EvaluatePartition<Adapter>::EvaluatePartition(
const RCP<const Environment> &env,
const RCP<const Comm<int> > &problemComm,
const RCP<const typename Adapter::base_adapter_t> &ia,
const PartitioningSolution<Adapter> *soln,
const RCP<const GraphModel<typename Adapter::base_adapter_t> > &graphModel):
env_(env), numGlobalParts_(0), targetGlobalParts_(0),
graphMetrics_(), graphMetricsConst_()
{
env->debug(DETAILED_STATUS,
std::string("Entering EvaluatePartition"));
env->timerStart(MACRO_TIMERS, "Computing graph metrics");
// When we add parameters for which weights to use, we
// should check those here. For now we compute graph metrics
// using all weights.
typedef typename Adapter::part_t part_t;
typedef typename Adapter::base_adapter_t base_adapter_t;
std::bitset<NUM_MODEL_FLAGS> modelFlags;
// Create a GraphModel based on input data.
RCP<GraphModel<base_adapter_t> > graph;
if (graphModel == Teuchos::null)
graph = rcp(new GraphModel<base_adapter_t>(ia,env,problemComm,modelFlags));
// Local number of objects.
size_t numLocalObjects = ia->getLocalNumIDs();
// Parts to which objects are assigned.
const part_t *parts;
if (soln) {
parts = soln->getPartListView();
env->localInputAssertion(__FILE__, __LINE__, "parts not set",
((numLocalObjects == 0) || parts), BASIC_ASSERTION);
} else {
part_t *procs = new part_t [numLocalObjects];
for (size_t i=0; i<numLocalObjects; i++) procs[i] = problemComm->getRank();
parts = procs;
}
ArrayView<const part_t> partArray(parts, numLocalObjects);
ArrayRCP<scalar_t> globalSums;
if (graphModel == Teuchos::null) {
try{
globalWeightedCutsByPart<Adapter>(env,
problemComm, graph, partArray,
numGlobalParts_, graphMetrics_,
globalSums);
}
Z2_FORWARD_EXCEPTIONS;
} else {
try{
EvaluatePartition<Adapter>::EvaluatePartition(
const RCP<const Environment> &env,
const RCP<const Comm<int> > &problemComm,
const RCP<const typename Adapter::base_adapter_t> &ia,
const PartitioningSolution<Adapter> *soln,
bool useDegreeAsWeight,
const RCP<const GraphModel<typename Adapter::base_adapter_t> > &graphModel):
env_(env), numGlobalParts_(0), targetGlobalParts_(0), numNonEmpty_(0),
metrics_(), metricsConst_(), graphMetrics_(), graphMetricsConst_()
{
env->debug(DETAILED_STATUS, std::string("Entering EvaluatePartition"));
env->timerStart(MACRO_TIMERS, "Computing metrics");
// When we add parameters for which weights to use, we
// should check those here. For now we compute metrics
// using all weights.
const Teuchos::ParameterList &pl = env->getParameters();
multiCriteriaNorm mcnorm = normBalanceTotalMaximum;
const Teuchos::ParameterEntry *pe = pl.getEntryPtr("partitioning_objective");
if (pe){
std::string strChoice = pe->getValue<std::string>(&strChoice);
if (strChoice == std::string("multicriteria_minimize_total_weight"))
mcnorm = normMinimizeTotalWeight;
else if (strChoice == std::string("multicriteria_minimize_maximum_weight"))
mcnorm = normMinimizeMaximumWeight;
}
try{
objectMetrics<Adapter>(env, problemComm, mcnorm, ia, soln,
useDegreeAsWeight, graphModel, numGlobalParts_,
numNonEmpty_,metrics_);
}
Z2_FORWARD_EXCEPTIONS;
if (soln)
targetGlobalParts_ = soln->getTargetGlobalNumberOfParts();
else
targetGlobalParts_ = problemComm->getSize();
env->timerStop(MACRO_TIMERS, "Computing metrics");
BaseAdapterType inputType = ia->adapterType();
if (inputType == GraphAdapterType ||
inputType == MatrixAdapterType ||
inputType == MeshAdapterType){
env->timerStart(MACRO_TIMERS, "Computing graph metrics");
// When we add parameters for which weights to use, we
// should check those here. For now we compute graph metrics
// using all weights.
std::bitset<NUM_MODEL_FLAGS> modelFlags;
// Create a GraphModel based on input data.
RCP<GraphModel<base_adapter_t> > graph;
if (graphModel == Teuchos::null)
graph=rcp(new GraphModel<base_adapter_t>(ia,env,problemComm,modelFlags));
// Local number of objects.
size_t numLocalObjects = ia->getLocalNumIDs();
// Parts to which objects are assigned.
const part_t *parts;
if (soln) {
parts = soln->getPartListView();
env->localInputAssertion(__FILE__, __LINE__, "parts not set",
((numLocalObjects == 0) || parts), BASIC_ASSERTION);
} else {
part_t *procs = new part_t [numLocalObjects];
for (size_t i=0; i<numLocalObjects; i++) procs[i]=problemComm->getRank();
parts = procs;
}
ArrayView<const part_t> partArray(parts, numLocalObjects);
ArrayRCP<scalar_t> globalSums;
if (graphModel == Teuchos::null) {
try{
globalWeightedCutsByPart<Adapter>(env,
problemComm, graph, partArray,
numGlobalParts_, graphMetrics_,
globalSums);
}
Z2_FORWARD_EXCEPTIONS;
} else {
try{
void EvaluatePartition<Adapter>::sharedConstructor(
const Adapter *ia,
ParameterList *p,
const RCP<const Comm<int> > &comm,
const PartitioningSolution<Adapter> *soln,
const RCP<const GraphModel<typename Adapter::base_adapter_t> > &graphModel)
{
RCP<const Comm<int> > problemComm;
if (comm == Teuchos::null) {
problemComm = DefaultComm<int>::getComm();//communicator is Teuchos default
} else {
problemComm = comm;
}
RCP<Environment> env = rcp(new Environment(*p, problemComm));
env->debug(DETAILED_STATUS, std::string("Entering EvaluatePartition"));
env->timerStart(MACRO_TIMERS, "Computing metrics");
// When we add parameters for which weights to use, we
// should check those here. For now we compute metrics
// using all weights.
multiCriteriaNorm mcnorm = normBalanceTotalMaximum;
const Teuchos::ParameterEntry *pe = p->getEntryPtr("partitioning_objective");
if (pe){
std::string strChoice = pe->getValue<std::string>(&strChoice);
if (strChoice == std::string("multicriteria_minimize_total_weight"))
mcnorm = normMinimizeTotalWeight;
else if (strChoice == std::string("multicriteria_minimize_maximum_weight"))
mcnorm = normMinimizeMaximumWeight;
}
const RCP<const base_adapter_t> bia =
rcp(dynamic_cast<const base_adapter_t *>(ia), false);
try{
objectMetrics<Adapter>(env, problemComm, mcnorm, ia, soln, graphModel,
numGlobalParts_, numNonEmpty_,metrics_);
}
Z2_FORWARD_EXCEPTIONS;
if (soln)
targetGlobalParts_ = soln->getTargetGlobalNumberOfParts();
else
targetGlobalParts_ = problemComm->getSize();
env->timerStop(MACRO_TIMERS, "Computing metrics");
BaseAdapterType inputType = bia->adapterType();
if (inputType == GraphAdapterType ||
inputType == MatrixAdapterType ||
inputType == MeshAdapterType){
env->timerStart(MACRO_TIMERS, "Computing graph metrics");
// When we add parameters for which weights to use, we
// should check those here. For now we compute graph metrics
// using all weights.
std::bitset<NUM_MODEL_FLAGS> modelFlags;
// Create a GraphModel based on input data.
RCP<GraphModel<base_adapter_t> > graph;
if (graphModel == Teuchos::null)
graph = rcp(new GraphModel<base_adapter_t>(bia, env, problemComm,
modelFlags));
// Local number of objects.
size_t numLocalObjects = bia->getLocalNumIDs();
// Parts to which objects are assigned.
const part_t *parts;
if (soln) {
// User provided a partitioning solution; use it.
parts = soln->getPartListView();
env->localInputAssertion(__FILE__, __LINE__, "parts not set",
((numLocalObjects == 0) || parts), BASIC_ASSERTION);
} else {
// User did not provide a partitioning solution;
// Use input adapter partition.
parts = NULL;
bia->getPartsView(parts);
if (parts == NULL) {
// User has not provided input parts in input adapter
part_t *procs = new part_t [numLocalObjects];
for (size_t i=0;i<numLocalObjects;i++) procs[i]=problemComm->getRank();
parts = procs;
}
}
ArrayView<const part_t> partArray(parts, numLocalObjects);
ArrayRCP<scalar_t> globalSums;
if (graphModel == Teuchos::null) {
try{
globalWeightedCutsByPart<Adapter>(env,
problemComm, graph, partArray,
numGlobalParts_, graphMetrics_,
globalSums);
}
Z2_FORWARD_EXCEPTIONS;
} else {
try{
