// TODO make it parallel...
// transpose a graph
private DirectedGraph<E> transpose(DirectedGraph<E> dg) {
DirectedGraph<E> dg2 = new DirectedGraphImpl<E>();
Collection<Node<E>> nodes = dg.getAllNodes();
Iterator<Node<E>> iter = nodes.iterator();
while (iter.hasNext()) {
Node<E> n = iter.next();
E start = n.getValue();
Collection<Node<E>> dsts = dg.getDestinations(n);
Iterator<Node<E>> dstsIter = dsts.iterator();
while (dstsIter.hasNext()) {
Node<E> adjNode = dstsIter.next();
dg2.addEdge(adjNode.getValue(), start, 0);
}
}
return dg2;
}
/**
* This is an implementation of a parallelization of Dijkstra's shortest path algorithm described
* by Crauser, Mehlhorn, Meyer and Sanders in their paper "A Parallelization of Dijkstra's
* Shortest Path Algorithm" in 23rd Symposium on Mathematical Foundations of Computer Science,
* 1998. To gain a complete understanding of this data structure, please first read this paper,
* available at: http://citeseer.ist.psu.edu/crauser98parallelization.html
*
* <p>This paper propose simple criteria which divide Dijkstra's sequential SSSP algorithm into a
* number of phases,such that the operations within a phase can be done in parallel.
*
* <p>In the first variant (OUT-version) we compute a threshold defined via the weights of the
* outgoing edges:let L=min{tent(u) + c(u,z) : u is queued and (u,z) belongs E} and remove all
* nodes v from the queue then dist(v) = tent(v). The threshold for the OUT-criterion can either
* be computed via a second priority queue for o(v) = tent(v) + min(c(u,v) : (u,v) belongs to E}
* or even on the fly while while removing nodes.
*
* <p>The second variant, the IN-version, is defined via the incoming edges: let M = min{tent(u) :
* u is queued} and i(v) = tent(v) - min{c(u,v) : (u,v) belongs to E} for any queued vertex v.
* Then v can be safely removed from the queue if i(v) <= M. Removable nodes of the IN-type can be
* found efficiently by using an additional priority queue for i(.).
*
* <p>Finally, the INOUT-applies both criteria in conjunction.
*
* <p>Sample performance results here.
*
* @param <E> type of element on node
* @param graph Graph
* @param exec Thread pool for executing task in parallel.
* @param source source node
* @param end end node
* @return the length of shortest path from source node to end node
*/
public static <E> double getShortestPath(
Graph<E> graph, ExecutorService exec, Node<E> source, Node<E> end) {
// if source is the same as end, return 0
if (end.equals(source)) {
return 0;
}
// executor for processing node over threshold
final CompletionService<Boolean> completionService =
new ExecutorCompletionService<Boolean>(exec);
// size of nodes in the graph
final int nodeSize = graph.getAllNodes().size();
// min weight on the edges in the graph. precompute once and for all
// upon initialization
final Map<Node<E>, Double> minWeight = computeMinWeight(graph);
// tentative length of path from source node to end nodes
final Map<Node<E>, Double> tentative = computeTentative(graph, source);
// auxiliary array to record predecessor of shortest path. use to
// recover the shortest path
final Map<Node<E>, AdjacentNode<E>> predecessor = computePredecessor(graph, source);
// nodes queue waiting for being processed
final PriorityQueue<AdjacentNode<E>> queued =
new PriorityQueue<AdjacentNode<E>>(nodeSize, nodeComparator);
// enqueue the source node as initiation
queued.offer(new AdjacentNode<E>(source, (tentative.get(source))));
/*
* thresholds decide which nodes should be choose to be processed
* parallely
*/
final PriorityQueue<AdjacentNode<E>> thresholds =
new PriorityQueue<AdjacentNode<E>>(nodeSize, nodeComparator);
// enqueue the source node as initiation
thresholds.offer(new AdjacentNode<E>(source, (tentative.get(source) + minWeight.get(source))));
// min threshold node from threshold queue
AdjacentNode<E> thrsNode;
// value of min threshold
double threshold;
// settled list all nodes has be processed
final List<Node<E>> settled = new ArrayList<Node<E>>();
while (!queued.isEmpty()) {
// there is still nodes to be processed
// find the first node not be settled from thresholds
do {
thrsNode = thresholds.poll();
} while (settled.contains(thrsNode.getNode()));
// get value of threshold
threshold = thrsNode.getWeight();
while (!queued.isEmpty()) {
// there is still nodes to be processed
if (queued.peek().getWeight() <= threshold) {
// weight of the first unprocessed node is less than
// threshold. it should be dequed for processing.
final Node<E> v = queued.poll().getNode();
// mark it as processed
settled.add(v);
// get the size of linked nodes
int size = graph.getLinkedNodes(v).size();
for (AdjacentNode<E> wAdj : graph.getLinkedNodes(v)) {
// process linked node
final Node<E> w = wAdj.getNode();
final double weight = wAdj.getWeight();
// submit the linked node to executor to parallelly
// process
completionService.submit(
new Callable<Boolean>() {
public Boolean call() {
double x = tentative.get(v) + weight;
// find a shorter path from source node v to
// end node w
if (x < tentative.get(w)) {
// update tentative array and predecessor
// array
tentative.put(w, x);
predecessor.put(w, new AdjacentNode<E>(v, weight));
// update the priority queue queued and
// threshold to reflect the new shorter path
decreasKey(queued, new AdjacentNode<E>(w, x));
decreasKey(thresholds, new AdjacentNode<E>(w, x + minWeight.get(w)));
}
return true;
}
});
}
// synchronization bar.continue after all linked node has
// been processed
try {
for (int i = 0; i < size; i++) {
completionService.take();
}
} catch (InterruptedException e) {
e.printStackTrace();
}
} else {
break;
}
}
}
// recover path using predecessor array
double length = 0;
boolean reachable = false;
AdjacentNode<E> start;
while ((start = predecessor.get(end)).getNode() != DUMMY_NODE) {
reachable = true;
length += start.getWeight();
end = start.getNode();
}
// return the length if reachable, otherwise return infinity
if (reachable) {
return length;
} else {
return Double.POSITIVE_INFINITY;
}
}