//===-- Briggs.h --- Briggs Heuristic for PBQP ------------------*- C++ -*-===// // // The LLVM Compiler Infrastructure // // This file is distributed under the University of Illinois Open Source // License. See LICENSE.TXT for details. // //===----------------------------------------------------------------------===// // // This class implements the Briggs test for "allocability" of nodes in a // PBQP graph representing a register allocation problem. Nodes which can be // proven allocable (by a safe and relatively accurate test) are removed from // the PBQP graph first. If no provably allocable node is present in the graph // then the node with the minimal spill-cost to degree ratio is removed. // //===----------------------------------------------------------------------===// #ifndef LLVM_CODEGEN_PBQP_HEURISTICS_BRIGGS_H #define LLVM_CODEGEN_PBQP_HEURISTICS_BRIGGS_H #include "../HeuristicBase.h" #include "../HeuristicSolver.h" #include namespace PBQP { namespace Heuristics { /// \brief PBQP Heuristic which applies an allocability test based on /// Briggs. /// /// This heuristic assumes that the elements of cost vectors in the PBQP /// problem represent storage options, with the first being the spill /// option and subsequent elements representing legal registers for the /// corresponding node. Edge cost matrices are likewise assumed to represent /// register constraints. /// If one or more nodes can be proven allocable by this heuristic (by /// inspection of their constraint matrices) then the allocable node of /// highest degree is selected for the next reduction and pushed to the /// solver stack. If no nodes can be proven allocable then the node with /// the lowest estimated spill cost is selected and push to the solver stack /// instead. /// /// This implementation is built on top of HeuristicBase. class Briggs : public HeuristicBase { private: class LinkDegreeComparator { public: LinkDegreeComparator(HeuristicSolverImpl &s) : s(&s) {} bool operator()(Graph::NodeId n1Id, Graph::NodeId n2Id) const { if (s->getSolverDegree(n1Id) > s->getSolverDegree(n2Id)) return true; return false; } private: HeuristicSolverImpl *s; }; class SpillCostComparator { public: SpillCostComparator(HeuristicSolverImpl &s) : s(&s), g(&s.getGraph()) {} bool operator()(Graph::NodeId n1Id, Graph::NodeId n2Id) const { const PBQP::Vector &cv1 = g->getNodeCosts(n1Id); const PBQP::Vector &cv2 = g->getNodeCosts(n2Id); PBQPNum cost1 = cv1[0] / s->getSolverDegree(n1Id); PBQPNum cost2 = cv2[0] / s->getSolverDegree(n2Id); if (cost1 < cost2) return true; return false; } private: HeuristicSolverImpl *s; Graph *g; }; typedef std::list RNAllocableList; typedef RNAllocableList::iterator RNAllocableListItr; typedef std::list RNUnallocableList; typedef RNUnallocableList::iterator RNUnallocableListItr; public: struct NodeData { typedef std::vector UnsafeDegreesArray; bool isHeuristic, isAllocable, isInitialized; unsigned numDenied, numSafe; UnsafeDegreesArray unsafeDegrees; RNAllocableListItr rnaItr; RNUnallocableListItr rnuItr; NodeData() : isHeuristic(false), isAllocable(false), isInitialized(false), numDenied(0), numSafe(0) { } }; struct EdgeData { typedef std::vector UnsafeArray; unsigned worst, reverseWorst; UnsafeArray unsafe, reverseUnsafe; bool isUpToDate; EdgeData() : worst(0), reverseWorst(0), isUpToDate(false) {} }; /// \brief Construct an instance of the Briggs heuristic. /// @param solver A reference to the solver which is using this heuristic. Briggs(HeuristicSolverImpl &solver) : HeuristicBase(solver) {} /// \brief Determine whether a node should be reduced using optimal /// reduction. /// @param nId Node id to be considered. /// @return True if the given node should be optimally reduced, false /// otherwise. /// /// Selects nodes of degree 0, 1 or 2 for optimal reduction, with one /// exception. Nodes whose spill cost (element 0 of their cost vector) is /// infinite are checked for allocability first. Allocable nodes may be /// optimally reduced, but nodes whose allocability cannot be proven are /// selected for heuristic reduction instead. bool shouldOptimallyReduce(Graph::NodeId nId) { if (getSolver().getSolverDegree(nId) < 3) { return true; } // else return false; } /// \brief Add a node to the heuristic reduce list. /// @param nId Node id to add to the heuristic reduce list. void addToHeuristicReduceList(Graph::NodeId nId) { NodeData &nd = getHeuristicNodeData(nId); initializeNode(nId); nd.isHeuristic = true; if (nd.isAllocable) { nd.rnaItr = rnAllocableList.insert(rnAllocableList.end(), nId); } else { nd.rnuItr = rnUnallocableList.insert(rnUnallocableList.end(), nId); } } /// \brief Heuristically reduce one of the nodes in the heuristic /// reduce list. /// @return True if a reduction takes place, false if the heuristic reduce /// list is empty. /// /// If the list of allocable nodes is non-empty a node is selected /// from it and pushed to the stack. Otherwise if the non-allocable list /// is non-empty a node is selected from it and pushed to the stack. /// If both lists are empty the method simply returns false with no action /// taken. bool heuristicReduce() { if (!rnAllocableList.empty()) { RNAllocableListItr rnaItr = min_element(rnAllocableList.begin(), rnAllocableList.end(), LinkDegreeComparator(getSolver())); Graph::NodeId nId = *rnaItr; rnAllocableList.erase(rnaItr); handleRemoveNode(nId); getSolver().pushToStack(nId); return true; } else if (!rnUnallocableList.empty()) { RNUnallocableListItr rnuItr = min_element(rnUnallocableList.begin(), rnUnallocableList.end(), SpillCostComparator(getSolver())); Graph::NodeId nId = *rnuItr; rnUnallocableList.erase(rnuItr); handleRemoveNode(nId); getSolver().pushToStack(nId); return true; } // else return false; } /// \brief Prepare a change in the costs on the given edge. /// @param eId Edge id. void preUpdateEdgeCosts(Graph::EdgeId eId) { Graph &g = getGraph(); Graph::NodeId n1Id = g.getEdgeNode1(eId), n2Id = g.getEdgeNode2(eId); NodeData &n1 = getHeuristicNodeData(n1Id), &n2 = getHeuristicNodeData(n2Id); if (n1.isHeuristic) subtractEdgeContributions(eId, getGraph().getEdgeNode1(eId)); if (n2.isHeuristic) subtractEdgeContributions(eId, getGraph().getEdgeNode2(eId)); EdgeData &ed = getHeuristicEdgeData(eId); ed.isUpToDate = false; } /// \brief Handle the change in the costs on the given edge. /// @param eId Edge id. void postUpdateEdgeCosts(Graph::EdgeId eId) { // This is effectively the same as adding a new edge now, since // we've factored out the costs of the old one. handleAddEdge(eId); } /// \brief Handle the addition of a new edge into the PBQP graph. /// @param eId Edge id for the added edge. /// /// Updates allocability of any nodes connected by this edge which are /// being managed by the heuristic. If allocability changes they are /// moved to the appropriate list. void handleAddEdge(Graph::EdgeId eId) { Graph &g = getGraph(); Graph::NodeId n1Id = g.getEdgeNode1(eId), n2Id = g.getEdgeNode2(eId); NodeData &n1 = getHeuristicNodeData(n1Id), &n2 = getHeuristicNodeData(n2Id); // If neither node is managed by the heuristic there's nothing to be // done. if (!n1.isHeuristic && !n2.isHeuristic) return; // Ok - we need to update at least one node. computeEdgeContributions(eId); // Update node 1 if it's managed by the heuristic. if (n1.isHeuristic) { bool n1WasAllocable = n1.isAllocable; addEdgeContributions(eId, n1Id); updateAllocability(n1Id); if (n1WasAllocable && !n1.isAllocable) { rnAllocableList.erase(n1.rnaItr); n1.rnuItr = rnUnallocableList.insert(rnUnallocableList.end(), n1Id); } } // Likewise for node 2. if (n2.isHeuristic) { bool n2WasAllocable = n2.isAllocable; addEdgeContributions(eId, n2Id); updateAllocability(n2Id); if (n2WasAllocable && !n2.isAllocable) { rnAllocableList.erase(n2.rnaItr); n2.rnuItr = rnUnallocableList.insert(rnUnallocableList.end(), n2Id); } } } /// \brief Handle disconnection of an edge from a node. /// @param eId Edge id for edge being disconnected. /// @param nId Node id for the node being disconnected from. /// /// Updates allocability of the given node and, if appropriate, moves the /// node to a new list. void handleRemoveEdge(Graph::EdgeId eId, Graph::NodeId nId) { NodeData &nd =getHeuristicNodeData(nId); // If the node is not managed by the heuristic there's nothing to be // done. if (!nd.isHeuristic) return; EdgeData &ed = getHeuristicEdgeData(eId); (void)ed; assert(ed.isUpToDate && "Edge data is not up to date."); // Update node. bool ndWasAllocable = nd.isAllocable; subtractEdgeContributions(eId, nId); updateAllocability(nId); // If the node has gone optimal... if (shouldOptimallyReduce(nId)) { nd.isHeuristic = false; addToOptimalReduceList(nId); if (ndWasAllocable) { rnAllocableList.erase(nd.rnaItr); } else { rnUnallocableList.erase(nd.rnuItr); } } else { // Node didn't go optimal, but we might have to move it // from "unallocable" to "allocable". if (!ndWasAllocable && nd.isAllocable) { rnUnallocableList.erase(nd.rnuItr); nd.rnaItr = rnAllocableList.insert(rnAllocableList.end(), nId); } } } private: NodeData& getHeuristicNodeData(Graph::NodeId nId) { return getSolver().getHeuristicNodeData(nId); } EdgeData& getHeuristicEdgeData(Graph::EdgeId eId) { return getSolver().getHeuristicEdgeData(eId); } // Work out what this edge will contribute to the allocability of the // nodes connected to it. void computeEdgeContributions(Graph::EdgeId eId) { EdgeData &ed = getHeuristicEdgeData(eId); if (ed.isUpToDate) return; // Edge data is already up to date. Matrix &eCosts = getGraph().getEdgeCosts(eId); unsigned numRegs = eCosts.getRows() - 1, numReverseRegs = eCosts.getCols() - 1; std::vector rowInfCounts(numRegs, 0), colInfCounts(numReverseRegs, 0); ed.worst = 0; ed.reverseWorst = 0; ed.unsafe.clear(); ed.unsafe.resize(numRegs, 0); ed.reverseUnsafe.clear(); ed.reverseUnsafe.resize(numReverseRegs, 0); for (unsigned i = 0; i < numRegs; ++i) { for (unsigned j = 0; j < numReverseRegs; ++j) { if (eCosts[i + 1][j + 1] == std::numeric_limits::infinity()) { ed.unsafe[i] = 1; ed.reverseUnsafe[j] = 1; ++rowInfCounts[i]; ++colInfCounts[j]; if (colInfCounts[j] > ed.worst) { ed.worst = colInfCounts[j]; } if (rowInfCounts[i] > ed.reverseWorst) { ed.reverseWorst = rowInfCounts[i]; } } } } ed.isUpToDate = true; } // Add the contributions of the given edge to the given node's // numDenied and safe members. No action is taken other than to update // these member values. Once updated these numbers can be used by clients // to update the node's allocability. void addEdgeContributions(Graph::EdgeId eId, Graph::NodeId nId) { EdgeData &ed = getHeuristicEdgeData(eId); assert(ed.isUpToDate && "Using out-of-date edge numbers."); NodeData &nd = getHeuristicNodeData(nId); unsigned numRegs = getGraph().getNodeCosts(nId).getLength() - 1; bool nIsNode1 = nId == getGraph().getEdgeNode1(eId); EdgeData::UnsafeArray &unsafe = nIsNode1 ? ed.unsafe : ed.reverseUnsafe; nd.numDenied += nIsNode1 ? ed.worst : ed.reverseWorst; for (unsigned r = 0; r < numRegs; ++r) { if (unsafe[r]) { if (nd.unsafeDegrees[r]==0) { --nd.numSafe; } ++nd.unsafeDegrees[r]; } } } // Subtract the contributions of the given edge to the given node's // numDenied and safe members. No action is taken other than to update // these member values. Once updated these numbers can be used by clients // to update the node's allocability. void subtractEdgeContributions(Graph::EdgeId eId, Graph::NodeId nId) { EdgeData &ed = getHeuristicEdgeData(eId); assert(ed.isUpToDate && "Using out-of-date edge numbers."); NodeData &nd = getHeuristicNodeData(nId); unsigned numRegs = getGraph().getNodeCosts(nId).getLength() - 1; bool nIsNode1 = nId == getGraph().getEdgeNode1(eId); EdgeData::UnsafeArray &unsafe = nIsNode1 ? ed.unsafe : ed.reverseUnsafe; nd.numDenied -= nIsNode1 ? ed.worst : ed.reverseWorst; for (unsigned r = 0; r < numRegs; ++r) { if (unsafe[r]) { if (nd.unsafeDegrees[r] == 1) { ++nd.numSafe; } --nd.unsafeDegrees[r]; } } } void updateAllocability(Graph::NodeId nId) { NodeData &nd = getHeuristicNodeData(nId); unsigned numRegs = getGraph().getNodeCosts(nId).getLength() - 1; nd.isAllocable = nd.numDenied < numRegs || nd.numSafe > 0; } void initializeNode(Graph::NodeId nId) { NodeData &nd = getHeuristicNodeData(nId); if (nd.isInitialized) return; // Node data is already up to date. unsigned numRegs = getGraph().getNodeCosts(nId).getLength() - 1; nd.numDenied = 0; const Vector& nCosts = getGraph().getNodeCosts(nId); for (unsigned i = 1; i < nCosts.getLength(); ++i) { if (nCosts[i] == std::numeric_limits::infinity()) ++nd.numDenied; } nd.numSafe = numRegs; nd.unsafeDegrees.resize(numRegs, 0); typedef HeuristicSolverImpl::SolverEdgeItr SolverEdgeItr; for (SolverEdgeItr aeItr = getSolver().solverEdgesBegin(nId), aeEnd = getSolver().solverEdgesEnd(nId); aeItr != aeEnd; ++aeItr) { Graph::EdgeId eId = *aeItr; computeEdgeContributions(eId); addEdgeContributions(eId, nId); } updateAllocability(nId); nd.isInitialized = true; } void handleRemoveNode(Graph::NodeId xnId) { typedef HeuristicSolverImpl::SolverEdgeItr SolverEdgeItr; std::vector edgesToRemove; for (SolverEdgeItr aeItr = getSolver().solverEdgesBegin(xnId), aeEnd = getSolver().solverEdgesEnd(xnId); aeItr != aeEnd; ++aeItr) { Graph::NodeId ynId = getGraph().getEdgeOtherNode(*aeItr, xnId); handleRemoveEdge(*aeItr, ynId); edgesToRemove.push_back(*aeItr); } while (!edgesToRemove.empty()) { getSolver().removeSolverEdge(edgesToRemove.back()); edgesToRemove.pop_back(); } } RNAllocableList rnAllocableList; RNUnallocableList rnUnallocableList; }; } } #endif // LLVM_CODEGEN_PBQP_HEURISTICS_BRIGGS_H