void calculateStats(Type& minVal, Type& maxVal, double& avgVal, double& devVal, const RCP<const Teuchos::Comm<int> >& comm, const Type& v) {
int numProcs = comm->getSize();
Type sumVal, sum2Val;
MueLu_sumAll(comm, v, sumVal);
MueLu_sumAll(comm, v*v, sum2Val);
MueLu_minAll(comm, v, minVal);
MueLu_maxAll(comm, v, maxVal);
avgVal = as<double>(sumVal) / numProcs;
devVal = (numProcs != 1 ? sqrt((sum2Val - sumVal*avgVal)/(numProcs-1)) : 0);
}
std::string MHDRAPFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::PrintLoadBalancingInfo(const Matrix & Ac, const std::string & msgTag) {
std::stringstream ss(std::stringstream::out);
// TODO: provide a option to skip this (to avoid global communication)
// TODO: skip if nproc == 1
//nonzero imbalance
size_t numMyNnz = Ac.getNodeNumEntries();
GO maxNnz, minNnz;
RCP<const Teuchos::Comm<int> > comm = Ac.getRowMap()->getComm();
MueLu_maxAll(comm,(GO)numMyNnz,maxNnz);
//min nnz over all proc (disallow any processors with 0 nnz)
MueLu_minAll(comm, (GO)((numMyNnz > 0) ? numMyNnz : maxNnz), minNnz);
double imbalance = ((double) maxNnz) / minNnz;
size_t numMyRows = Ac.getNodeNumRows();
//Check whether Ac is spread over more than one process.
GO numActiveProcesses=0;
MueLu_sumAll(comm, (GO)((numMyRows > 0) ? 1 : 0), numActiveProcesses);
//min, max, and avg # rows per proc
GO minNumRows, maxNumRows;
double avgNumRows;
MueLu_maxAll(comm, (GO)numMyRows, maxNumRows);
MueLu_minAll(comm, (GO)((numMyRows > 0) ? numMyRows : maxNumRows), minNumRows);
assert(numActiveProcesses > 0);
avgNumRows = Ac.getGlobalNumRows() / numActiveProcesses;
ss << msgTag << " # processes with rows = " << numActiveProcesses << std::endl;
ss << msgTag << " min # rows per proc = " << minNumRows << ", max # rows per proc = " << maxNumRows << ", avg # rows per proc = " << avgNumRows << std::endl;
ss << msgTag << " nonzero imbalance = " << imbalance << std::endl;
return ss.str();
}
void RAPFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::CheckRepairMainDiagonal(RCP<Matrix>& Ac) const {
const Teuchos::ParameterList& pL = GetParameterList();
bool repairZeroDiagonals = pL.get<bool>("RepairMainDiagonal");
bool checkAc = pL.get<bool>("CheckMainDiagonal");
if (!checkAc && !repairZeroDiagonals)
return;
SC zero = Teuchos::ScalarTraits<SC>::zero(), one = Teuchos::ScalarTraits<SC>::one();
Teuchos::RCP<Teuchos::ParameterList> p = Teuchos::rcp(new Teuchos::ParameterList());
p->set("DoOptimizeStorage", true);
RCP<const Map> rowMap = Ac->getRowMap();
RCP<Vector> diagVec = VectorFactory::Build(rowMap);
Ac->getLocalDiagCopy(*diagVec);
LO lZeroDiags = 0;
Teuchos::ArrayRCP< Scalar > diagVal = diagVec->getDataNonConst(0);
for (size_t i = 0; i < rowMap->getNodeNumElements(); i++) {
if (diagVal[i] == zero) {
lZeroDiags++;
}
}
GO gZeroDiags;
MueLu_sumAll(rowMap->getComm(), Teuchos::as<GO>(lZeroDiags), gZeroDiags);
if (repairZeroDiagonals && gZeroDiags > 0) {
// TAW: If Ac has empty rows, put a 1 on the diagonal of Ac. Be aware that Ac might have empty rows AND columns.
// The columns might not exist in the column map at all.
//
// It would be nice to add the entries to the original matrix Ac. But then we would have to use
// insertLocalValues. However we cannot add new entries for local column indices that do not exist in the column map
// of Ac (at least Epetra is not able to do this).
//
// Here we build a diagonal matrix with zeros on the diagonal and ones on the diagonal for the rows where Ac has empty rows
// We have to build a new matrix to be able to use insertGlobalValues. Then we add the original matrix Ac to our new block
// diagonal matrix and use the result as new (non-singular) matrix Ac.
// This is very inefficient.
//
// If you know something better, please let me know.
RCP<Matrix> fixDiagMatrix = Teuchos::null;
fixDiagMatrix = MatrixFactory::Build(rowMap, 1);
for (size_t r = 0; r < rowMap->getNodeNumElements(); r++) {
if (diagVal[r] == zero) {
GO grid = rowMap->getGlobalElement(r);
Teuchos::ArrayRCP<GO> indout(1,grid);
Teuchos::ArrayRCP<SC> valout(1, one);
fixDiagMatrix->insertGlobalValues(grid,indout.view(0, 1), valout.view(0, 1));
}
}
{
Teuchos::TimeMonitor m1(*Teuchos::TimeMonitor::getNewTimer("CheckRepairMainDiagonal: fillComplete1"));
Ac->fillComplete(p);
}
MueLu::Utils2<Scalar, LocalOrdinal, GlobalOrdinal, Node>::TwoMatrixAdd(*Ac, false, 1.0, *fixDiagMatrix, 1.0);
if (Ac->IsView("stridedMaps"))
fixDiagMatrix->CreateView("stridedMaps", Ac);
Ac = Teuchos::null; // free singular coarse level matrix
Ac = fixDiagMatrix; // set fixed non-singular coarse level matrix
}
// call fillComplete with optimized storage option set to true
// This is necessary for new faster Epetra MM kernels.
{
Teuchos::TimeMonitor m1(*Teuchos::TimeMonitor::getNewTimer("CheckRepairMainDiagonal: fillComplete2"));
Ac->fillComplete(p);
}
// print some output
if (IsPrint(Warnings0))
GetOStream(Warnings0) << "RAPFactory (WARNING): " << (repairZeroDiagonals ? "repaired " : "found ")
<< gZeroDiags << " zeros on main diagonal of Ac." << std::endl;
#ifdef HAVE_MUELU_DEBUG // only for debugging
// check whether Ac has been repaired...
Ac->getLocalDiagCopy(*diagVec);
Teuchos::ArrayRCP< Scalar > diagVal2 = diagVec->getDataNonConst(0);
for (size_t r = 0; r < Ac->getRowMap()->getNodeNumElements(); r++) {
if (diagVal2[r] == zero) {
GetOStream(Errors,-1) << "Error: there are zeros left on diagonal after repair..." << std::endl;
break;
}
}
#endif
}
void RepartitionFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::Build(Level& currentLevel) const {
FactoryMonitor m(*this, "Build", currentLevel);
const Teuchos::ParameterList & pL = GetParameterList();
// Access parameters here to make sure that we set the parameter entry flag to "used" even in case of short-circuit evaluation.
// TODO (JG): I don't really know if we want to do this.
const int startLevel = pL.get<int> ("repartition: start level");
const LO minRowsPerProcessor = pL.get<LO> ("repartition: min rows per proc");
const double nonzeroImbalance = pL.get<double>("repartition: max imbalance");
const bool remapPartitions = pL.get<bool> ("repartition: remap parts");
// TODO: We only need a CrsGraph. This class does not have to be templated on Scalar types.
RCP<Matrix> A = Get< RCP<Matrix> >(currentLevel, "A");
// ======================================================================================================
// Determine whether partitioning is needed
// ======================================================================================================
// NOTE: most tests include some global communication, which is why we currently only do tests until we make
// a decision on whether to repartition. However, there is value in knowing how "close" we are to having to
// rebalance an operator. So, it would probably be beneficial to do and report *all* tests.
// Test1: skip repartitioning if current level is less than the specified minimum level for repartitioning
if (currentLevel.GetLevelID() < startLevel) {
GetOStream(Statistics0) << "Repartitioning? NO:" <<
"\n current level = " << Teuchos::toString(currentLevel.GetLevelID()) <<
", first level where repartitioning can happen is " + Teuchos::toString(startLevel) << std::endl;
Set<RCP<const Import> >(currentLevel, "Importer", Teuchos::null);
return;
}
RCP<const Map> rowMap = A->getRowMap();
// NOTE: Teuchos::MPIComm::duplicate() calls MPI_Bcast inside, so this is
// a synchronization point. However, as we do MueLu_sumAll afterwards anyway, it
// does not matter.
RCP<const Teuchos::Comm<int> > origComm = rowMap->getComm();
RCP<const Teuchos::Comm<int> > comm = origComm->duplicate();
// Test 2: check whether A is actually distributed, i.e. more than one processor owns part of A
// TODO: this global communication can be avoided if we store the information with the matrix (it is known when matrix is created)
// TODO: further improvements could be achieved when we use subcommunicator for the active set. Then we only need to check its size
{
int numActiveProcesses = 0;
MueLu_sumAll(comm, Teuchos::as<int>((A->getNodeNumRows() > 0) ? 1 : 0), numActiveProcesses);
if (numActiveProcesses == 1) {
GetOStream(Statistics0) << "Repartitioning? NO:" <<
"\n # processes with rows = " << Teuchos::toString(numActiveProcesses) << std::endl;
Set<RCP<const Import> >(currentLevel, "Importer", Teuchos::null);
return;
}
}
bool test3 = false, test4 = false;
std::string msg3, msg4;
// Test3: check whether number of rows on any processor satisfies the minimum number of rows requirement
// NOTE: Test2 ensures that repartitionning is not done when there is only one processor (it may or may not satisfy Test3)
if (minRowsPerProcessor > 0) {
LO numMyRows = Teuchos::as<LO>(A->getNodeNumRows()), minNumRows, LOMAX = Teuchos::OrdinalTraits<LO>::max();
LO haveFewRows = (numMyRows < minRowsPerProcessor ? 1 : 0), numWithFewRows = 0;
MueLu_sumAll(comm, haveFewRows, numWithFewRows);
MueLu_minAll(comm, (numMyRows > 0 ? numMyRows : LOMAX), minNumRows);
// TODO: we could change it to repartition only if the number of processors with numRows < minNumRows is larger than some
// percentage of the total number. This way, we won't repartition if 2 out of 1000 processors don't have enough elements.
// I'm thinking maybe 20% threshold. To implement, simply add " && numWithFewRows < .2*numProcs" to the if statement.
if (numWithFewRows > 0)
test3 = true;
msg3 = "\n min # rows per proc = " + Teuchos::toString(minNumRows) + ", min allowable = " + Teuchos::toString(minRowsPerProcessor);
}
// Test4: check whether the balance in the number of nonzeros per processor is greater than threshold
if (!test3) {
GO minNnz, maxNnz, numMyNnz = Teuchos::as<GO>(A->getNodeNumEntries());
MueLu_maxAll(comm, numMyNnz, maxNnz);
MueLu_minAll(comm, (numMyNnz > 0 ? numMyNnz : maxNnz), minNnz); // min nnz over all active processors
double imbalance = Teuchos::as<double>(maxNnz)/minNnz;
if (imbalance > nonzeroImbalance)
test4 = true;
msg4 = "\n nonzero imbalance = " + Teuchos::toString(imbalance) + ", max allowable = " + Teuchos::toString(nonzeroImbalance);
}
if (!test3 && !test4) {
GetOStream(Statistics0) << "Repartitioning? NO:" << msg3 + msg4 << std::endl;
Set<RCP<const Import> >(currentLevel, "Importer", Teuchos::null);
return;
}
GetOStream(Statistics0) << "Repartitioning? YES:" << msg3 + msg4 << std::endl;
GO indexBase = rowMap->getIndexBase();
Xpetra::UnderlyingLib lib = rowMap->lib();
int myRank = comm->getRank();
int numProcs = comm->getSize();
RCP<const Teuchos::MpiComm<int> > tmpic = rcp_dynamic_cast<const Teuchos::MpiComm<int> >(comm);
TEUCHOS_TEST_FOR_EXCEPTION(tmpic == Teuchos::null, Exceptions::RuntimeError, "Cannot cast base Teuchos::Comm to Teuchos::MpiComm object.");
RCP<const Teuchos::OpaqueWrapper<MPI_Comm> > rawMpiComm = tmpic->getRawMpiComm();
// ======================================================================================================
// Calculate number of partitions
// ======================================================================================================
// FIXME Quick way to figure out how many partitions there should be (same algorithm as ML)
// FIXME Should take into account nnz? Perhaps only when user is using min #nnz per row threshold.
GO numPartitions;
if (currentLevel.IsAvailable("number of partitions")) {
numPartitions = currentLevel.Get<GO>("number of partitions");
GetOStream(Warnings0) << "Using user-provided \"number of partitions\", the performance is unknown" << std::endl;
} else {
if (Teuchos::as<GO>(A->getGlobalNumRows()) < minRowsPerProcessor) {
// System is too small, migrate it to a single processor
numPartitions = 1;
} else {
// Make sure that each processor has approximately minRowsPerProcessor
numPartitions = A->getGlobalNumRows() / minRowsPerProcessor;
}
numPartitions = std::min(numPartitions, Teuchos::as<GO>(numProcs));
currentLevel.Set("number of partitions", numPartitions, NoFactory::get());
}
GetOStream(Statistics0) << "Number of partitions to use = " << numPartitions << std::endl;
// ======================================================================================================
// Construct decomposition vector
// ======================================================================================================
RCP<GOVector> decomposition;
if (numPartitions == 1) {
// Trivial case: decomposition is the trivial one, all zeros. We skip the call to Zoltan_Interface
// (this is mostly done to avoid extra output messages, as even if we didn't skip there is a shortcut
// in Zoltan[12]Interface).
// TODO: We can probably skip more work in this case (like building all extra data structures)
GetOStream(Warnings0) << "Only one partition: Skip call to the repartitioner." << std::endl;
decomposition = Xpetra::VectorFactory<GO, LO, GO, NO>::Build(A->getRowMap(), true);
} else {
decomposition = Get<RCP<GOVector> >(currentLevel, "Partition");
if (decomposition.is_null()) {
GetOStream(Warnings0) << "No repartitioning necessary: partitions were left unchanged by the repartitioner" << std::endl;
Set<RCP<const Import> >(currentLevel, "Importer", Teuchos::null);
return;
}
}
// ======================================================================================================
// Remap if necessary
// ======================================================================================================
// From a user perspective, we want user to not care about remapping, thinking of it as only a performance feature.
// There are two problems, however.
// (1) Next level aggregation depends on the order of GIDs in the vector, if one uses "natural" or "random" orderings.
// This also means that remapping affects next level aggregation, despite the fact that the _set_ of GIDs for
// each partition is the same.
// (2) Even with the fixed order of GIDs, the remapping may influence the aggregation for the next-next level.
// Let us consider the following example. Lets assume that when we don't do remapping, processor 0 would have
// GIDs {0,1,2}, and processor 1 GIDs {3,4,5}, and if we do remapping processor 0 would contain {3,4,5} and
// processor 1 {0,1,2}. Now, when we run repartitioning algorithm on the next level (say Zoltan1 RCB), it may
// be dependent on whether whether it is [{0,1,2}, {3,4,5}] or [{3,4,5}, {0,1,2}]. Specifically, the tie-breaking
// algorithm can resolve these differently. For instance, running
// mpirun -np 5 ./MueLu_ScalingTestParamList.exe --xml=easy_sa.xml --nx=12 --ny=12 --nz=12
// with
// <ParameterList name="MueLu">
// <Parameter name="coarse: max size" type="int" value="1"/>
// <Parameter name="repartition: enable" type="bool" value="true"/>
// <Parameter name="repartition: min rows per proc" type="int" value="2"/>
// <ParameterList name="level 1">
// <Parameter name="repartition: remap parts" type="bool" value="false/true"/>
// </ParameterList>
// </ParameterList>
// produces different repartitioning for level 2.
// This different repartitioning may then escalate into different aggregation for the next level.
//
// We fix (1) by fixing the order of GIDs in a vector by sorting the resulting vector.
// Fixing (2) is more complicated.
// FIXME: Fixing (2) in Zoltan may not be enough, as we may use some arbitration in MueLu,
// for instance with CoupledAggregation. What we really need to do is to use the same order of processors containing
// the same order of GIDs. To achieve that, the newly created subcommunicator must be conforming with the order. For
// instance, if we have [{0,1,2}, {3,4,5}], we create a subcommunicator where processor 0 gets rank 0, and processor 1
// gets rank 1. If, on the other hand, we have [{3,4,5}, {0,1,2}], we assign rank 1 to processor 0, and rank 0 to processor 1.
// This rank permutation requires help from Epetra/Tpetra, both of which have no such API in place.
// One should also be concerned that if we had such API in place, rank 0 in subcommunicator may no longer be rank 0 in
// MPI_COMM_WORLD, which may lead to issues for logging.
if (remapPartitions) {
SubFactoryMonitor m1(*this, "DeterminePartitionPlacement", currentLevel);
DeterminePartitionPlacement(*A, *decomposition, numPartitions);
}
// ======================================================================================================
// Construct importer
// ======================================================================================================
// At this point, the following is true:
// * Each processors owns 0 or 1 partitions
// * If a processor owns a partition, that partition number is equal to the processor rank
// * The decomposition vector contains the partitions ids that the corresponding GID belongs to
ArrayRCP<const GO> decompEntries;
if (decomposition->getLocalLength() > 0)
decompEntries = decomposition->getData(0);
#ifdef HAVE_MUELU_DEBUG
// Test range of partition ids
int incorrectRank = -1;
for (int i = 0; i < decompEntries.size(); i++)
if (decompEntries[i] >= numProcs || decompEntries[i] < 0) {
incorrectRank = myRank;
break;
}
int incorrectGlobalRank = -1;
MueLu_maxAll(comm, incorrectRank, incorrectGlobalRank);
TEUCHOS_TEST_FOR_EXCEPTION(incorrectGlobalRank >- 1, Exceptions::RuntimeError, "pid " + Teuchos::toString(incorrectGlobalRank) + " encountered a partition number is that out-of-range");
#endif
Array<GO> myGIDs;
myGIDs.reserve(decomposition->getLocalLength());
// Step 0: Construct mapping
// part number -> GIDs I own which belong to this part
// NOTE: my own part GIDs are not part of the map
typedef std::map<GO, Array<GO> > map_type;
map_type sendMap;
for (LO i = 0; i < decompEntries.size(); i++) {
GO id = decompEntries[i];
GO GID = rowMap->getGlobalElement(i);
if (id == myRank)
myGIDs .push_back(GID);
else
sendMap[id].push_back(GID);
}
decompEntries = Teuchos::null;
if (IsPrint(Statistics2)) {
GO numLocalKept = myGIDs.size(), numGlobalKept, numGlobalRows = A->getGlobalNumRows();
MueLu_sumAll(comm,numLocalKept, numGlobalKept);
GetOStream(Statistics2) << "Unmoved rows: " << numGlobalKept << " / " << numGlobalRows << " (" << 100*Teuchos::as<double>(numGlobalKept)/numGlobalRows << "%)" << std::endl;
}
int numSend = sendMap.size(), numRecv;
// Arrayify map keys
Array<GO> myParts(numSend), myPart(1);
int cnt = 0;
myPart[0] = myRank;
for (typename map_type::const_iterator it = sendMap.begin(); it != sendMap.end(); it++)
myParts[cnt++] = it->first;
// Step 1: Find out how many processors send me data
// partsIndexBase starts from zero, as the processors ids start from zero
GO partsIndexBase = 0;
RCP<Map> partsIHave = MapFactory ::Build(lib, Teuchos::OrdinalTraits<Xpetra::global_size_t>::invalid(), myParts(), partsIndexBase, comm);
RCP<Map> partsIOwn = MapFactory ::Build(lib, numProcs, myPart(), partsIndexBase, comm);
RCP<Export> partsExport = ExportFactory::Build(partsIHave, partsIOwn);
RCP<GOVector> partsISend = Xpetra::VectorFactory<GO, LO, GO, NO>::Build(partsIHave);
RCP<GOVector> numPartsIRecv = Xpetra::VectorFactory<GO, LO, GO, NO>::Build(partsIOwn);
if (numSend) {
ArrayRCP<GO> partsISendData = partsISend->getDataNonConst(0);
for (int i = 0; i < numSend; i++)
partsISendData[i] = 1;
}
(numPartsIRecv->getDataNonConst(0))[0] = 0;
numPartsIRecv->doExport(*partsISend, *partsExport, Xpetra::ADD);
numRecv = (numPartsIRecv->getData(0))[0];
// Step 2: Get my GIDs from everybody else
MPI_Datatype MpiType = MpiTypeTraits<GO>::getType();
int msgTag = 12345; // TODO: use Comm::dup for all internal messaging
// Post sends
Array<MPI_Request> sendReqs(numSend);
cnt = 0;
for (typename map_type::iterator it = sendMap.begin(); it != sendMap.end(); it++)
MPI_Isend(static_cast<void*>(it->second.getRawPtr()), it->second.size(), MpiType, Teuchos::as<GO>(it->first), msgTag, *rawMpiComm, &sendReqs[cnt++]);
map_type recvMap;
size_t totalGIDs = myGIDs.size();
for (int i = 0; i < numRecv; i++) {
MPI_Status status;
MPI_Probe(MPI_ANY_SOURCE, msgTag, *rawMpiComm, &status);
// Get rank and number of elements from status
int fromRank = status.MPI_SOURCE, count;
MPI_Get_count(&status, MpiType, &count);
recvMap[fromRank].resize(count);
MPI_Recv(static_cast<void*>(recvMap[fromRank].getRawPtr()), count, MpiType, fromRank, msgTag, *rawMpiComm, &status);
totalGIDs += count;
}
// Do waits on send requests
if (numSend) {
Array<MPI_Status> sendStatuses(numSend);
MPI_Waitall(numSend, sendReqs.getRawPtr(), sendStatuses.getRawPtr());
}
// Merge GIDs
myGIDs.reserve(totalGIDs);
for (typename map_type::const_iterator it = recvMap.begin(); it != recvMap.end(); it++) {
int offset = myGIDs.size(), len = it->second.size();
if (len) {
myGIDs.resize(offset + len);
memcpy(myGIDs.getRawPtr() + offset, it->second.getRawPtr(), len*sizeof(GO));
}
}
// NOTE 2: The general sorting algorithm could be sped up by using the knowledge that original myGIDs and all received chunks
// (i.e. it->second) are sorted. Therefore, a merge sort would work well in this situation.
std::sort(myGIDs.begin(), myGIDs.end());
// Step 3: Construct importer
RCP<Map> newRowMap = MapFactory ::Build(lib, rowMap->getGlobalNumElements(), myGIDs(), indexBase, origComm);
RCP<const Import> rowMapImporter;
{
SubFactoryMonitor m1(*this, "Import construction", currentLevel);
rowMapImporter = ImportFactory::Build(rowMap, newRowMap);
}
Set(currentLevel, "Importer", rowMapImporter);
// ======================================================================================================
// Print some data
// ======================================================================================================
if (pL.get<bool>("repartition: print partition distribution") && IsPrint(Statistics2)) {
// Print the grid of processors
GetOStream(Statistics2) << "Partition distribution over cores (ownership is indicated by '+')" << std::endl;
char amActive = (myGIDs.size() ? 1 : 0);
std::vector<char> areActive(numProcs, 0);
MPI_Gather(&amActive, 1, MPI_CHAR, &areActive[0], 1, MPI_CHAR, 0, *rawMpiComm);
int rowWidth = std::min(Teuchos::as<int>(ceil(sqrt(numProcs))), 100);
for (int proc = 0; proc < numProcs; proc += rowWidth) {
for (int j = 0; j < rowWidth; j++)
if (proc + j < numProcs)
GetOStream(Statistics2) << (areActive[proc + j] ? "+" : ".");
else
GetOStream(Statistics2) << " ";
GetOStream(Statistics2) << " " << proc << ":" << std::min(proc + rowWidth, numProcs) - 1 << std::endl;
}
}
} // Build
void BrickAggregationFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::Build(Level& currentLevel) const {
FactoryMonitor m(*this, "Build", currentLevel);
typedef Xpetra::MultiVector<double,LO,GO,NO> MultiVector_d;
const ParameterList& pL = GetParameterList();
RCP<MultiVector_d> coords = Get<RCP<MultiVector_d> >(currentLevel, "Coordinates");
RCP<Matrix> A = Get< RCP<Matrix> > (currentLevel, "A");
RCP<const Map> rowMap = A->getRowMap();
RCP<const Map> colMap = A->getColMap();
RCP<const Teuchos::Comm<int> > comm = rowMap->getComm();
int numProcs = comm->getSize();
int myRank = comm->getRank();
int numPoints = colMap->getNodeNumElements();
bx_ = pL.get<int>("aggregation: brick x size");
by_ = pL.get<int>("aggregation: brick y size");
bz_ = pL.get<int>("aggregation: brick z size");
if (numProcs > 1) {
// TODO: deal with block size > 1 (see comments above)
TEUCHOS_TEST_FOR_EXCEPTION(bx_ > 3 || by_ > 3 || bz_ > 3, Exceptions::RuntimeError, "Currently cannot deal with brick size > 3");
}
RCP<MultiVector_d> overlappedCoords = coords;
RCP<const Import> importer = ImportFactory::Build(coords->getMap(), colMap);
if (!importer.is_null()) {
overlappedCoords = Xpetra::MultiVectorFactory<double,LO,GO,NO>::Build(colMap, coords->getNumVectors());
overlappedCoords->doImport(*coords, *importer, Xpetra::INSERT);
}
// Setup misc structures
// Logically, we construct enough data to query topological information of a rectangular grid
Setup(comm, overlappedCoords, colMap);
GetOStream(Runtime0) << "Using brick size: " << bx_
<< (nDim_ > 1 ? "x " + toString(by_) : "")
<< (nDim_ > 2 ? "x " + toString(bz_) : "") << std::endl;
// Construct aggregates
RCP<Aggregates> aggregates = rcp(new Aggregates(colMap));
aggregates->setObjectLabel("Brick");
ArrayRCP<LO> vertex2AggId = aggregates->GetVertex2AggId()->getDataNonConst(0);
ArrayRCP<LO> procWinner = aggregates->GetProcWinner() ->getDataNonConst(0);
// In the first pass, we set a mapping from a vertex to aggregate global id. We deal with a structured
// rectangular mesh, therefore we know the structure of aggregates. For each vertex we can tell exactly
// which aggregate it belongs to.
// If we determine that the aggregate does not belong to us (i.e. the root vertex does not belong to this
// processor, or is outside and we lost "" arbitration), we record the global aggregate id in order to
// fetch the local info from the processor owning the aggregate. This is required for aggregates, as it
// uses the local aggregate ids of the owning processor.
std::set<GO> myAggGIDs, remoteAggGIDs;
for (LO LID = 0; LID < numPoints; LID++) {
GO aggGID = getAggGID(LID);
if ((revMap_.find(getRoot(LID)) != revMap_.end()) && rowMap->isNodeGlobalElement(colMap->getGlobalElement(revMap_[getRoot(LID)]))) {
// Root of the brick aggregate containing GID (<- LID) belongs to us
vertex2AggId[LID] = aggGID;
myAggGIDs.insert(aggGID);
if (isRoot(LID))
aggregates->SetIsRoot(LID);
} else {
remoteAggGIDs.insert(aggGID);
}
}
size_t numAggregates = myAggGIDs .size();
size_t numRemote = remoteAggGIDs.size();
aggregates->SetNumAggregates(numAggregates);
std::map<GO,LO> AggG2L; // Map: Agg GID -> Agg LID (possibly on a different processor)
std::map<GO,int> AggG2R; // Map: Agg GID -> processor rank owning aggregate
Array<GO> myAggGIDsArray(numAggregates), remoteAggGIDsArray(numRemote);
// Fill in the maps for aggregates that we own
size_t ind = 0;
for (typename std::set<GO>::const_iterator it = myAggGIDs.begin(); it != myAggGIDs.end(); it++) {
AggG2L[*it] = ind;
AggG2R[*it] = myRank;
myAggGIDsArray[ind++] = *it;
}
// The map is a convenient way to fetch remote local indices from global indices.
RCP<Map> aggMap = MapFactory::Build(rowMap->lib(), Teuchos::OrdinalTraits<Xpetra::global_size_t>::invalid(),
myAggGIDsArray, 0, comm);
ind = 0;
for (typename std::set<GO>::const_iterator it = remoteAggGIDs.begin(); it != remoteAggGIDs.end(); it++)
remoteAggGIDsArray[ind++] = *it;
// Fetch the required aggregate local ids and ranks
Array<int> remoteProcIDs(numRemote);
Array<LO> remoteLIDs (numRemote);
aggMap->getRemoteIndexList(remoteAggGIDsArray, remoteProcIDs, remoteLIDs);
// Fill in the maps for aggregates that we don't own but which have some of our vertices
for (size_t i = 0; i < numRemote; i++) {
AggG2L[remoteAggGIDsArray[i]] = remoteLIDs [i];
AggG2R[remoteAggGIDsArray[i]] = remoteProcIDs[i];
}
// Remap aggregate GIDs to LIDs and set up owning processors
for (LO LID = 0; LID < numPoints; LID++) {
if (revMap_.find(getRoot(LID)) != revMap_.end() && rowMap->isNodeGlobalElement(colMap->getGlobalElement(revMap_[getRoot(LID)]))) {
GO aggGID = vertex2AggId[LID];
vertex2AggId[LID] = AggG2L[aggGID];
procWinner [LID] = AggG2R[aggGID];
}
}
GO numGlobalRemote;
MueLu_sumAll(comm, as<GO>(numRemote), numGlobalRemote);
aggregates->AggregatesCrossProcessors(numGlobalRemote);
Set(currentLevel, "Aggregates", aggregates);
GetOStream(Statistics0) << aggregates->description() << std::endl;
}
void UncoupledAggregationFactory_kokkos<LocalOrdinal, GlobalOrdinal, Node>::Build(Level ¤tLevel) const {
FactoryMonitor m(*this, "Build", currentLevel);
ParameterList pL = GetParameterList();
bDefinitionPhase_ = false; // definition phase is finished, now all aggregation algorithm information is fixed
if (pL.get<int>("aggregation: max agg size") == -1)
pL.set("aggregation: max agg size", INT_MAX);
// define aggregation algorithms
RCP<const FactoryBase> graphFact = GetFactory("Graph");
// TODO Can we keep different aggregation algorithms over more Build calls?
algos_.clear();
algos_.push_back(rcp(new PreserveDirichletAggregationAlgorithm_kokkos(graphFact)));
if (pL.get<bool>("aggregation: allow user-specified singletons") == true) algos_.push_back(rcp(new OnePtAggregationAlgorithm_kokkos (graphFact)));
if (pL.get<bool>("aggregation: enable phase 1" ) == true) algos_.push_back(rcp(new AggregationPhase1Algorithm_kokkos (graphFact)));
if (pL.get<bool>("aggregation: enable phase 2a") == true) algos_.push_back(rcp(new AggregationPhase2aAlgorithm_kokkos (graphFact)));
if (pL.get<bool>("aggregation: enable phase 2b") == true) algos_.push_back(rcp(new AggregationPhase2bAlgorithm_kokkos (graphFact)));
if (pL.get<bool>("aggregation: enable phase 3" ) == true) algos_.push_back(rcp(new AggregationPhase3Algorithm_kokkos (graphFact)));
std::string mapOnePtName = pL.get<std::string>("OnePt aggregate map name");
RCP<Map> OnePtMap = Teuchos::null;
if (mapOnePtName.length()) {
std::string mapOnePtFactName = pL.get<std::string>("OnePt aggregate map factory");
if (mapOnePtFactName == "" || mapOnePtFactName == "NoFactory") {
OnePtMap = currentLevel.Get<RCP<Map> >(mapOnePtName, NoFactory::get());
} else {
RCP<const FactoryBase> mapOnePtFact = GetFactory(mapOnePtFactName);
OnePtMap = currentLevel.Get<RCP<Map> >(mapOnePtName, mapOnePtFact.get());
}
}
RCP<const LWGraph_kokkos> graph = Get< RCP<LWGraph_kokkos> >(currentLevel, "Graph");
// Build
RCP<Aggregates_kokkos> aggregates = rcp(new Aggregates_kokkos(*graph));
aggregates->setObjectLabel("UC");
const LO numRows = graph->GetNodeNumVertices();
// construct aggStat information
std::vector<unsigned> aggStat(numRows, READY);
// TODO
//ArrayRCP<const bool> dirichletBoundaryMap = graph->GetBoundaryNodeMap();
ArrayRCP<const bool> dirichletBoundaryMap;
if (dirichletBoundaryMap != Teuchos::null)
for (LO i = 0; i < numRows; i++)
if (dirichletBoundaryMap[i] == true)
aggStat[i] = BOUNDARY;
LO nDofsPerNode = Get<LO>(currentLevel, "DofsPerNode");
GO indexBase = graph->GetDomainMap()->getIndexBase();
if (OnePtMap != Teuchos::null) {
for (LO i = 0; i < numRows; i++) {
// reconstruct global row id (FIXME only works for contiguous maps)
GO grid = (graph->GetDomainMap()->getGlobalElement(i)-indexBase) * nDofsPerNode + indexBase;
for (LO kr = 0; kr < nDofsPerNode; kr++)
if (OnePtMap->isNodeGlobalElement(grid + kr))
aggStat[i] = ONEPT;
}
}
const RCP<const Teuchos::Comm<int> > comm = graph->GetComm();
GO numGlobalRows = 0;
if (IsPrint(Statistics1))
MueLu_sumAll(comm, as<GO>(numRows), numGlobalRows);
LO numNonAggregatedNodes = numRows;
GO numGlobalAggregatedPrev = 0, numGlobalAggsPrev = 0;
for (size_t a = 0; a < algos_.size(); a++) {
std::string phase = algos_[a]->description();
SubFactoryMonitor sfm(*this, "Algo \"" + phase + "\"", currentLevel);
int oldRank = algos_[a]->SetProcRankVerbose(this->GetProcRankVerbose());
algos_[a]->BuildAggregates(pL, *graph, *aggregates, aggStat, numNonAggregatedNodes);
algos_[a]->SetProcRankVerbose(oldRank);
if (IsPrint(Statistics1)) {
GO numLocalAggregated = numRows - numNonAggregatedNodes, numGlobalAggregated = 0;
GO numLocalAggs = aggregates->GetNumAggregates(), numGlobalAggs = 0;
MueLu_sumAll(comm, numLocalAggregated, numGlobalAggregated);
MueLu_sumAll(comm, numLocalAggs, numGlobalAggs);
double aggPercent = 100*as<double>(numGlobalAggregated)/as<double>(numGlobalRows);
if (aggPercent > 99.99 && aggPercent < 100.00) {
// Due to round off (for instance, for 140465733/140466897), we could
// get 100.00% display even if there are some remaining nodes. This
// is bad from the users point of view. It is much better to change
// it to display 99.99%.
aggPercent = 99.99;
}
GetOStream(Statistics1) << " aggregated : " << (numGlobalAggregated - numGlobalAggregatedPrev) << " (phase), " << std::fixed
<< std::setprecision(2) << numGlobalAggregated << "/" << numGlobalRows << " [" << aggPercent << "%] (total)\n"
<< " remaining : " << numGlobalRows - numGlobalAggregated << "\n"
<< " aggregates : " << numGlobalAggs-numGlobalAggsPrev << " (phase), " << numGlobalAggs << " (total)" << std::endl;
numGlobalAggregatedPrev = numGlobalAggregated;
numGlobalAggsPrev = numGlobalAggs;
}
}
TEUCHOS_TEST_FOR_EXCEPTION(numNonAggregatedNodes, Exceptions::RuntimeError, "MueLu::UncoupledAggregationFactory::Build: Leftover nodes found! Error!");
aggregates->AggregatesCrossProcessors(false);
Set(currentLevel, "Aggregates", aggregates);
GetOStream(Statistics1) << aggregates->description() << std::endl;
}
