>
 105 <div align="center" class="align-center"><img alt="Parallel Boost Graph Library architecture" class="align-center" src="../architecture.png" style="width: 485px; height: 410px;" /></div>
 106 <hr class="docutils" />
 107 <p>Copyright (C) 2005 The Trustees of Indiana University.</p>
 108 <p>Authors: Douglas Gregor and Andrew Lumsdaine</p>
 109 <span class="target" id="process-groups"></span>
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