NodeIDAllocator.cpp

Go to the documentation of this file.

//===- NodeIDAllocator.cpp -- Allocates node IDs on request ------------------------//
 
#include <iomanip>
#include <iostream>
#include <queue>
#include <cmath>
 
#include "FastCluster/fastcluster.h"
#include "MemoryModel/PointerAnalysisImpl.h"
#include "Util/PTAStat.h"
#include "Util/NodeIDAllocator.h"
#include "SVFIR/SVFValue.h"
#include "SVFIR/SVFType.h"
#include "Util/SVFUtil.h"
#include "Util/Options.h"
 
namespace SVF
{
const NodeID NodeIDAllocator::blackHoleObjectId = 0;
const NodeID NodeIDAllocator::constantObjectId = 1;
const NodeID NodeIDAllocator::blackHolePointerId = 2;
const NodeID NodeIDAllocator::nullPointerId = 3;
 
NodeIDAllocator *NodeIDAllocator::allocator = nullptr;
 
NodeIDAllocator *NodeIDAllocator::get(void)
{
    if (allocator == nullptr)
    {
        allocator = new NodeIDAllocator();
    }
 
    return allocator;
}
 
void NodeIDAllocator::unset(void)
{
    if (allocator != nullptr)
    {
        delete allocator;
        allocator = nullptr;
    }
}
 
// Initialise counts to 4 because that's how many special nodes we have.
NodeIDAllocator::NodeIDAllocator(void)
    : numObjects(4), numValues(4), numSymbols(4), numNodes(4), strategy(Options::NodeAllocStrat())
{ }
 
NodeID NodeIDAllocator::allocateObjectId(void)
{
    NodeID id = 0;
    if (strategy == Strategy::DENSE)
    {
        // We allocate objects from 0(-ish, considering the special nodes) to # of objects.
        id = numObjects;
    }
    else if (strategy == Strategy::REVERSE_DENSE)
    {
        id = UINT_MAX - numObjects;
    }
    else if (strategy == Strategy::SEQ)
    {
        // Everything is sequential and intermixed.
        id = numNodes;
    }
    else if (strategy == Strategy::DBUG)
    {
        // Non-GEPs just grab the next available ID.
        // We may have "holes" because GEPs increment the total
        // but allocate far away. This is not a problem because
        // we don't care about the relative distances between nodes.
        id = numNodes;
    }
    else
    {
        assert(false && "NodeIDAllocator::allocateObjectId: unimplemented node allocation strategy.");
    }
 
    increaseNumOfObjAndNodes();
 
    assert(id != 0 && "NodeIDAllocator::allocateObjectId: ID not allocated");
    return id;
}
 
NodeID NodeIDAllocator::allocateGepObjectId(NodeID base, u32_t offset, u32_t maxFieldLimit)
{
    NodeID id = 0;
    if (strategy == Strategy::DENSE)
    {
        // Nothing different to the other case.
        id =  numObjects;
    }
    else if (strategy == Strategy::REVERSE_DENSE)
    {
        id = UINT_MAX - numObjects;
    }
    else if (strategy == Strategy::SEQ)
    {
        // Everything is sequential and intermixed.
        id = numNodes;
    }
    else if (strategy == Strategy::DBUG)
    {
        // For a gep id, base id is set at lower bits, and offset is set at higher bits
        // e.g., 1100050 denotes base=50 and offset=10
        // The offset is 10, not 11, because we add 1 to the offset to ensure that the
        // high bits are never 0. For example, we do not want the gep id to be 50 when
        // the base is 50 and the offset is 0.
        NodeID gepMultiplier = pow(10, ceil(log10(
                                                numSymbols > maxFieldLimit ?
                                                numSymbols : maxFieldLimit
                                            )));
        id = (offset + 1) * gepMultiplier + base;
        assert(id > numSymbols && "NodeIDAllocator::allocateGepObjectId: GEP allocation clashing with other nodes");
    }
    else
    {
        assert(false && "NodeIDAllocator::allocateGepObjectId: unimplemented node allocation strategy");
    }
 
    increaseNumOfObjAndNodes();
 
    assert(id != 0 && "NodeIDAllocator::allocateGepObjectId: ID not allocated");
    return id;
}
 
NodeID NodeIDAllocator::allocateValueId(void)
{
    NodeID id = 0;
    if (strategy == Strategy::DENSE)
    {
        // We allocate values from UINT_MAX to UINT_MAX - # of values.
        // TODO: UINT_MAX does not allow for an easily changeable type
        //       of NodeID (though it is already in use elsewhere).
        id = UINT_MAX - numValues;
    }
    else if (strategy == Strategy::REVERSE_DENSE)
    {
        id = numValues;
    }
    else if (strategy == Strategy::SEQ)
    {
        // Everything is sequential and intermixed.
        id = numNodes;
    }
    else if (strategy == Strategy::DBUG)
    {
        id = numNodes;
    }
    else
    {
        assert(false && "NodeIDAllocator::allocateValueId: unimplemented node allocation strategy");
    }
 
    ++numValues;
    ++numNodes;
 
    assert(id != 0 && "NodeIDAllocator::allocateValueId: ID not allocated");
    return id;
}
 
NodeID NodeIDAllocator::endSymbolAllocation(void)
{
    numSymbols = numNodes;
    return numSymbols;
}
 
const std::string NodeIDAllocator::Clusterer::NumObjects = "NumObjects";
const std::string NodeIDAllocator::Clusterer::RegioningTime = "RegioningTime";
const std::string NodeIDAllocator::Clusterer::DistanceMatrixTime = "DistanceMatrixTime";
const std::string NodeIDAllocator::Clusterer::FastClusterTime = "FastClusterTime";
const std::string NodeIDAllocator::Clusterer::DendrogramTraversalTime = "DendrogramTravTime";
const std::string NodeIDAllocator::Clusterer::EvalTime = "EvalTime";
const std::string NodeIDAllocator::Clusterer::TotalTime = "TotalTime";
const std::string NodeIDAllocator::Clusterer::TheoreticalNumWords = "TheoreticalWords";
const std::string NodeIDAllocator::Clusterer::OriginalBvNumWords = "OriginalBvWords";
const std::string NodeIDAllocator::Clusterer::OriginalSbvNumWords = "OriginalSbvWords";
const std::string NodeIDAllocator::Clusterer::NewBvNumWords = "NewBvWords";
const std::string NodeIDAllocator::Clusterer::NewSbvNumWords = "NewSbvWords";
const std::string NodeIDAllocator::Clusterer::NumRegions = "NumRegions";
const std::string NodeIDAllocator::Clusterer::NumGtIntRegions = "NumGtIntRegions";
const std::string NodeIDAllocator::Clusterer::LargestRegion = "LargestRegion";
const std::string NodeIDAllocator::Clusterer::BestCandidate = "BestCandidate";
const std::string NodeIDAllocator::Clusterer::NumNonTrivialRegionObjects = "NumNonTrivObj";
 
std::vector<NodeID> NodeIDAllocator::Clusterer::cluster(BVDataPTAImpl *pta, const std::vector<std::pair<NodeID, unsigned>> keys, std::vector<std::pair<hclust_fast_methods, std::vector<NodeID>>> &candidates, std::string evalSubtitle)
{
    assert(pta != nullptr && "Clusterer::cluster: given null BVDataPTAImpl");
    assert(Options::NodeAllocStrat() == Strategy::DENSE && "Clusterer::cluster: only dense allocation clustering currently supported");
 
    Map<std::string, std::string> overallStats;
    double fastClusterTime = 0.0;
    double distanceMatrixTime = 0.0;
    double dendrogramTraversalTime = 0.0;
    double regioningTime = 0.0;
    double evalTime = 0.0;
 
    // Pair of nodes to their (minimum) distance and the number of occurrences of that distance.
    Map<std::pair<NodeID, NodeID>, std::pair<unsigned, unsigned>> distances;
 
    double clkStart = PTAStat::getClk(true);
 
    // Map points-to sets to occurrences.
    Map<PointsTo, unsigned> pointsToSets;
 
    // Objects each object shares at least a points-to set with.
    Map<NodeID, Set<NodeID>> coPointeeGraph;
    for (const std::pair<NodeID, unsigned> &keyOcc : keys)
    {
        const PointsTo &pts = pta->getPts(keyOcc.first);
        const size_t oldSize = pointsToSets.size();
        pointsToSets[pts] += keyOcc.second;;
 
        // Edges in this graph have no weight or uniqueness, so we only need to
        // do this for each points-to set once.
        if (oldSize != pointsToSets.size())
        {
            NodeID firstO = !pts.empty() ? *(pts.begin()) : 0;
            Set<NodeID> &firstOsNeighbours = coPointeeGraph[firstO];
            for (const NodeID o : pts)
            {
                if (o != firstO)
                {
                    firstOsNeighbours.insert(o);
                    coPointeeGraph[o].insert(firstO);
                }
            }
        }
    }
 
    size_t numObjects = NodeIDAllocator::get()->numObjects;
    overallStats[NumObjects] = std::to_string(numObjects);
 
    size_t numRegions = 0;
    std::vector<unsigned> objectsRegion;
    if (Options::RegionedClustering())
    {
        objectsRegion = regionObjects(coPointeeGraph, numObjects, numRegions);
    }
    else
    {
        // Just a single big region (0).
        objectsRegion.insert(objectsRegion.end(), numObjects, 0);
        numRegions = 1;
    }
 
    // Set needs to be ordered because getDistanceMatrix, in its n^2 iteration, expects
    // sets to be ordered (we are building a condensed matrix, not a full matrix, so it
    // matters). In getDistanceMatrix, doing regionReverseMapping for oi and oj, where
    // oi < oj, and getting a result moi > moj gives incorrect results.
    // In the condensed matrix, [b][a] where b >= a, is incorrect.
    std::vector<OrderedSet<NodeID>> regionsObjects(numRegions);
    for (NodeID o = 0; o < numObjects; ++o) regionsObjects[objectsRegion[o]].insert(o);
 
    // Size of the return node mapping. It is potentially larger than the number of
    // objects because we align each region to NATIVE_INT_SIZE.
    // size_t numMappings = 0;
 
    // Maps a region to a mapping which maps 0 to n to all objects
    // in that region.
    std::vector<std::vector<NodeID>> regionMappings(numRegions);
    // The reverse: region to mapping of objects to a 0 to n from above.
    std::vector<Map<NodeID, unsigned>> regionReverseMappings(numRegions);
    // We can thus use 0 to n for each region to create smaller distance matrices.
    for (unsigned region = 0; region < numRegions; ++region)
    {
        size_t curr = 0;
        // With the OrderedSet above, o1 < o2 => map[o1] < map[o2].
        for (NodeID o : regionsObjects[region])
        {
            // push_back here is just like p...[region][curr] = o.
            regionMappings[region].push_back(o);
            regionReverseMappings[region][o] = curr++;
        }
 
        // curr is the number of objects. A region with no objects makes no sense.
        assert(curr != 0);
 
        // Number of bits needed for this region if we were
        // to start assigning from 0 rounded up to the fewest needed
        // native ints. This is added to the number of mappings since
        // we align each region to a native int.
        // numMappings += requiredBits(regionsObjects[region].size());
    }
 
    // Points-to sets which are relevant to a region, i.e., those whose elements
    // belong to that region. Pair is for occurrences.
    std::vector<std::vector<std::pair<const PointsTo *, unsigned>>> regionsPointsTos(numRegions);
    for (const Map<PointsTo, unsigned>::value_type &ptocc : pointsToSets)
    {
        const PointsTo &pt = ptocc.first;
        const unsigned occ = ptocc.second;
        if (pt.empty()) continue;
        // Guaranteed that begin() != end() because of the continue above. All objects in pt
        // will be relevant to the same region.
        unsigned region = objectsRegion[*(pt.begin())];
        // In our "graph", objects in the same points-to set have an edge between them,
        // so they are all in the same connected component/region.
        regionsPointsTos[region].push_back(std::make_pair(&pt, occ));
    }
 
    double clkEnd = PTAStat::getClk(true);
    regioningTime = (clkEnd - clkStart) / TIMEINTERVAL;
    overallStats[RegioningTime] = std::to_string(regioningTime);
    overallStats[NumRegions] = std::to_string(numRegions);
 
    std::vector<hclust_fast_methods> methods;
    if ((enum hclust_fast_methods)Options::ClusterMethod() == HCLUST_METHOD_SVF_BEST)
    {
        methods.push_back(HCLUST_METHOD_SINGLE);
        methods.push_back(HCLUST_METHOD_COMPLETE);
        methods.push_back(HCLUST_METHOD_AVERAGE);
    }
    else
    {
        methods.push_back((enum hclust_fast_methods)Options::ClusterMethod());
    }
 
    for (const hclust_fast_methods method : methods)
    {
        std::vector<NodeID> nodeMap(numObjects, UINT_MAX);
 
        unsigned numGtIntRegions = 0;
        unsigned largestRegion = 0;
        unsigned nonTrivialRegionObjects = 0;
        unsigned allocCounter = 0;
        for (unsigned region = 0; region < numRegions; ++region)
        {
            const size_t regionNumObjects = regionsObjects[region].size();
            // Round up to next Word: ceiling of current allocation to get how
            // many words and multiply to get the number of bits; if we're aligning.
            if (Options::RegionAlign())
            {
                allocCounter =
                    ((allocCounter + NATIVE_INT_SIZE - 1) / NATIVE_INT_SIZE) * NATIVE_INT_SIZE;
            }
 
            if (regionNumObjects > largestRegion) largestRegion = regionNumObjects;
 
            // For regions with fewer than 64 objects, we can just allocate them
            // however as they will be in the one int regardless..
            if (regionNumObjects < NATIVE_INT_SIZE)
            {
                for (NodeID o : regionsObjects[region]) nodeMap[o] = allocCounter++;
                continue;
            }
 
            ++numGtIntRegions;
            nonTrivialRegionObjects += regionNumObjects;
 
            double *distMatrix = getDistanceMatrix(regionsPointsTos[region], regionNumObjects,
                                                   regionReverseMappings[region], distanceMatrixTime);
 
            clkStart = PTAStat::getClk(true);
            int *dendrogram = new int[2 * (regionNumObjects - 1)];
            double *height = new double[regionNumObjects - 1];
            hclust_fast(regionNumObjects, distMatrix, method, dendrogram, height);
            delete[] distMatrix;
            delete[] height;
            clkEnd = PTAStat::getClk(true);
            fastClusterTime += (clkEnd - clkStart) / TIMEINTERVAL;
 
            clkStart = PTAStat::getClk(true);
            Set<int> visited;
            traverseDendrogram(nodeMap, dendrogram, regionNumObjects, allocCounter,
                               visited, regionNumObjects - 1, regionMappings[region]);
            delete[] dendrogram;
            clkEnd = PTAStat::getClk(true);
            dendrogramTraversalTime += (clkEnd - clkStart) / TIMEINTERVAL;
        }
 
        candidates.push_back(std::make_pair(method, nodeMap));
 
        // Though we "update" these in the loop, they will be the same every iteration.
        overallStats[NumGtIntRegions] = std::to_string(numGtIntRegions);
        overallStats[LargestRegion] = std::to_string(largestRegion);
        overallStats[NumNonTrivialRegionObjects] = std::to_string(nonTrivialRegionObjects);
    }
 
    // Work out which of the mappings we generated looks best.
    std::pair<hclust_fast_methods, std::vector<NodeID>> bestMapping = determineBestMapping(candidates, pointsToSets,
            evalSubtitle, evalTime);
 
    overallStats[DistanceMatrixTime] = std::to_string(distanceMatrixTime);
    overallStats[DendrogramTraversalTime] = std::to_string(dendrogramTraversalTime);
    overallStats[FastClusterTime] = std::to_string(fastClusterTime);
    overallStats[EvalTime] = std::to_string(evalTime);
    overallStats[TotalTime] = std::to_string(distanceMatrixTime + dendrogramTraversalTime + fastClusterTime + regioningTime + evalTime);
 
    overallStats[BestCandidate] = SVFUtil::hclustMethodToString(bestMapping.first);
    printStats(evalSubtitle + ": overall", overallStats);
 
    return bestMapping.second;
}
 
std::vector<NodeID> NodeIDAllocator::Clusterer::getReverseNodeMapping(const std::vector<NodeID> &nodeMapping)
{
    // nodeMapping.size() may not be big enough because we leave some gaps, but it's a start.
    std::vector<NodeID> reverseNodeMapping(nodeMapping.size(), UINT_MAX);
    for (size_t i = 0; i < nodeMapping.size(); ++i)
    {
        const NodeID mapsTo = nodeMapping.at(i);
        if (mapsTo >= reverseNodeMapping.size()) reverseNodeMapping.resize(mapsTo + 1, UINT_MAX);
        reverseNodeMapping.at(mapsTo) = i;
    }
 
    return reverseNodeMapping;
}
 
size_t NodeIDAllocator::Clusterer::condensedIndex(size_t n, size_t i, size_t j)
{
    // From https://stackoverflow.com/a/14839010
    return n*(n-1)/2 - (n-i)*(n-i-1)/2 + j - i - 1;
}
 
unsigned NodeIDAllocator::Clusterer::requiredBits(const PointsTo &pts)
{
    return requiredBits(pts.count());
}
 
unsigned NodeIDAllocator::Clusterer::requiredBits(const size_t n)
{
    if (n == 0) return 0;
    // Ceiling of number of bits amongst each native integer gives needed native ints,
    // so we then multiply again by the number of bits in each native int.
    return ((n - 1) / NATIVE_INT_SIZE + 1) * NATIVE_INT_SIZE;
}
 
double *NodeIDAllocator::Clusterer::getDistanceMatrix(const std::vector<std::pair<const PointsTo *, unsigned>> pointsToSets,
        const size_t numObjects, const Map<NodeID, unsigned> &nodeMap,
        double &distanceMatrixTime)
{
    const double clkStart = PTAStat::getClk(true);
    size_t condensedSize = (numObjects * (numObjects - 1)) / 2;
    double *distMatrix = new double[condensedSize];
    for (size_t i = 0; i < condensedSize; ++i) distMatrix[i] = numObjects * numObjects;
 
    // TODO: maybe use machine epsilon?
    // For reducing distance due to extra occurrences.
    // Can differentiate ~9999 occurrences.
    double occurrenceEpsilon = 0.0001;
 
    for (const std::pair<const PointsTo *, unsigned> &ptsOcc : pointsToSets)
    {
        const PointsTo *pts = ptsOcc.first;
        assert(pts != nullptr);
        const unsigned occ = ptsOcc.second;
 
        // Distance between each element of pts.
        unsigned distance = requiredBits(*pts) / NATIVE_INT_SIZE;
 
        // Use a vector so we can index into pts.
        std::vector<NodeID> ptsVec;
        for (const NodeID o : *pts) ptsVec.push_back(o);
        for (size_t i = 0; i < ptsVec.size(); ++i)
        {
            const NodeID oi = ptsVec[i];
            const Map<NodeID, unsigned>::const_iterator moi = nodeMap.find(oi);
            assert(moi != nodeMap.end());
            for (size_t j = i + 1; j < ptsVec.size(); ++j)
            {
                const NodeID oj = ptsVec[j];
                const Map<NodeID, unsigned>::const_iterator moj = nodeMap.find(oj);
                assert(moj != nodeMap.end());
                double &existingDistance = distMatrix[condensedIndex(numObjects, moi->second, moj->second)];
 
                // Subtract extra occurrenceEpsilon to make upcoming logic simpler.
                // When existingDistance is never whole, it is always between two distances.
                if (distance < existingDistance) existingDistance = distance - occurrenceEpsilon;
 
                if (distance == std::ceil(existingDistance))
                {
                    // We have something like distance == x, existingDistance == x - e, for some e < 1
                    // (potentially even set during this iteration).
                    // So, the new distance is an occurrence the existingDistance being tracked, it just
                    // had some reductions because of multiple occurrences.
                    // If there is not room within this distance to reduce more (increase priority),
                    // just ignore it. TODO: maybe warn?
                    if (existingDistance - occ * occurrenceEpsilon > std::floor(existingDistance))
                    {
                        existingDistance -= occ * occurrenceEpsilon;
                    }
                    else
                    {
                        // Reached minimum.
                        existingDistance = std::floor(existingDistance) + occurrenceEpsilon;
                    }
                }
            }
        }
 
    }
 
    const double clkEnd = PTAStat::getClk(true);
    distanceMatrixTime += (clkEnd - clkStart) / TIMEINTERVAL;
 
    return distMatrix;
}
 
void NodeIDAllocator::Clusterer::traverseDendrogram(std::vector<NodeID> &nodeMap, const int *dendrogram, const size_t numObjects, unsigned &allocCounter, Set<int> &visited, const int index, const std::vector<NodeID> &regionNodeMap)
{
    if (visited.find(index) != visited.end()) return;
    visited.insert(index);
 
    int left = dendrogram[index - 1];
    if (left < 0)
    {
        // Reached a leaf.
        // -1 because the items start from 1 per fastcluster (TODO).
        nodeMap[regionNodeMap[std::abs(left) - 1]] = allocCounter;
        ++allocCounter;
    }
    else
    {
        traverseDendrogram(nodeMap, dendrogram, numObjects, allocCounter, visited, left, regionNodeMap);
    }
 
    // Repeat for the right child.
    int right = dendrogram[(numObjects - 1) + index - 1];
    if (right < 0)
    {
        nodeMap[regionNodeMap[std::abs(right) - 1]] = allocCounter;
        ++allocCounter;
    }
    else
    {
        traverseDendrogram(nodeMap, dendrogram, numObjects, allocCounter, visited, right, regionNodeMap);
    }
}
 
std::vector<NodeID> NodeIDAllocator::Clusterer::regionObjects(const Map<NodeID, Set<NodeID>> &graph, size_t numObjects, size_t &numLabels)
{
    unsigned label = UINT_MAX;
    std::vector<NodeID> labels(numObjects, UINT_MAX);
    Set<NodeID> labelled;
    for (const Map<NodeID, Set<NodeID>>::value_type &oos : graph)
    {
        const NodeID o = oos.first;
        if (labels[o] != UINT_MAX) continue;
        std::queue<NodeID> bfsQueue;
        bfsQueue.push(o);
        ++label;
        while (!bfsQueue.empty())
        {
            const NodeID o = bfsQueue.front();
            bfsQueue.pop();
            if (labels[o] != UINT_MAX)
            {
                assert(labels[o] == label);
                continue;
            }
 
            labels[o] = label;
            Map<NodeID, Set<NodeID>>::const_iterator neighboursIt = graph.find(o);
            assert(neighboursIt != graph.end());
            for (const NodeID neighbour : neighboursIt->second) bfsQueue.push(neighbour);
        }
    }
 
    // The remaining objects have no relation with others: they get their own label.
    for (size_t o = 0; o < numObjects; ++o)
    {
        if (labels[o] == UINT_MAX) labels[o] = ++label;
    }
 
    numLabels = label + 1;
 
    return labels;
}
 
void NodeIDAllocator::Clusterer::evaluate(const std::vector<NodeID> &nodeMap, const Map<PointsTo, unsigned> pointsToSets, Map<std::string, std::string> &stats, bool accountForOcc)
{
    u64_t totalTheoretical = 0;
    u64_t totalOriginalSbv = 0;
    u64_t totalOriginalBv = 0;
    u64_t totalNewSbv = 0;
    u64_t totalNewBv = 0;
 
    for (const Map<PointsTo, unsigned>::value_type &ptsOcc : pointsToSets)
    {
        const PointsTo &pts = ptsOcc.first;
        const unsigned occ = ptsOcc.second;
        if (pts.count() == 0) continue;
 
        u64_t theoretical = requiredBits(pts) / NATIVE_INT_SIZE;
        if (accountForOcc) theoretical *= occ;
 
        // Check number of words for original SBV.
        Set<unsigned> words;
        // TODO: nasty hardcoding.
        for (const NodeID o : pts) words.insert(o / 128);
        u64_t originalSbv = words.size() * 2;
        if (accountForOcc) originalSbv *= occ;
 
        // Check number of words for original BV.
        NodeID min = UINT_MAX;
        NodeID max = 0;
        for (NodeID o : pts)
        {
            if (o < min) min = o;
            if (o > max) max = o;
        }
        words.clear();
        for (NodeID b = min; b <= max; ++b)
        {
            words.insert(b / NATIVE_INT_SIZE);
        }
        u64_t originalBv = words.size();
        if (accountForOcc) originalBv *= occ;
 
        // Check number of words for new SBV.
        words.clear();
        // TODO: nasty hardcoding.
        for (const NodeID o : pts) words.insert(nodeMap[o] / 128);
        u64_t newSbv = words.size() * 2;
        if (accountForOcc) newSbv *= occ;
 
        // Check number of words for new BV.
        min = UINT_MAX;
        max = 0;
        for (const NodeID o : pts)
        {
            const NodeID mappedO = nodeMap[o];
            if (mappedO < min) min = mappedO;
            if (mappedO > max) max = mappedO;
        }
 
        words.clear();
        // No nodeMap[b] because min and max and from nodeMap.
        for (NodeID b = min; b <= max; ++b) words.insert(b / NATIVE_INT_SIZE);
        u64_t newBv = words.size();
        if (accountForOcc) newBv *= occ;
 
        totalTheoretical += theoretical;
        totalOriginalSbv += originalSbv;
        totalOriginalBv += originalBv;
        totalNewSbv += newSbv;
        totalNewBv += newBv;
    }
 
    stats[TheoreticalNumWords] = std::to_string(totalTheoretical);
    stats[OriginalSbvNumWords] = std::to_string(totalOriginalSbv);
    stats[OriginalBvNumWords] = std::to_string(totalOriginalBv);
    stats[NewSbvNumWords] = std::to_string(totalNewSbv);
    stats[NewBvNumWords] = std::to_string(totalNewBv);
}
 
// Work out which of the mappings we generated looks best.
std::pair<hclust_fast_methods, std::vector<NodeID>> NodeIDAllocator::Clusterer::determineBestMapping(
            const std::vector<std::pair<hclust_fast_methods, std::vector<NodeID>>> &candidates,
            Map<PointsTo, unsigned> pointsToSets, const std::string &evalSubtitle, double &evalTime)
{
    // In case we're not comparing anything, set to first "candidate".
    std::pair<hclust_fast_methods, std::vector<NodeID>> bestMapping = candidates[0];
    // Number of bits required for the best candidate.
    size_t bestWords = std::numeric_limits<size_t>::max();
    if (evalSubtitle != "" || (enum hclust_fast_methods)Options::ClusterMethod() == HCLUST_METHOD_SVF_BEST)
    {
        for (const std::pair<hclust_fast_methods, std::vector<NodeID>> &candidate : candidates)
        {
            Map<std::string, std::string> candidateStats;
            hclust_fast_methods candidateMethod = candidate.first;
            std::string candidateMethodName = SVFUtil::hclustMethodToString(candidateMethod);
            std::vector<NodeID> candidateMapping = candidate.second;
 
            // TODO: parameterise final arg.
            const double clkStart = PTAStat::getClk(true);
            evaluate(candidateMapping, pointsToSets, candidateStats, true);
            const double clkEnd = PTAStat::getClk(true);
            evalTime += (clkEnd - clkStart) / TIMEINTERVAL;
            printStats(evalSubtitle + ": candidate " + candidateMethodName, candidateStats);
 
            size_t candidateWords = 0;
            if (Options::PtType() == PointsTo::SBV) candidateWords = std::stoull(candidateStats[NewSbvNumWords]);
            else if (Options::PtType() == PointsTo::CBV) candidateWords = std::stoull(candidateStats[NewBvNumWords]);
            else assert(false && "Clusterer::cluster: unsupported BV type for clustering.");
 
            if (candidateWords < bestWords)
            {
                bestWords = candidateWords;
                bestMapping = candidate;
            }
        }
    }
 
    return bestMapping;
}
 
void NodeIDAllocator::Clusterer::printStats(std::string subtitle, Map<std::string, std::string> &stats)
{
    // When not in order, it is too hard to compare original/new SBV/BV words, so this array forces an order.
    static const std::string statKeys[] =
    {
        NumObjects, TheoreticalNumWords, OriginalSbvNumWords, OriginalBvNumWords,
        NewSbvNumWords, NewBvNumWords, NumRegions, NumGtIntRegions,
        NumNonTrivialRegionObjects, LargestRegion, RegioningTime,
        DistanceMatrixTime, FastClusterTime, DendrogramTraversalTime,
        EvalTime, TotalTime, BestCandidate
    };
 
    const unsigned fieldWidth = 20;
    SVFUtil::outs().flags(std::ios::left);
    SVFUtil::outs() << "****Clusterer Statistics: " << subtitle << "****\n";
    for (const std::string& statKey : statKeys)
    {
        Map<std::string, std::string>::const_iterator stat = stats.find(statKey);
        if (stat != stats.end())
        {
            SVFUtil::outs() << std::setw(fieldWidth) << statKey << " " << stat->second << "\n";
        }
    }
 
    SVFUtil::outs().flush();
}
 
};  // namespace SVF.