int main(int argc, char *argv[])
{
// 读取传参，顺序如下：线上环境、线下环境、词表
if (argc != 4) {
printf("Usage: %s onlineEnv offlineEnv dictFilename\n", argv[0]);
exit(-1);
} else {
g_pArgs = &gArgs;
// strcpy(g_pArgs->pOnlineEnv, argv[1]);
// strcpy(g_pArgs->pOfflineEnv, argv[2]);
strcpy(g_pArgs->pOnlineEnv, "http://yf-map-mirror-wpng.vm.baidu.com:8000");
strcpy(g_pArgs->pOfflineEnv, "http://cp01-centos43-testing005.epc.baidu.com:8000");
strcpy(g_pArgs->pDictName, argv[3]);
}
// 读取配置文件初始化词表目录、数据结果目录
if (globalInit() != 0) {
ul_writelog(UL_LOG_FATAL, "globalInit fail, exit!!!");
exit(-1);
}
// strcpy(g_pArgs->pStrTimestamp, "201508052127");
// filterFields();
time_t start, end;
start = time(NULL);
// 根据词表文件多线程发送http请求
sendReqs();
end = time(NULL);
printf("Total cost is : %f\n", difftime(end, start));
// 生成diff结果后，开始对结果进行处理
// TODO
dealResultFiles();
return 0;
}
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