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fc93ae629e
The new graph structure replaces the node and edge linked lists with vectors. Free lists (well, free vectors) are used for fast insertion/deletion. The ultimate aim is to make PBQP graphs cheap to clone. The motivation is that the PBQP solver destructively consumes input graphs while computing a solution, forcing the graph to be fully reconstructed for each round of PBQP. This imposes a high cost on large functions, which often require several rounds of solving/spilling to find a final register allocation. If we can cheaply clone the PBQP graph and incrementally update it between rounds then hopefully we can reduce this cost. Further, once we begin pooling matrix/vector values (future work), we can cache some PBQP solver metadata and share it between cloned graphs, allowing the PBQP solver to re-use some of the computation done in earlier rounds. For now this is just a data structure update. The allocator and solver still use the graph the same way as before, fully reconstructing it between each round. I expect no material change from this update, although it may change the iteration order of the nodes, causing ties in the solver to break in different directions, and this could perturb the generated allocations (hopefully in a completely benign way). Thanks very much to Arnaud Allard de Grandmaison for encouraging me to get back to work on this, and for a lot of discussion and many useful PBQP test cases. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194300 91177308-0d34-0410-b5e6-96231b3b80d8
619 lines
19 KiB
C++
619 lines
19 KiB
C++
//===-- HeuristicSolver.h - Heuristic PBQP Solver --------------*- C++ -*-===//
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//
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// The LLVM Compiler Infrastructure
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//
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// This file is distributed under the University of Illinois Open Source
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// License. See LICENSE.TXT for details.
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//
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//===----------------------------------------------------------------------===//
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//
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// Heuristic PBQP solver. This solver is able to perform optimal reductions for
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// nodes of degree 0, 1 or 2. For nodes of degree >2 a plugable heuristic is
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// used to select a node for reduction.
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//
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//===----------------------------------------------------------------------===//
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#ifndef LLVM_CODEGEN_PBQP_HEURISTICSOLVER_H
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#define LLVM_CODEGEN_PBQP_HEURISTICSOLVER_H
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#include "Graph.h"
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#include "Solution.h"
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#include <limits>
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#include <vector>
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namespace PBQP {
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/// \brief Heuristic PBQP solver implementation.
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///
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/// This class should usually be created (and destroyed) indirectly via a call
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/// to HeuristicSolver<HImpl>::solve(Graph&).
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/// See the comments for HeuristicSolver.
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///
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/// HeuristicSolverImpl provides the R0, R1 and R2 reduction rules,
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/// backpropagation phase, and maintains the internal copy of the graph on
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/// which the reduction is carried out (the original being kept to facilitate
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/// backpropagation).
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template <typename HImpl>
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class HeuristicSolverImpl {
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private:
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typedef typename HImpl::NodeData HeuristicNodeData;
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typedef typename HImpl::EdgeData HeuristicEdgeData;
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typedef std::list<Graph::EdgeId> SolverEdges;
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public:
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/// \brief Iterator type for edges in the solver graph.
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typedef SolverEdges::iterator SolverEdgeItr;
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private:
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class NodeData {
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public:
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NodeData() : solverDegree(0) {}
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HeuristicNodeData& getHeuristicData() { return hData; }
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SolverEdgeItr addSolverEdge(Graph::EdgeId eId) {
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++solverDegree;
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return solverEdges.insert(solverEdges.end(), eId);
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}
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void removeSolverEdge(SolverEdgeItr seItr) {
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--solverDegree;
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solverEdges.erase(seItr);
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}
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SolverEdgeItr solverEdgesBegin() { return solverEdges.begin(); }
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SolverEdgeItr solverEdgesEnd() { return solverEdges.end(); }
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unsigned getSolverDegree() const { return solverDegree; }
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void clearSolverEdges() {
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solverDegree = 0;
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solverEdges.clear();
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}
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private:
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HeuristicNodeData hData;
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unsigned solverDegree;
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SolverEdges solverEdges;
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};
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class EdgeData {
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public:
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HeuristicEdgeData& getHeuristicData() { return hData; }
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void setN1SolverEdgeItr(SolverEdgeItr n1SolverEdgeItr) {
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this->n1SolverEdgeItr = n1SolverEdgeItr;
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}
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SolverEdgeItr getN1SolverEdgeItr() { return n1SolverEdgeItr; }
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void setN2SolverEdgeItr(SolverEdgeItr n2SolverEdgeItr){
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this->n2SolverEdgeItr = n2SolverEdgeItr;
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}
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SolverEdgeItr getN2SolverEdgeItr() { return n2SolverEdgeItr; }
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private:
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HeuristicEdgeData hData;
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SolverEdgeItr n1SolverEdgeItr, n2SolverEdgeItr;
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};
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Graph &g;
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HImpl h;
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Solution s;
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std::vector<Graph::NodeId> stack;
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typedef std::list<NodeData> NodeDataList;
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NodeDataList nodeDataList;
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typedef std::list<EdgeData> EdgeDataList;
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EdgeDataList edgeDataList;
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public:
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/// \brief Construct a heuristic solver implementation to solve the given
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/// graph.
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/// @param g The graph representing the problem instance to be solved.
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HeuristicSolverImpl(Graph &g) : g(g), h(*this) {}
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/// \brief Get the graph being solved by this solver.
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/// @return The graph representing the problem instance being solved by this
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/// solver.
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Graph& getGraph() { return g; }
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/// \brief Get the heuristic data attached to the given node.
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/// @param nItr Node iterator.
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/// @return The heuristic data attached to the given node.
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HeuristicNodeData& getHeuristicNodeData(Graph::NodeId nId) {
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return getSolverNodeData(nId).getHeuristicData();
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}
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/// \brief Get the heuristic data attached to the given edge.
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/// @param eItr Edge iterator.
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/// @return The heuristic data attached to the given node.
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HeuristicEdgeData& getHeuristicEdgeData(Graph::EdgeId eId) {
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return getSolverEdgeData(eId).getHeuristicData();
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}
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/// \brief Begin iterator for the set of edges adjacent to the given node in
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/// the solver graph.
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/// @param nItr Node iterator.
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/// @return Begin iterator for the set of edges adjacent to the given node
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/// in the solver graph.
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SolverEdgeItr solverEdgesBegin(Graph::NodeId nId) {
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return getSolverNodeData(nId).solverEdgesBegin();
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}
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/// \brief End iterator for the set of edges adjacent to the given node in
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/// the solver graph.
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/// @param nItr Node iterator.
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/// @return End iterator for the set of edges adjacent to the given node in
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/// the solver graph.
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SolverEdgeItr solverEdgesEnd(Graph::NodeId nId) {
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return getSolverNodeData(nId).solverEdgesEnd();
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}
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/// \brief Remove a node from the solver graph.
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/// @param eItr Edge iterator for edge to be removed.
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///
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/// Does <i>not</i> notify the heuristic of the removal. That should be
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/// done manually if necessary.
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void removeSolverEdge(Graph::EdgeId eId) {
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EdgeData &eData = getSolverEdgeData(eId);
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NodeData &n1Data = getSolverNodeData(g.getEdgeNode1(eId)),
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&n2Data = getSolverNodeData(g.getEdgeNode2(eId));
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n1Data.removeSolverEdge(eData.getN1SolverEdgeItr());
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n2Data.removeSolverEdge(eData.getN2SolverEdgeItr());
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}
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/// \brief Compute a solution to the PBQP problem instance with which this
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/// heuristic solver was constructed.
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/// @return A solution to the PBQP problem.
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///
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/// Performs the full PBQP heuristic solver algorithm, including setup,
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/// calls to the heuristic (which will call back to the reduction rules in
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/// this class), and cleanup.
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Solution computeSolution() {
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setup();
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h.setup();
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h.reduce();
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backpropagate();
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h.cleanup();
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cleanup();
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return s;
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}
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/// \brief Add to the end of the stack.
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/// @param nItr Node iterator to add to the reduction stack.
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void pushToStack(Graph::NodeId nId) {
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getSolverNodeData(nId).clearSolverEdges();
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stack.push_back(nId);
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}
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/// \brief Returns the solver degree of the given node.
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/// @param nItr Node iterator for which degree is requested.
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/// @return Node degree in the <i>solver</i> graph (not the original graph).
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unsigned getSolverDegree(Graph::NodeId nId) {
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return getSolverNodeData(nId).getSolverDegree();
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}
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/// \brief Set the solution of the given node.
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/// @param nItr Node iterator to set solution for.
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/// @param selection Selection for node.
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void setSolution(const Graph::NodeId &nId, unsigned selection) {
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s.setSelection(nId, selection);
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for (Graph::AdjEdgeItr aeItr = g.adjEdgesBegin(nId),
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aeEnd = g.adjEdgesEnd(nId);
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aeItr != aeEnd; ++aeItr) {
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Graph::EdgeId eId(*aeItr);
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Graph::NodeId anId(g.getEdgeOtherNode(eId, nId));
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getSolverNodeData(anId).addSolverEdge(eId);
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}
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}
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/// \brief Apply rule R0.
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/// @param nItr Node iterator for node to apply R0 to.
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///
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/// Node will be automatically pushed to the solver stack.
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void applyR0(Graph::NodeId nId) {
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assert(getSolverNodeData(nId).getSolverDegree() == 0 &&
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"R0 applied to node with degree != 0.");
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// Nothing to do. Just push the node onto the reduction stack.
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pushToStack(nId);
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s.recordR0();
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}
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/// \brief Apply rule R1.
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/// @param xnItr Node iterator for node to apply R1 to.
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///
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/// Node will be automatically pushed to the solver stack.
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void applyR1(Graph::NodeId xnId) {
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NodeData &nd = getSolverNodeData(xnId);
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assert(nd.getSolverDegree() == 1 &&
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"R1 applied to node with degree != 1.");
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Graph::EdgeId eId = *nd.solverEdgesBegin();
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const Matrix &eCosts = g.getEdgeCosts(eId);
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const Vector &xCosts = g.getNodeCosts(xnId);
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// Duplicate a little to avoid transposing matrices.
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if (xnId == g.getEdgeNode1(eId)) {
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Graph::NodeId ynId = g.getEdgeNode2(eId);
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Vector &yCosts = g.getNodeCosts(ynId);
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for (unsigned j = 0; j < yCosts.getLength(); ++j) {
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PBQPNum min = eCosts[0][j] + xCosts[0];
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for (unsigned i = 1; i < xCosts.getLength(); ++i) {
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PBQPNum c = eCosts[i][j] + xCosts[i];
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if (c < min)
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min = c;
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}
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yCosts[j] += min;
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}
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h.handleRemoveEdge(eId, ynId);
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} else {
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Graph::NodeId ynId = g.getEdgeNode1(eId);
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Vector &yCosts = g.getNodeCosts(ynId);
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for (unsigned i = 0; i < yCosts.getLength(); ++i) {
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PBQPNum min = eCosts[i][0] + xCosts[0];
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for (unsigned j = 1; j < xCosts.getLength(); ++j) {
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PBQPNum c = eCosts[i][j] + xCosts[j];
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if (c < min)
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min = c;
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}
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yCosts[i] += min;
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}
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h.handleRemoveEdge(eId, ynId);
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}
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removeSolverEdge(eId);
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assert(nd.getSolverDegree() == 0 &&
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"Degree 1 with edge removed should be 0.");
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pushToStack(xnId);
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s.recordR1();
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}
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/// \brief Apply rule R2.
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/// @param xnItr Node iterator for node to apply R2 to.
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///
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/// Node will be automatically pushed to the solver stack.
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void applyR2(Graph::NodeId xnId) {
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assert(getSolverNodeData(xnId).getSolverDegree() == 2 &&
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"R2 applied to node with degree != 2.");
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NodeData &nd = getSolverNodeData(xnId);
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const Vector &xCosts = g.getNodeCosts(xnId);
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SolverEdgeItr aeItr = nd.solverEdgesBegin();
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Graph::EdgeId yxeId = *aeItr,
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zxeId = *(++aeItr);
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Graph::NodeId ynId = g.getEdgeOtherNode(yxeId, xnId),
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znId = g.getEdgeOtherNode(zxeId, xnId);
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bool flipEdge1 = (g.getEdgeNode1(yxeId) == xnId),
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flipEdge2 = (g.getEdgeNode1(zxeId) == xnId);
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const Matrix *yxeCosts = flipEdge1 ?
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new Matrix(g.getEdgeCosts(yxeId).transpose()) :
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&g.getEdgeCosts(yxeId);
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const Matrix *zxeCosts = flipEdge2 ?
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new Matrix(g.getEdgeCosts(zxeId).transpose()) :
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&g.getEdgeCosts(zxeId);
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unsigned xLen = xCosts.getLength(),
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yLen = yxeCosts->getRows(),
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zLen = zxeCosts->getRows();
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Matrix delta(yLen, zLen);
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for (unsigned i = 0; i < yLen; ++i) {
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for (unsigned j = 0; j < zLen; ++j) {
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PBQPNum min = (*yxeCosts)[i][0] + (*zxeCosts)[j][0] + xCosts[0];
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for (unsigned k = 1; k < xLen; ++k) {
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PBQPNum c = (*yxeCosts)[i][k] + (*zxeCosts)[j][k] + xCosts[k];
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if (c < min) {
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min = c;
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}
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}
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delta[i][j] = min;
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}
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}
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if (flipEdge1)
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delete yxeCosts;
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if (flipEdge2)
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delete zxeCosts;
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Graph::EdgeId yzeId = g.findEdge(ynId, znId);
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bool addedEdge = false;
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if (yzeId == g.invalidEdgeId()) {
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yzeId = g.addEdge(ynId, znId, delta);
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addedEdge = true;
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} else {
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Matrix &yzeCosts = g.getEdgeCosts(yzeId);
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h.preUpdateEdgeCosts(yzeId);
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if (ynId == g.getEdgeNode1(yzeId)) {
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yzeCosts += delta;
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} else {
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yzeCosts += delta.transpose();
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}
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}
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bool nullCostEdge = tryNormaliseEdgeMatrix(yzeId);
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if (!addedEdge) {
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// If we modified the edge costs let the heuristic know.
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h.postUpdateEdgeCosts(yzeId);
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}
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if (nullCostEdge) {
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// If this edge ended up null remove it.
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if (!addedEdge) {
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// We didn't just add it, so we need to notify the heuristic
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// and remove it from the solver.
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h.handleRemoveEdge(yzeId, ynId);
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h.handleRemoveEdge(yzeId, znId);
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removeSolverEdge(yzeId);
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}
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g.removeEdge(yzeId);
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} else if (addedEdge) {
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// If the edge was added, and non-null, finish setting it up, add it to
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// the solver & notify heuristic.
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edgeDataList.push_back(EdgeData());
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g.setEdgeData(yzeId, &edgeDataList.back());
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addSolverEdge(yzeId);
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h.handleAddEdge(yzeId);
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}
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h.handleRemoveEdge(yxeId, ynId);
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removeSolverEdge(yxeId);
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h.handleRemoveEdge(zxeId, znId);
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removeSolverEdge(zxeId);
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pushToStack(xnId);
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s.recordR2();
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}
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/// \brief Record an application of the RN rule.
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///
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/// For use by the HeuristicBase.
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void recordRN() { s.recordRN(); }
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private:
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NodeData& getSolverNodeData(Graph::NodeId nId) {
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return *static_cast<NodeData*>(g.getNodeData(nId));
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}
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EdgeData& getSolverEdgeData(Graph::EdgeId eId) {
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return *static_cast<EdgeData*>(g.getEdgeData(eId));
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}
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void addSolverEdge(Graph::EdgeId eId) {
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EdgeData &eData = getSolverEdgeData(eId);
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NodeData &n1Data = getSolverNodeData(g.getEdgeNode1(eId)),
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&n2Data = getSolverNodeData(g.getEdgeNode2(eId));
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eData.setN1SolverEdgeItr(n1Data.addSolverEdge(eId));
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eData.setN2SolverEdgeItr(n2Data.addSolverEdge(eId));
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}
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void setup() {
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if (h.solverRunSimplify()) {
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simplify();
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}
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// Create node data objects.
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for (Graph::NodeItr nItr = g.nodesBegin(), nEnd = g.nodesEnd();
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nItr != nEnd; ++nItr) {
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nodeDataList.push_back(NodeData());
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g.setNodeData(*nItr, &nodeDataList.back());
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}
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// Create edge data objects.
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for (Graph::EdgeItr eItr = g.edgesBegin(), eEnd = g.edgesEnd();
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eItr != eEnd; ++eItr) {
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edgeDataList.push_back(EdgeData());
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g.setEdgeData(*eItr, &edgeDataList.back());
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addSolverEdge(*eItr);
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}
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}
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void simplify() {
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disconnectTrivialNodes();
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eliminateIndependentEdges();
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}
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// Eliminate trivial nodes.
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void disconnectTrivialNodes() {
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unsigned numDisconnected = 0;
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for (Graph::NodeItr nItr = g.nodesBegin(), nEnd = g.nodesEnd();
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nItr != nEnd; ++nItr) {
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Graph::NodeId nId = *nItr;
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if (g.getNodeCosts(nId).getLength() == 1) {
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std::vector<Graph::EdgeId> edgesToRemove;
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for (Graph::AdjEdgeItr aeItr = g.adjEdgesBegin(nId),
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aeEnd = g.adjEdgesEnd(nId);
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aeItr != aeEnd; ++aeItr) {
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Graph::EdgeId eId = *aeItr;
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if (g.getEdgeNode1(eId) == nId) {
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Graph::NodeId otherNodeId = g.getEdgeNode2(eId);
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g.getNodeCosts(otherNodeId) +=
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g.getEdgeCosts(eId).getRowAsVector(0);
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}
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else {
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Graph::NodeId otherNodeId = g.getEdgeNode1(eId);
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g.getNodeCosts(otherNodeId) +=
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g.getEdgeCosts(eId).getColAsVector(0);
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}
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edgesToRemove.push_back(eId);
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}
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if (!edgesToRemove.empty())
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++numDisconnected;
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while (!edgesToRemove.empty()) {
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g.removeEdge(edgesToRemove.back());
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edgesToRemove.pop_back();
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}
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}
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}
|
|
}
|
|
|
|
void eliminateIndependentEdges() {
|
|
std::vector<Graph::EdgeId> edgesToProcess;
|
|
unsigned numEliminated = 0;
|
|
|
|
for (Graph::EdgeItr eItr = g.edgesBegin(), eEnd = g.edgesEnd();
|
|
eItr != eEnd; ++eItr) {
|
|
edgesToProcess.push_back(*eItr);
|
|
}
|
|
|
|
while (!edgesToProcess.empty()) {
|
|
if (tryToEliminateEdge(edgesToProcess.back()))
|
|
++numEliminated;
|
|
edgesToProcess.pop_back();
|
|
}
|
|
}
|
|
|
|
bool tryToEliminateEdge(Graph::EdgeId eId) {
|
|
if (tryNormaliseEdgeMatrix(eId)) {
|
|
g.removeEdge(eId);
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
bool tryNormaliseEdgeMatrix(Graph::EdgeId &eId) {
|
|
|
|
const PBQPNum infinity = std::numeric_limits<PBQPNum>::infinity();
|
|
|
|
Matrix &edgeCosts = g.getEdgeCosts(eId);
|
|
Vector &uCosts = g.getNodeCosts(g.getEdgeNode1(eId)),
|
|
&vCosts = g.getNodeCosts(g.getEdgeNode2(eId));
|
|
|
|
for (unsigned r = 0; r < edgeCosts.getRows(); ++r) {
|
|
PBQPNum rowMin = infinity;
|
|
|
|
for (unsigned c = 0; c < edgeCosts.getCols(); ++c) {
|
|
if (vCosts[c] != infinity && edgeCosts[r][c] < rowMin)
|
|
rowMin = edgeCosts[r][c];
|
|
}
|
|
|
|
uCosts[r] += rowMin;
|
|
|
|
if (rowMin != infinity) {
|
|
edgeCosts.subFromRow(r, rowMin);
|
|
}
|
|
else {
|
|
edgeCosts.setRow(r, 0);
|
|
}
|
|
}
|
|
|
|
for (unsigned c = 0; c < edgeCosts.getCols(); ++c) {
|
|
PBQPNum colMin = infinity;
|
|
|
|
for (unsigned r = 0; r < edgeCosts.getRows(); ++r) {
|
|
if (uCosts[r] != infinity && edgeCosts[r][c] < colMin)
|
|
colMin = edgeCosts[r][c];
|
|
}
|
|
|
|
vCosts[c] += colMin;
|
|
|
|
if (colMin != infinity) {
|
|
edgeCosts.subFromCol(c, colMin);
|
|
}
|
|
else {
|
|
edgeCosts.setCol(c, 0);
|
|
}
|
|
}
|
|
|
|
return edgeCosts.isZero();
|
|
}
|
|
|
|
void backpropagate() {
|
|
while (!stack.empty()) {
|
|
computeSolution(stack.back());
|
|
stack.pop_back();
|
|
}
|
|
}
|
|
|
|
void computeSolution(Graph::NodeId nId) {
|
|
|
|
NodeData &nodeData = getSolverNodeData(nId);
|
|
|
|
Vector v(g.getNodeCosts(nId));
|
|
|
|
// Solve based on existing solved edges.
|
|
for (SolverEdgeItr solvedEdgeItr = nodeData.solverEdgesBegin(),
|
|
solvedEdgeEnd = nodeData.solverEdgesEnd();
|
|
solvedEdgeItr != solvedEdgeEnd; ++solvedEdgeItr) {
|
|
|
|
Graph::EdgeId eId(*solvedEdgeItr);
|
|
Matrix &edgeCosts = g.getEdgeCosts(eId);
|
|
|
|
if (nId == g.getEdgeNode1(eId)) {
|
|
Graph::NodeId adjNode(g.getEdgeNode2(eId));
|
|
unsigned adjSolution = s.getSelection(adjNode);
|
|
v += edgeCosts.getColAsVector(adjSolution);
|
|
}
|
|
else {
|
|
Graph::NodeId adjNode(g.getEdgeNode1(eId));
|
|
unsigned adjSolution = s.getSelection(adjNode);
|
|
v += edgeCosts.getRowAsVector(adjSolution);
|
|
}
|
|
|
|
}
|
|
|
|
setSolution(nId, v.minIndex());
|
|
}
|
|
|
|
void cleanup() {
|
|
h.cleanup();
|
|
nodeDataList.clear();
|
|
edgeDataList.clear();
|
|
}
|
|
};
|
|
|
|
/// \brief PBQP heuristic solver class.
|
|
///
|
|
/// Given a PBQP Graph g representing a PBQP problem, you can find a solution
|
|
/// by calling
|
|
/// <tt>Solution s = HeuristicSolver<H>::solve(g);</tt>
|
|
///
|
|
/// The choice of heuristic for the H parameter will affect both the solver
|
|
/// speed and solution quality. The heuristic should be chosen based on the
|
|
/// nature of the problem being solved.
|
|
/// Currently the only solver included with LLVM is the Briggs heuristic for
|
|
/// register allocation.
|
|
template <typename HImpl>
|
|
class HeuristicSolver {
|
|
public:
|
|
static Solution solve(Graph &g) {
|
|
HeuristicSolverImpl<HImpl> hs(g);
|
|
return hs.computeSolution();
|
|
}
|
|
};
|
|
|
|
}
|
|
|
|
#endif // LLVM_CODEGEN_PBQP_HEURISTICSOLVER_H
|