void Teuchos::updateParametersFromYamlFileAndBroadcast(
const std::string &yamlFileName,
const Teuchos::Ptr<Teuchos::ParameterList> ¶mList,
const Teuchos::Comm<int> &comm,
bool overwrite)
{
struct SafeFile
{
SafeFile(const char* fname, const char* options)
{
handle = fopen(fname, options);
}
~SafeFile()
{
if(handle)
fclose(handle);
}
FILE* handle;
};
//BMK note: see teuchos/comm/src/Teuchos_XMLParameterListHelpers.cpp
if(comm.getSize() == 1)
{
updateParametersFromYamlFile(yamlFileName, paramList);
}
else
{
if(comm.getRank() == 0)
{
//BMK: TODO! //reader.setAllowsDuplicateSublists(false);
//create a string and load file contents into it
//C way for readability and speed, same thing with C++ streams is slow & ugly
SafeFile yamlFile(yamlFileName.c_str(), "rb");
if(!yamlFile.handle)
{
throw std::runtime_error(std::string("Failed to open YAML file \"") + yamlFileName + "\"for reading.");
}
fseek(yamlFile.handle, 0, SEEK_END);
int strsize = ftell(yamlFile.handle) + 1;
rewind(yamlFile.handle);
//Make the array raii
Teuchos::ArrayRCP<char> contents(new char[strsize], 0, strsize, true);
fread((void*) contents.get(), strsize - 1, 1, yamlFile.handle);
contents.get()[strsize - 1] = 0;
Teuchos::broadcast<int, int>(comm, 0, &strsize);
Teuchos::broadcast<int, char>(comm, 0, strsize, contents.get());
updateParametersFromYamlCString(contents.get(), paramList, overwrite);
}
else
{
int strsize;
Teuchos::broadcast<int, int>(comm, 0, &strsize);
Teuchos::ArrayRCP<char> contents(new char[strsize], 0, strsize, true);
Teuchos::broadcast<int, char>(comm, 0, strsize, contents.get());
updateParametersFromYamlCString(contents.get(), paramList, overwrite);
}
}
}
void
gathervPrint (std::ostream& out,
const std::string& s,
const Teuchos::Comm<int>& comm)
{
using Teuchos::ArrayRCP;
using Teuchos::CommRequest;
using Teuchos::ireceive;
using Teuchos::isend;
using Teuchos::outArg;
using Teuchos::RCP;
using Teuchos::wait;
const int myRank = comm.getRank ();
const int rootRank = 0;
if (myRank == rootRank) {
out << s; // Proc 0 prints its buffer first
}
const int numProcs = comm.getSize ();
const int sizeTag = 42;
const int msgTag = 43;
ArrayRCP<size_t> sizeBuf (1);
ArrayRCP<char> msgBuf; // to be resized later
RCP<CommRequest<int> > req;
for (int p = 1; p < numProcs; ++p) {
if (myRank == p) {
sizeBuf[0] = s.size ();
req = isend<int, size_t> (sizeBuf, rootRank, sizeTag, comm);
(void) wait<int> (comm, outArg (req));
const size_t msgSize = s.size ();
msgBuf.resize (msgSize + 1); // for the '\0'
std::copy (s.begin (), s.end (), msgBuf.begin ());
msgBuf[msgSize] = '\0';
req = isend<int, char> (msgBuf, rootRank, msgTag, comm);
(void) wait<int> (comm, outArg (req));
}
else if (myRank == rootRank) {
sizeBuf[0] = 0; // just a precaution
req = ireceive<int, size_t> (sizeBuf, p, sizeTag, comm);
(void) wait<int> (comm, outArg (req));
const size_t msgSize = sizeBuf[0];
msgBuf.resize (msgSize + 1); // for the '\0'
req = ireceive<int, char> (msgBuf, p, msgTag, comm);
(void) wait<int> (comm, outArg (req));
std::string msg (msgBuf.getRawPtr ());
out << msg;
}
}
}
void run_samples(
const Teuchos::Comm<int>& comm ,
ProblemType& problem,
const CoeffFunctionType& coeff_function,
const Teuchos::RCP<Kokkos::Example::FENL::SampleGrouping<double> >& grouper,
const Teuchos::RCP<Teuchos::ParameterList>& fenlParams,
const CMD & cmd ,
const double bc_lower_value,
const double bc_upper_value,
const Teuchos::Array< Teuchos::Array<double> >& points,
Teuchos::Array<double>& responses,
Teuchos::Array<int>& iterations,
Kokkos::Example::FENL::Perf& perf_total)
{
typedef typename CoeffFunctionType::RandomVariableView RV;
typedef typename RV::HostMirror HRV;
RV rv = coeff_function.getRandomVariables();
HRV hrv = Kokkos::create_mirror_view(rv);
const int num_samples = points.size();
const int dim = rv.dimension_0();;
for (int sample=0; sample<num_samples; ++sample) {
// Set random variable values to this sample
for (int i=0; i<dim; ++i)
hrv(i) = points[sample][i];
Kokkos::deep_copy( rv, hrv );
// Evaluate response at quadrature point
double response = 0;
Kokkos::Example::FENL::Perf perf =
fenl( problem , fenlParams ,
cmd.PRINT , cmd.USE_TRIALS , cmd.USE_ATOMIC ,
cmd.USE_BELOS , cmd.USE_MUELU , cmd.USE_MEANBASED ,
coeff_function , cmd.USE_ISOTROPIC ,
cmd.USE_COEFF_SRC , cmd.USE_COEFF_ADV ,
bc_lower_value , bc_upper_value ,
response);
responses[sample] = response;
iterations[sample] = perf.cg_iter_count;
if (cmd.PRINT_ITS && 0 == comm.getRank()) {
std::cout << sample << " : " << perf.cg_iter_count << " ( ";
for (int i=0; i<dim; ++i)
std::cout << hrv(i) << " ";
std::cout << ")" << std::endl;
}
// Increment timing statistics
perf_total.increment(perf, !cmd.USE_BELOS);
}
}
// Print memory usage to stream
void print_memory_usage(std::ostream& s, const Teuchos::Comm<int>& comm) {
MemUsage mem = get_memory_usage(comm);
if ( 0 == comm.getRank() ) {
s << std::fixed;
s.precision(3);
s << "Memory usage across all processors (MB):" << std::endl
<< "\t Max: " << mem.max_mem << std::endl
<< "\t Min: " << mem.min_mem << std::endl
<< "\t Tot: " << mem.tot_mem << std::endl;
}
}
DefaultMappingStrategy::DefaultMappingStrategy(const RCP<const Thyra::LinearOpBase<double> > & thyraOp,const Teuchos::Comm<Thyra::Ordinal> & comm)
{
RCP<Teuchos::Comm<Thyra::Ordinal> > newComm = comm.duplicate();
// extract vector spaces from linear operator
domainSpace_ = thyraOp->domain();
rangeSpace_ = thyraOp->range();
domainMap_ = Teko::TpetraHelpers::thyraVSToTpetraMap(*domainSpace_,newComm);
rangeMap_ = Teko::TpetraHelpers::thyraVSToTpetraMap(*rangeSpace_,newComm);
}
void ReportTimeAndMemory(Teuchos::Time const &timer, Teuchos::Comm<int> const &Comm)
{
double maxTime=0,minTime=0,avgTime=0;
double localTime = timer.totalElapsedTime();
int ntimers=1, root=0;
#ifdef HAVE_MPI
MPI_Reduce(&localTime,&maxTime,ntimers,MPI_DOUBLE,MPI_MAX,root,MPI_COMM_WORLD);
MPI_Reduce(&localTime,&minTime,ntimers,MPI_DOUBLE,MPI_MIN,root,MPI_COMM_WORLD);
MPI_Reduce(&localTime,&avgTime,ntimers,MPI_DOUBLE,MPI_SUM,root,MPI_COMM_WORLD);
#else
maxTime = localTime;
minTime = localTime;
avgTime = localTime;
#endif
avgTime /= Comm.getSize();
//std::cout << "(" << Comm.getRank() << ") " << localTime << std::endl;
if (Comm.getRank()==0) {
std::cout << "&&&" << timer.name()
<< " max=" << maxTime << " min=" << minTime << " avg=" << avgTime << std::endl;
std::cout << "&&&" << timer.name() << " " << MemUtils::PrintMemoryUsage() << std::endl;
}
} //ReportTimeAndMemory
unsigned int generateSeed(Teuchos::Comm<int> const &comm, const double initSeed)
{
timeval t1;
gettimeofday(&t1, NULL);
unsigned int seed;
if (initSeed > -1) seed = Teuchos::as<unsigned int>(initSeed);
else seed = t1.tv_usec * t1.tv_sec;
// use variant of proc 0's seed so we can always reproduce the results
const Teuchos::MpiComm<int> &mpicomm = dynamic_cast<const Teuchos::MpiComm<int> &>(comm);
TEUCHOS_TEST_FOR_EXCEPTION(&mpicomm==0,MueLu::Exceptions::RuntimeError,"cast to MpiComm failed");
MPI_Bcast((void*)&seed,1,MPI_UNSIGNED,0,*(mpicomm.getRawMpiComm()));
seed = seed * (1+comm.getRank());
return seed;
}
void run_file(
const Teuchos::Comm<int>& comm ,
ProblemType& problem ,
const CoeffFunctionType & coeff_function,
const Teuchos::RCP<Kokkos::Example::FENL::SampleGrouping<double> >& grouper,
const Teuchos::RCP<Teuchos::ParameterList>& fenlParams,
const CMD & cmd ,
const double bc_lower_value,
const double bc_upper_value,
Kokkos::Example::FENL::Perf& perf_total)
{
using Teuchos::Array;
const int dim = cmd.USE_UQ_DIM;
int num_quad_points;
Array< Array<double > > quad_points;
// Open and read sample points
std::ifstream fin("samples.txt");
fin >> num_quad_points;
quad_points.resize(num_quad_points);
for (int i=0; i<num_quad_points; ++i) {
quad_points[i].resize(dim);
for (int j=0; j<dim; ++j)
fin >> quad_points[i][j];
}
fin.close();
// Evaluate response at each quadrature point
Array<double> responses(num_quad_points);
Array<int> iterations(num_quad_points);
run_samples(comm, problem, coeff_function, grouper,
fenlParams, cmd,
bc_lower_value, bc_upper_value,
quad_points, responses, iterations, perf_total);
// Write responses to file, including solver iterations
if (comm.getRank() == 0) {
std::ofstream fout("responses.txt");
fout << num_quad_points << std::endl;
for (int i=0; i<num_quad_points; ++i) {
fout << responses[i] << " " << iterations[i] << std::endl;
}
fout.close();
}
perf_total.response_mean = 0.0;
perf_total.response_std_dev = 0.0;
}
//////////////////////////////////////////////////////////////////////////////
//
// printMemoryUsage()
//
//////////////////////////////////////////////////////////////////////////////
void printMemoryUsage(std::ostream& s, const Teuchos::Comm<int>& comm,
const MemUsage& mem)
{
using std::endl;
if (0 == comm.getRank())
{
s << "Estimated memory usage across all processors:" << endl
<< " Current Peak " << endl
<< " ------------ ------------" << endl << " Min: ";
pretty(s, mem.currMin); pretty(s, mem.peakMin); s << endl << " Max: ";
pretty(s, mem.currMax); pretty(s, mem.peakMax); s << endl << " Tot: ";
pretty(s, mem.currTot); pretty(s, mem.peakTot); s << endl;
}
return;
} // end of printMemoryUsage()
Teuchos::RCP<NodeType>
createKokkosNode( const CMD & cmd , const Teuchos::Comm<int>& comm ) {
Teuchos::ParameterList params;
params.set("Verbose", 0);
if ( cmd.USE_THREADS )
params.set("Num Threads", cmd.USE_THREADS);
else if ( cmd.USE_OPENMP )
params.set("Num Threads", cmd.USE_OPENMP);
if ( cmd.USE_NUMA && cmd.USE_CORE_PER_NUMA ) {
params.set("Num NUMA", cmd.USE_NUMA );
params.set("Num CoresPerNUMA", cmd.USE_CORE_PER_NUMA );
}
if ( cmd.USE_CUDA )
params.set("Device", cmd.USE_CUDA_DEV );
Teuchos::RCP<NodeType> node = Teuchos::rcp (new NodeType(params));
if ( cmd.VERBOSE ) {
typedef typename NodeType::execution_space Device;
if (comm.getRank() == 0)
Device::print_configuration(std::cout);
std::cout.flush();
if ( cmd.USE_CUDA ) {
for (int i=0; i<comm.getSize(); ++i) {
comm.barrier();
comm.barrier();
comm.barrier();
if ( i == comm.getRank() ) {
std::cout << "MPI rank " << comm.getRank()
<< " attached to CUDA device "
<< cmd.USE_CUDA_DEV << std::endl;
std::cout.flush();
}
comm.barrier();
comm.barrier();
comm.barrier();
}
}
}
return node;
}
Ordinal SpmdVectorSpaceUtilities::computeMapCode(
const Teuchos::Comm<Ordinal> &comm, const Ordinal localSubDim
)
{
using Teuchos::outArg;
using Teuchos::REDUCE_SUM;
using Teuchos::reduceAll;
//
// Here we will make a map code out of just the local sub-dimension on each
// processor. If each processor has the same number of local elements, then
// the map codes will be the same and this is all you need for RTOp
// compatibility.
//
const int procRank = comm.getSize ();
Ordinal mapCode = -1;
Ordinal localCode = localSubDim % (procRank+1) + localSubDim;
reduceAll<Ordinal, Ordinal> (comm, REDUCE_SUM, localCode, outArg (mapCode));
return mapCode;
}
// TODO: this function can be templated (T=double).
ArrayRCP<double> ReduceMaxMinAvg(double localValue, Teuchos::Comm<int> const &comm, int rootNode) {
ArrayRCP<double> r = ArrayRCP<double>(3, localValue);
#ifdef HAVE_MPI
double & maxTime = r[0], & minTime = r[1], & avgTime = r[2];
// Note: workaround because reduce() is not implemented in Teuchos::Comm
const Teuchos::MpiComm<int> & mpiComm = dynamic_cast<const Teuchos::MpiComm<int>& >(comm);
MPI_Comm rawMpiComm = (*mpiComm.getRawMpiComm())();
//
// DEBUG std::cout << comm.getRank() << ": " << localValue << std::endl;
int ntimers=1;
MPI_Reduce(&localValue, &maxTime, ntimers, MPI_DOUBLE, MPI_MAX, rootNode, rawMpiComm);
MPI_Reduce(&localValue, &minTime, ntimers, MPI_DOUBLE, MPI_MIN, rootNode, rawMpiComm);
MPI_Reduce(&localValue, &avgTime, ntimers, MPI_DOUBLE, MPI_SUM, rootNode, rawMpiComm); avgTime /= comm.getSize();
#endif // HAVE_MPI
return r;
}
void run_samples(
const Teuchos::Comm<int>& comm ,
Kokkos::Example::FENL::Problem< Sacado::MP::Vector<Storage>, Device, ElemOrder>& problem ,
const CoeffFunctionType & coeff_function,
const Teuchos::RCP<Kokkos::Example::FENL::SampleGrouping<double> >& grouper,
const Teuchos::RCP<Teuchos::ParameterList>& fenlParams,
const CMD & cmd ,
const double bc_lower_value,
const double bc_upper_value,
const Teuchos::Array< Teuchos::Array<double> >& points,
Teuchos::Array<double>& responses,
Teuchos::Array<int>& iterations,
Kokkos::Example::FENL::Perf& perf_total)
{
using Teuchos::Array;
using Teuchos::Ordinal;
typedef typename Sacado::MP::Vector<Storage> Scalar;
typedef typename CoeffFunctionType::RandomVariableView RV;
typedef typename RV::HostMirror HRV;
static const int VectorSize = Storage::static_size;
// Group points into ensembles
Array< Array<Ordinal> > groups;
Ordinal num_duplicate = 0;
grouper->group(VectorSize, points, groups, num_duplicate);
const int num_groups = groups.size();
RV rv = coeff_function.getRandomVariables();
HRV hrv = Kokkos::create_mirror_view(rv);
const int dim = rv.dimension_0();
// Loop over quadrature point groups
for (int group=0; group<num_groups; ++group) {
// Set random variables
for (int qp=0; qp<VectorSize; ++qp)
for (int i=0; i<dim; ++i)
hrv(i).fastAccessCoeff(qp) = points[groups[group][qp]][i];
Kokkos::deep_copy( rv, hrv );
// Evaluate response at quadrature point
Scalar response = 0;
Kokkos::Example::FENL::Perf perf =
fenl( problem , fenlParams ,
cmd.PRINT , cmd.USE_TRIALS , cmd.USE_ATOMIC ,
cmd.USE_BELOS , cmd.USE_MUELU , cmd.USE_MEANBASED ,
coeff_function , cmd.USE_ISOTROPIC ,
cmd.USE_COEFF_SRC , cmd.USE_COEFF_ADV ,
bc_lower_value , bc_upper_value ,
response);
// Save response -- note currently all samples within an ensemble
// get the same number of iterations
for (int qp=0; qp<VectorSize; ++qp) {
responses[groups[group][qp]] = response.coeff(qp);
iterations[groups[group][qp]] = perf.cg_iter_count;
}
if (cmd.PRINT_ITS && 0 == comm.getRank()) {
std::cout << group << " : " << perf.cg_iter_count << " ( ";
for (int qp=0; qp<VectorSize; ++qp)
std::cout << groups[group][qp] << " ";
std::cout << ")";
std::cout << " ( ";
for (int i=0; i<dim; ++i)
std::cout << hrv(i) << " ";
std::cout << ")" << std::endl;
}
// Adjust timing statistics for ensemble size
perf.newton_iter_count *= VectorSize;
perf.cg_iter_count *= VectorSize;
perf.map_ratio *= VectorSize;
perf.fill_node_set *= VectorSize;
perf.scan_node_count *= VectorSize;
perf.fill_graph_entries *= VectorSize;
perf.sort_graph_entries *= VectorSize;
perf.fill_element_graph *= VectorSize;
// Increment timing statistics
perf_total.increment(perf, !cmd.USE_BELOS);
}
}
void run_tasmanian(
const Teuchos::Comm<int>& comm ,
ProblemType& problem ,
const CoeffFunctionType & coeff_function,
const Teuchos::RCP<Kokkos::Example::FENL::SampleGrouping<double> >& grouper,
const Teuchos::RCP<Teuchos::ParameterList>& fenlParams,
const CMD & cmd ,
const double bc_lower_value,
const double bc_upper_value,
Kokkos::Example::FENL::Perf& perf_total)
{
#ifdef HAVE_TRILINOSCOUPLINGS_TASMANIAN
using Teuchos::Array;
// Start up Tasmanian
TasGrid::TasmanianSparseGrid sparseGrid;
// Algorithmic parameters
const int dim = cmd.USE_UQ_DIM;
const int qoi = 2;
const int initial_level = cmd.USE_UQ_INIT_LEVEL;
const int max_level = cmd.USE_UQ_MAX_LEVEL;
const int max_order = 1;
const double tol = cmd.USE_UQ_TOL;
const TasGrid::TypeOneDRule rule = TasGrid::rule_localp;
const TasGrid::TypeRefinement refinement = TasGrid::refine_classic;
const int qoi_to_refine = 0;
// Create the initial grid
sparseGrid.makeLocalPolynomialGrid(dim, qoi, initial_level, max_order, rule);
int num_new_points = sparseGrid.getNumNeeded();
perf_total.uq_count = num_new_points;
int level = initial_level;
while (num_new_points > 0 && level <= max_level) {
if (cmd.PRINT_ITS && 0 == comm.getRank()) {
std::cout << "Tasmanian grid level " << level
<< ", " << num_new_points << " points"
<< std::endl;
}
// Get the sample points
const double *points = sparseGrid.getNeededPoints();
// Copy points into Teuchos arrays
Array< Array<double> > quad_points(num_new_points);
for (int i=0; i<num_new_points; ++i) {
quad_points[i].resize(dim);
for (int j=0; j<dim; ++j)
quad_points[i][j] = points[dim*i+j];
}
// Evaluate response on those points
Array<double> responses(num_new_points);
Array<int> iterations(num_new_points);
run_samples(comm, problem, coeff_function, grouper,
fenlParams, cmd,
bc_lower_value, bc_upper_value,
quad_points, responses, iterations, perf_total);
// Load responses back into Tasmanian
Array<double> tas_responses(qoi*num_new_points);
for (int i=0; i<num_new_points; ++i) {
tas_responses[i*qoi] = responses[i]; // for mean
tas_responses[i*qoi+1] = responses[i]*responses[i]; // for variance
}
sparseGrid.loadNeededPoints(&tas_responses[0]);
// Refine the grid
sparseGrid.setSurplusRefinement(tol, refinement, qoi_to_refine);
// Get the number of new points
num_new_points = sparseGrid.getNumNeeded();
perf_total.uq_count += num_new_points;
++level;
}
if (level > max_level && comm.getRank() == 0)
std::cout << "Warning: Tasmanian did not achieve refinement tolerance "
<< tol << std::endl;
// Compute mean and standard deviation of response
double s[qoi];
sparseGrid.integrate(s);
const double weight = std::pow(0.5, dim); // uniform measure in dim dimensions
s[0] *= weight; s[1] *= weight;
perf_total.response_mean = s[0];
perf_total.response_std_dev = std::sqrt(s[1]-s[0]*s[0]);
#else
TEUCHOS_TEST_FOR_EXCEPTION(true, std::logic_error, "TASMANIAN not available. Please re-configure with TASMANIAN TPL enabled.");
#endif
}
Machine(const Teuchos::Comm<int> &comm) :
numRanks(comm.getSize()), myRank(comm.getRank())
{ }
