mirror of
https://github.com/c64scene-ar/llvm-6502.git
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d0f6f01731
any command line paramater changed the register allocation produced by PBQP. Turns out variety is not the spice of life. Fixed some comparators, added others. All good now. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@108613 91177308-0d34-0410-b5e6-96231b3b80d8
461 lines
17 KiB
C++
461 lines
17 KiB
C++
//===-- Briggs.h --- Briggs Heuristic for PBQP ------------------*- 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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// This class implements the Briggs test for "allocability" of nodes in a
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// PBQP graph representing a register allocation problem. Nodes which can be
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// proven allocable (by a safe and relatively accurate test) are removed from
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// the PBQP graph first. If no provably allocable node is present in the graph
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// then the node with the minimal spill-cost to degree ratio is removed.
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//
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//===----------------------------------------------------------------------===//
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#ifndef LLVM_CODEGEN_PBQP_HEURISTICS_BRIGGS_H
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#define LLVM_CODEGEN_PBQP_HEURISTICS_BRIGGS_H
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#include "../HeuristicSolver.h"
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#include "../HeuristicBase.h"
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#include <set>
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#include <limits>
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namespace PBQP {
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namespace Heuristics {
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/// \brief PBQP Heuristic which applies an allocability test based on
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/// Briggs.
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///
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/// This heuristic assumes that the elements of cost vectors in the PBQP
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/// problem represent storage options, with the first being the spill
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/// option and subsequent elements representing legal registers for the
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/// corresponding node. Edge cost matrices are likewise assumed to represent
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/// register constraints.
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/// If one or more nodes can be proven allocable by this heuristic (by
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/// inspection of their constraint matrices) then the allocable node of
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/// highest degree is selected for the next reduction and pushed to the
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/// solver stack. If no nodes can be proven allocable then the node with
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/// the lowest estimated spill cost is selected and push to the solver stack
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/// instead.
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///
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/// This implementation is built on top of HeuristicBase.
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class Briggs : public HeuristicBase<Briggs> {
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private:
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class LinkDegreeComparator {
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public:
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LinkDegreeComparator(HeuristicSolverImpl<Briggs> &s) : s(&s) {}
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bool operator()(Graph::NodeItr n1Itr, Graph::NodeItr n2Itr) const {
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if (s->getSolverDegree(n1Itr) > s->getSolverDegree(n2Itr))
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return true;
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return false;
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}
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private:
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HeuristicSolverImpl<Briggs> *s;
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};
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class SpillCostComparator {
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public:
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SpillCostComparator(HeuristicSolverImpl<Briggs> &s)
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: s(&s), g(&s.getGraph()) {}
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bool operator()(Graph::NodeItr n1Itr, Graph::NodeItr n2Itr) const {
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PBQPNum cost1 = g->getNodeCosts(n1Itr)[0] / s->getSolverDegree(n1Itr),
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cost2 = g->getNodeCosts(n2Itr)[0] / s->getSolverDegree(n2Itr);
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if (cost1 < cost2)
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return true;
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return false;
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}
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private:
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HeuristicSolverImpl<Briggs> *s;
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Graph *g;
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};
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typedef std::list<Graph::NodeItr> RNAllocableList;
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typedef RNAllocableList::iterator RNAllocableListItr;
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typedef std::list<Graph::NodeItr> RNUnallocableList;
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typedef RNUnallocableList::iterator RNUnallocableListItr;
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public:
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struct NodeData {
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typedef std::vector<unsigned> UnsafeDegreesArray;
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bool isHeuristic, isAllocable, isInitialized;
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unsigned numDenied, numSafe;
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UnsafeDegreesArray unsafeDegrees;
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RNAllocableListItr rnaItr;
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RNUnallocableListItr rnuItr;
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NodeData()
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: isHeuristic(false), isAllocable(false), isInitialized(false),
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numDenied(0), numSafe(0) { }
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};
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struct EdgeData {
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typedef std::vector<unsigned> UnsafeArray;
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unsigned worst, reverseWorst;
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UnsafeArray unsafe, reverseUnsafe;
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bool isUpToDate;
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EdgeData() : worst(0), reverseWorst(0), isUpToDate(false) {}
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};
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/// \brief Construct an instance of the Briggs heuristic.
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/// @param solver A reference to the solver which is using this heuristic.
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Briggs(HeuristicSolverImpl<Briggs> &solver) :
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HeuristicBase<Briggs>(solver) {}
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/// \brief Determine whether a node should be reduced using optimal
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/// reduction.
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/// @param nItr Node iterator to be considered.
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/// @return True if the given node should be optimally reduced, false
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/// otherwise.
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///
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/// Selects nodes of degree 0, 1 or 2 for optimal reduction, with one
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/// exception. Nodes whose spill cost (element 0 of their cost vector) is
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/// infinite are checked for allocability first. Allocable nodes may be
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/// optimally reduced, but nodes whose allocability cannot be proven are
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/// selected for heuristic reduction instead.
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bool shouldOptimallyReduce(Graph::NodeItr nItr) {
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if (getSolver().getSolverDegree(nItr) < 3) {
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return true;
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}
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// else
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return false;
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}
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/// \brief Add a node to the heuristic reduce list.
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/// @param nItr Node iterator to add to the heuristic reduce list.
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void addToHeuristicReduceList(Graph::NodeItr nItr) {
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NodeData &nd = getHeuristicNodeData(nItr);
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initializeNode(nItr);
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nd.isHeuristic = true;
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if (nd.isAllocable) {
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nd.rnaItr = rnAllocableList.insert(rnAllocableList.end(), nItr);
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} else {
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nd.rnuItr = rnUnallocableList.insert(rnUnallocableList.end(), nItr);
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}
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}
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/// \brief Heuristically reduce one of the nodes in the heuristic
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/// reduce list.
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/// @return True if a reduction takes place, false if the heuristic reduce
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/// list is empty.
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///
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/// If the list of allocable nodes is non-empty a node is selected
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/// from it and pushed to the stack. Otherwise if the non-allocable list
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/// is non-empty a node is selected from it and pushed to the stack.
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/// If both lists are empty the method simply returns false with no action
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/// taken.
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bool heuristicReduce() {
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if (!rnAllocableList.empty()) {
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RNAllocableListItr rnaItr =
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min_element(rnAllocableList.begin(), rnAllocableList.end(),
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LinkDegreeComparator(getSolver()));
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Graph::NodeItr nItr = *rnaItr;
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rnAllocableList.erase(rnaItr);
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handleRemoveNode(nItr);
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getSolver().pushToStack(nItr);
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return true;
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} else if (!rnUnallocableList.empty()) {
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RNUnallocableListItr rnuItr =
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min_element(rnUnallocableList.begin(), rnUnallocableList.end(),
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SpillCostComparator(getSolver()));
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Graph::NodeItr nItr = *rnuItr;
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rnUnallocableList.erase(rnuItr);
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handleRemoveNode(nItr);
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getSolver().pushToStack(nItr);
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return true;
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}
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// else
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return false;
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}
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/// \brief Prepare a change in the costs on the given edge.
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/// @param eItr Edge iterator.
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void preUpdateEdgeCosts(Graph::EdgeItr eItr) {
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Graph &g = getGraph();
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Graph::NodeItr n1Itr = g.getEdgeNode1(eItr),
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n2Itr = g.getEdgeNode2(eItr);
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NodeData &n1 = getHeuristicNodeData(n1Itr),
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&n2 = getHeuristicNodeData(n2Itr);
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if (n1.isHeuristic)
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subtractEdgeContributions(eItr, getGraph().getEdgeNode1(eItr));
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if (n2.isHeuristic)
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subtractEdgeContributions(eItr, getGraph().getEdgeNode2(eItr));
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EdgeData &ed = getHeuristicEdgeData(eItr);
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ed.isUpToDate = false;
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}
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/// \brief Handle the change in the costs on the given edge.
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/// @param eItr Edge iterator.
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void postUpdateEdgeCosts(Graph::EdgeItr eItr) {
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// This is effectively the same as adding a new edge now, since
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// we've factored out the costs of the old one.
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handleAddEdge(eItr);
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}
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/// \brief Handle the addition of a new edge into the PBQP graph.
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/// @param eItr Edge iterator for the added edge.
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///
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/// Updates allocability of any nodes connected by this edge which are
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/// being managed by the heuristic. If allocability changes they are
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/// moved to the appropriate list.
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void handleAddEdge(Graph::EdgeItr eItr) {
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Graph &g = getGraph();
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Graph::NodeItr n1Itr = g.getEdgeNode1(eItr),
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n2Itr = g.getEdgeNode2(eItr);
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NodeData &n1 = getHeuristicNodeData(n1Itr),
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&n2 = getHeuristicNodeData(n2Itr);
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// If neither node is managed by the heuristic there's nothing to be
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// done.
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if (!n1.isHeuristic && !n2.isHeuristic)
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return;
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// Ok - we need to update at least one node.
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computeEdgeContributions(eItr);
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// Update node 1 if it's managed by the heuristic.
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if (n1.isHeuristic) {
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bool n1WasAllocable = n1.isAllocable;
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addEdgeContributions(eItr, n1Itr);
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updateAllocability(n1Itr);
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if (n1WasAllocable && !n1.isAllocable) {
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rnAllocableList.erase(n1.rnaItr);
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n1.rnuItr =
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rnUnallocableList.insert(rnUnallocableList.end(), n1Itr);
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}
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}
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// Likewise for node 2.
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if (n2.isHeuristic) {
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bool n2WasAllocable = n2.isAllocable;
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addEdgeContributions(eItr, n2Itr);
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updateAllocability(n2Itr);
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if (n2WasAllocable && !n2.isAllocable) {
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rnAllocableList.erase(n2.rnaItr);
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n2.rnuItr =
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rnUnallocableList.insert(rnUnallocableList.end(), n2Itr);
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}
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}
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}
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/// \brief Handle disconnection of an edge from a node.
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/// @param eItr Edge iterator for edge being disconnected.
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/// @param nItr Node iterator for the node being disconnected from.
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///
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/// Updates allocability of the given node and, if appropriate, moves the
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/// node to a new list.
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void handleRemoveEdge(Graph::EdgeItr eItr, Graph::NodeItr nItr) {
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NodeData &nd = getHeuristicNodeData(nItr);
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// If the node is not managed by the heuristic there's nothing to be
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// done.
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if (!nd.isHeuristic)
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return;
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EdgeData &ed = getHeuristicEdgeData(eItr);
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(void)ed;
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assert(ed.isUpToDate && "Edge data is not up to date.");
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// Update node.
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bool ndWasAllocable = nd.isAllocable;
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subtractEdgeContributions(eItr, nItr);
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updateAllocability(nItr);
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// If the node has gone optimal...
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if (shouldOptimallyReduce(nItr)) {
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nd.isHeuristic = false;
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addToOptimalReduceList(nItr);
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if (ndWasAllocable) {
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rnAllocableList.erase(nd.rnaItr);
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} else {
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rnUnallocableList.erase(nd.rnuItr);
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}
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} else {
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// Node didn't go optimal, but we might have to move it
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// from "unallocable" to "allocable".
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if (!ndWasAllocable && nd.isAllocable) {
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rnUnallocableList.erase(nd.rnuItr);
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nd.rnaItr = rnAllocableList.insert(rnAllocableList.end(), nItr);
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}
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}
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}
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private:
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NodeData& getHeuristicNodeData(Graph::NodeItr nItr) {
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return getSolver().getHeuristicNodeData(nItr);
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}
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EdgeData& getHeuristicEdgeData(Graph::EdgeItr eItr) {
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return getSolver().getHeuristicEdgeData(eItr);
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}
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// Work out what this edge will contribute to the allocability of the
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// nodes connected to it.
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void computeEdgeContributions(Graph::EdgeItr eItr) {
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EdgeData &ed = getHeuristicEdgeData(eItr);
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if (ed.isUpToDate)
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return; // Edge data is already up to date.
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Matrix &eCosts = getGraph().getEdgeCosts(eItr);
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unsigned numRegs = eCosts.getRows() - 1,
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numReverseRegs = eCosts.getCols() - 1;
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std::vector<unsigned> rowInfCounts(numRegs, 0),
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colInfCounts(numReverseRegs, 0);
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ed.worst = 0;
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ed.reverseWorst = 0;
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ed.unsafe.clear();
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ed.unsafe.resize(numRegs, 0);
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ed.reverseUnsafe.clear();
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ed.reverseUnsafe.resize(numReverseRegs, 0);
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for (unsigned i = 0; i < numRegs; ++i) {
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for (unsigned j = 0; j < numReverseRegs; ++j) {
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if (eCosts[i + 1][j + 1] ==
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std::numeric_limits<PBQPNum>::infinity()) {
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ed.unsafe[i] = 1;
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ed.reverseUnsafe[j] = 1;
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++rowInfCounts[i];
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++colInfCounts[j];
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if (colInfCounts[j] > ed.worst) {
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ed.worst = colInfCounts[j];
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}
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if (rowInfCounts[i] > ed.reverseWorst) {
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ed.reverseWorst = rowInfCounts[i];
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}
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}
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}
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}
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ed.isUpToDate = true;
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}
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// Add the contributions of the given edge to the given node's
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// numDenied and safe members. No action is taken other than to update
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// these member values. Once updated these numbers can be used by clients
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// to update the node's allocability.
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void addEdgeContributions(Graph::EdgeItr eItr, Graph::NodeItr nItr) {
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EdgeData &ed = getHeuristicEdgeData(eItr);
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assert(ed.isUpToDate && "Using out-of-date edge numbers.");
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NodeData &nd = getHeuristicNodeData(nItr);
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unsigned numRegs = getGraph().getNodeCosts(nItr).getLength() - 1;
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bool nIsNode1 = nItr == getGraph().getEdgeNode1(eItr);
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EdgeData::UnsafeArray &unsafe =
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nIsNode1 ? ed.unsafe : ed.reverseUnsafe;
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nd.numDenied += nIsNode1 ? ed.worst : ed.reverseWorst;
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for (unsigned r = 0; r < numRegs; ++r) {
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if (unsafe[r]) {
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if (nd.unsafeDegrees[r]==0) {
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--nd.numSafe;
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}
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++nd.unsafeDegrees[r];
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}
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}
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}
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// Subtract the contributions of the given edge to the given node's
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// numDenied and safe members. No action is taken other than to update
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// these member values. Once updated these numbers can be used by clients
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// to update the node's allocability.
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void subtractEdgeContributions(Graph::EdgeItr eItr, Graph::NodeItr nItr) {
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EdgeData &ed = getHeuristicEdgeData(eItr);
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assert(ed.isUpToDate && "Using out-of-date edge numbers.");
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NodeData &nd = getHeuristicNodeData(nItr);
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unsigned numRegs = getGraph().getNodeCosts(nItr).getLength() - 1;
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bool nIsNode1 = nItr == getGraph().getEdgeNode1(eItr);
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EdgeData::UnsafeArray &unsafe =
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nIsNode1 ? ed.unsafe : ed.reverseUnsafe;
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nd.numDenied -= nIsNode1 ? ed.worst : ed.reverseWorst;
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for (unsigned r = 0; r < numRegs; ++r) {
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if (unsafe[r]) {
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if (nd.unsafeDegrees[r] == 1) {
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++nd.numSafe;
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}
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--nd.unsafeDegrees[r];
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}
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}
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}
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void updateAllocability(Graph::NodeItr nItr) {
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NodeData &nd = getHeuristicNodeData(nItr);
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unsigned numRegs = getGraph().getNodeCosts(nItr).getLength() - 1;
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nd.isAllocable = nd.numDenied < numRegs || nd.numSafe > 0;
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}
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void initializeNode(Graph::NodeItr nItr) {
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NodeData &nd = getHeuristicNodeData(nItr);
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if (nd.isInitialized)
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return; // Node data is already up to date.
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unsigned numRegs = getGraph().getNodeCosts(nItr).getLength() - 1;
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nd.numDenied = 0;
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nd.numSafe = numRegs;
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nd.unsafeDegrees.resize(numRegs, 0);
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typedef HeuristicSolverImpl<Briggs>::SolverEdgeItr SolverEdgeItr;
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for (SolverEdgeItr aeItr = getSolver().solverEdgesBegin(nItr),
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aeEnd = getSolver().solverEdgesEnd(nItr);
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aeItr != aeEnd; ++aeItr) {
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Graph::EdgeItr eItr = *aeItr;
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computeEdgeContributions(eItr);
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addEdgeContributions(eItr, nItr);
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}
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updateAllocability(nItr);
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nd.isInitialized = true;
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}
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void handleRemoveNode(Graph::NodeItr xnItr) {
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typedef HeuristicSolverImpl<Briggs>::SolverEdgeItr SolverEdgeItr;
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std::vector<Graph::EdgeItr> edgesToRemove;
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for (SolverEdgeItr aeItr = getSolver().solverEdgesBegin(xnItr),
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aeEnd = getSolver().solverEdgesEnd(xnItr);
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aeItr != aeEnd; ++aeItr) {
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Graph::NodeItr ynItr = getGraph().getEdgeOtherNode(*aeItr, xnItr);
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handleRemoveEdge(*aeItr, ynItr);
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edgesToRemove.push_back(*aeItr);
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}
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while (!edgesToRemove.empty()) {
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getSolver().removeSolverEdge(edgesToRemove.back());
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edgesToRemove.pop_back();
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}
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}
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RNAllocableList rnAllocableList;
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RNUnallocableList rnUnallocableList;
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};
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}
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}
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#endif // LLVM_CODEGEN_PBQP_HEURISTICS_BRIGGS_H
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