Algorithm for computing partial orderings of dependency graphs

I'm trying to compute a partial "topological sort" of a dependency graph, which is actually a DAG (Directed Acyclic Graph) to be precise; so as to execute tasks without conflicting dependencies in parallel.

I came up with this simple algorithm because what I found on Google wasn't all that helpful (I keep finding only algorithms that themselves run in parallel to compute a normal topological sort).

visit(node)
{
    maxdist = 0;
    foreach (e : in_edge(node))
    {
        maxdist = max(maxdist, 1 + visit(source(e)))
    }
    distance[node] = maxdist;
    return distance[node];
}

make_partial_ordering(node)
{
    set all node distances to +infinite;
    num_levels = visit(node, 0);
    return sort(nodes, by increasing distance) where distances < +infinite;
}

(Note that this is only pseudocode and there would certainly be a few possible small optimizations)

As for the correctness, it seems pretty obvious: For the leafs (:= nodes which have no further dependency) the maximum distance-to-leaf is always 0 (correct because the loop gets skipped due to 0 edges). For any node connected to nodes n1,..,nk the maximum distance-to-leaf is 1 + max{distance[n1],..,distance[nk]}.

I did find this article after I had written down the algorithm: http://msdn.microsoft.com/en-us/magazine/dd569760.aspx But honestly, why are they doing all that list copying and so on, it just seems so really inefficient..?

Anyway I was wondering whether my algorithm is correct, and if so what it is called so that I can read up some stuff about it.

Update: I implemented th开发者_JS百科e algorithm in my program and it seems to be working great for everything I tested. Code-wise it looks like this:

  typedef boost::graph_traits<my_graph> my_graph_traits;
  typedef my_graph_traits::vertices_size_type vertices_size_type;
  typedef my_graph_traits::vertex_descriptor vertex;
  typedef my_graph_traits::edge_descriptor edge;

  vertices_size_type find_partial_ordering(vertex v,
      std::map<vertices_size_type, std::set<vertex> >& distance_map)
  {
      vertices_size_type d = 0;

      BOOST_FOREACH(edge e, in_edges(v))
      {
          d = std::max(d, 1 + find_partial_ordering(source(e), distance_map));
      }

      distance_map[d].insert(v);

      return d;
  }

Your algorithm (C++) works, but it has very bad worst-case time complexity. It computes find_partial_ordering on a node for as many paths as there are to that node. In the case of a tree, the number of paths is 1, but in a general directed acyclic graph the number of paths can be exponential.

You can fix this by memoizing your find_partial_ordering results and returning without recursing when you have already computed the value for a particular node. However, this still leaves you with a stack-busting recursive solution.

An efficient (linear) algorithm for topological sorting is given on Wikipedia. Doesn't this fit your needs?

Edit: ah, I see, you want to know where the depth boundaries are so that you can parallelize everything at a given depth. You can still use the algorithm on Wikipedia for this (and so avoid recursion).

First, Do a topological sort with the algorithm on Wikipedia. Now compute depths by visiting nodes in topological order:

depths : array 1..n
for i in 1..n
    depths[i] = 0
    for j in children of i
        depths[i] = max(depths[i], depths[j] + 1)
return depths

Notice that there's no recursion above, just a plain O(|V| + |E|) algorithm. This has the same complexity as the algorithm on Wikipedia.