mirror of
https://github.com/c64scene-ar/llvm-6502.git
synced 2024-12-25 14:32:53 +00:00
ddcdcc8863
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@130068 91177308-0d34-0410-b5e6-96231b3b80d8
464 lines
17 KiB
C++
464 lines
17 KiB
C++
//===-- 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 "../HeuristicSolver.h"
|
|
#include "../HeuristicBase.h"
|
|
|
|
#include <limits>
|
|
|
|
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<Briggs> {
|
|
private:
|
|
|
|
class LinkDegreeComparator {
|
|
public:
|
|
LinkDegreeComparator(HeuristicSolverImpl<Briggs> &s) : s(&s) {}
|
|
bool operator()(Graph::NodeItr n1Itr, Graph::NodeItr n2Itr) const {
|
|
if (s->getSolverDegree(n1Itr) > s->getSolverDegree(n2Itr))
|
|
return true;
|
|
return false;
|
|
}
|
|
private:
|
|
HeuristicSolverImpl<Briggs> *s;
|
|
};
|
|
|
|
class SpillCostComparator {
|
|
public:
|
|
SpillCostComparator(HeuristicSolverImpl<Briggs> &s)
|
|
: s(&s), g(&s.getGraph()) {}
|
|
bool operator()(Graph::NodeItr n1Itr, Graph::NodeItr n2Itr) const {
|
|
const PBQP::Vector &cv1 = g->getNodeCosts(n1Itr);
|
|
const PBQP::Vector &cv2 = g->getNodeCosts(n2Itr);
|
|
|
|
PBQPNum cost1 = cv1[0] / s->getSolverDegree(n1Itr);
|
|
PBQPNum cost2 = cv2[0] / s->getSolverDegree(n2Itr);
|
|
|
|
if (cost1 < cost2)
|
|
return true;
|
|
return false;
|
|
}
|
|
|
|
private:
|
|
HeuristicSolverImpl<Briggs> *s;
|
|
Graph *g;
|
|
};
|
|
|
|
typedef std::list<Graph::NodeItr> RNAllocableList;
|
|
typedef RNAllocableList::iterator RNAllocableListItr;
|
|
|
|
typedef std::list<Graph::NodeItr> RNUnallocableList;
|
|
typedef RNUnallocableList::iterator RNUnallocableListItr;
|
|
|
|
public:
|
|
|
|
struct NodeData {
|
|
typedef std::vector<unsigned> 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<unsigned> 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<Briggs> &solver) :
|
|
HeuristicBase<Briggs>(solver) {}
|
|
|
|
/// \brief Determine whether a node should be reduced using optimal
|
|
/// reduction.
|
|
/// @param nItr Node iterator 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::NodeItr nItr) {
|
|
if (getSolver().getSolverDegree(nItr) < 3) {
|
|
return true;
|
|
}
|
|
// else
|
|
return false;
|
|
}
|
|
|
|
/// \brief Add a node to the heuristic reduce list.
|
|
/// @param nItr Node iterator to add to the heuristic reduce list.
|
|
void addToHeuristicReduceList(Graph::NodeItr nItr) {
|
|
NodeData &nd = getHeuristicNodeData(nItr);
|
|
initializeNode(nItr);
|
|
nd.isHeuristic = true;
|
|
if (nd.isAllocable) {
|
|
nd.rnaItr = rnAllocableList.insert(rnAllocableList.end(), nItr);
|
|
} else {
|
|
nd.rnuItr = rnUnallocableList.insert(rnUnallocableList.end(), nItr);
|
|
}
|
|
}
|
|
|
|
/// \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::NodeItr nItr = *rnaItr;
|
|
rnAllocableList.erase(rnaItr);
|
|
handleRemoveNode(nItr);
|
|
getSolver().pushToStack(nItr);
|
|
return true;
|
|
} else if (!rnUnallocableList.empty()) {
|
|
RNUnallocableListItr rnuItr =
|
|
min_element(rnUnallocableList.begin(), rnUnallocableList.end(),
|
|
SpillCostComparator(getSolver()));
|
|
Graph::NodeItr nItr = *rnuItr;
|
|
rnUnallocableList.erase(rnuItr);
|
|
handleRemoveNode(nItr);
|
|
getSolver().pushToStack(nItr);
|
|
return true;
|
|
}
|
|
// else
|
|
return false;
|
|
}
|
|
|
|
/// \brief Prepare a change in the costs on the given edge.
|
|
/// @param eItr Edge iterator.
|
|
void preUpdateEdgeCosts(Graph::EdgeItr eItr) {
|
|
Graph &g = getGraph();
|
|
Graph::NodeItr n1Itr = g.getEdgeNode1(eItr),
|
|
n2Itr = g.getEdgeNode2(eItr);
|
|
NodeData &n1 = getHeuristicNodeData(n1Itr),
|
|
&n2 = getHeuristicNodeData(n2Itr);
|
|
|
|
if (n1.isHeuristic)
|
|
subtractEdgeContributions(eItr, getGraph().getEdgeNode1(eItr));
|
|
if (n2.isHeuristic)
|
|
subtractEdgeContributions(eItr, getGraph().getEdgeNode2(eItr));
|
|
|
|
EdgeData &ed = getHeuristicEdgeData(eItr);
|
|
ed.isUpToDate = false;
|
|
}
|
|
|
|
/// \brief Handle the change in the costs on the given edge.
|
|
/// @param eItr Edge iterator.
|
|
void postUpdateEdgeCosts(Graph::EdgeItr eItr) {
|
|
// This is effectively the same as adding a new edge now, since
|
|
// we've factored out the costs of the old one.
|
|
handleAddEdge(eItr);
|
|
}
|
|
|
|
/// \brief Handle the addition of a new edge into the PBQP graph.
|
|
/// @param eItr Edge iterator 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::EdgeItr eItr) {
|
|
Graph &g = getGraph();
|
|
Graph::NodeItr n1Itr = g.getEdgeNode1(eItr),
|
|
n2Itr = g.getEdgeNode2(eItr);
|
|
NodeData &n1 = getHeuristicNodeData(n1Itr),
|
|
&n2 = getHeuristicNodeData(n2Itr);
|
|
|
|
// 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(eItr);
|
|
|
|
// Update node 1 if it's managed by the heuristic.
|
|
if (n1.isHeuristic) {
|
|
bool n1WasAllocable = n1.isAllocable;
|
|
addEdgeContributions(eItr, n1Itr);
|
|
updateAllocability(n1Itr);
|
|
if (n1WasAllocable && !n1.isAllocable) {
|
|
rnAllocableList.erase(n1.rnaItr);
|
|
n1.rnuItr =
|
|
rnUnallocableList.insert(rnUnallocableList.end(), n1Itr);
|
|
}
|
|
}
|
|
|
|
// Likewise for node 2.
|
|
if (n2.isHeuristic) {
|
|
bool n2WasAllocable = n2.isAllocable;
|
|
addEdgeContributions(eItr, n2Itr);
|
|
updateAllocability(n2Itr);
|
|
if (n2WasAllocable && !n2.isAllocable) {
|
|
rnAllocableList.erase(n2.rnaItr);
|
|
n2.rnuItr =
|
|
rnUnallocableList.insert(rnUnallocableList.end(), n2Itr);
|
|
}
|
|
}
|
|
}
|
|
|
|
/// \brief Handle disconnection of an edge from a node.
|
|
/// @param eItr Edge iterator for edge being disconnected.
|
|
/// @param nItr Node iterator 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::EdgeItr eItr, Graph::NodeItr nItr) {
|
|
NodeData &nd = getHeuristicNodeData(nItr);
|
|
|
|
// If the node is not managed by the heuristic there's nothing to be
|
|
// done.
|
|
if (!nd.isHeuristic)
|
|
return;
|
|
|
|
EdgeData &ed = getHeuristicEdgeData(eItr);
|
|
(void)ed;
|
|
assert(ed.isUpToDate && "Edge data is not up to date.");
|
|
|
|
// Update node.
|
|
bool ndWasAllocable = nd.isAllocable;
|
|
subtractEdgeContributions(eItr, nItr);
|
|
updateAllocability(nItr);
|
|
|
|
// If the node has gone optimal...
|
|
if (shouldOptimallyReduce(nItr)) {
|
|
nd.isHeuristic = false;
|
|
addToOptimalReduceList(nItr);
|
|
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(), nItr);
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
|
|
NodeData& getHeuristicNodeData(Graph::NodeItr nItr) {
|
|
return getSolver().getHeuristicNodeData(nItr);
|
|
}
|
|
|
|
EdgeData& getHeuristicEdgeData(Graph::EdgeItr eItr) {
|
|
return getSolver().getHeuristicEdgeData(eItr);
|
|
}
|
|
|
|
// Work out what this edge will contribute to the allocability of the
|
|
// nodes connected to it.
|
|
void computeEdgeContributions(Graph::EdgeItr eItr) {
|
|
EdgeData &ed = getHeuristicEdgeData(eItr);
|
|
|
|
if (ed.isUpToDate)
|
|
return; // Edge data is already up to date.
|
|
|
|
Matrix &eCosts = getGraph().getEdgeCosts(eItr);
|
|
|
|
unsigned numRegs = eCosts.getRows() - 1,
|
|
numReverseRegs = eCosts.getCols() - 1;
|
|
|
|
std::vector<unsigned> 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<PBQPNum>::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::EdgeItr eItr, Graph::NodeItr nItr) {
|
|
EdgeData &ed = getHeuristicEdgeData(eItr);
|
|
|
|
assert(ed.isUpToDate && "Using out-of-date edge numbers.");
|
|
|
|
NodeData &nd = getHeuristicNodeData(nItr);
|
|
unsigned numRegs = getGraph().getNodeCosts(nItr).getLength() - 1;
|
|
|
|
bool nIsNode1 = nItr == getGraph().getEdgeNode1(eItr);
|
|
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::EdgeItr eItr, Graph::NodeItr nItr) {
|
|
EdgeData &ed = getHeuristicEdgeData(eItr);
|
|
|
|
assert(ed.isUpToDate && "Using out-of-date edge numbers.");
|
|
|
|
NodeData &nd = getHeuristicNodeData(nItr);
|
|
unsigned numRegs = getGraph().getNodeCosts(nItr).getLength() - 1;
|
|
|
|
bool nIsNode1 = nItr == getGraph().getEdgeNode1(eItr);
|
|
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::NodeItr nItr) {
|
|
NodeData &nd = getHeuristicNodeData(nItr);
|
|
unsigned numRegs = getGraph().getNodeCosts(nItr).getLength() - 1;
|
|
nd.isAllocable = nd.numDenied < numRegs || nd.numSafe > 0;
|
|
}
|
|
|
|
void initializeNode(Graph::NodeItr nItr) {
|
|
NodeData &nd = getHeuristicNodeData(nItr);
|
|
|
|
if (nd.isInitialized)
|
|
return; // Node data is already up to date.
|
|
|
|
unsigned numRegs = getGraph().getNodeCosts(nItr).getLength() - 1;
|
|
|
|
nd.numDenied = 0;
|
|
nd.numSafe = numRegs;
|
|
nd.unsafeDegrees.resize(numRegs, 0);
|
|
|
|
typedef HeuristicSolverImpl<Briggs>::SolverEdgeItr SolverEdgeItr;
|
|
|
|
for (SolverEdgeItr aeItr = getSolver().solverEdgesBegin(nItr),
|
|
aeEnd = getSolver().solverEdgesEnd(nItr);
|
|
aeItr != aeEnd; ++aeItr) {
|
|
|
|
Graph::EdgeItr eItr = *aeItr;
|
|
computeEdgeContributions(eItr);
|
|
addEdgeContributions(eItr, nItr);
|
|
}
|
|
|
|
updateAllocability(nItr);
|
|
nd.isInitialized = true;
|
|
}
|
|
|
|
void handleRemoveNode(Graph::NodeItr xnItr) {
|
|
typedef HeuristicSolverImpl<Briggs>::SolverEdgeItr SolverEdgeItr;
|
|
std::vector<Graph::EdgeItr> edgesToRemove;
|
|
for (SolverEdgeItr aeItr = getSolver().solverEdgesBegin(xnItr),
|
|
aeEnd = getSolver().solverEdgesEnd(xnItr);
|
|
aeItr != aeEnd; ++aeItr) {
|
|
Graph::NodeItr ynItr = getGraph().getEdgeOtherNode(*aeItr, xnItr);
|
|
handleRemoveEdge(*aeItr, ynItr);
|
|
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
|