void TwoParticleGF::compute(bool clear, const boost::mpi::communicator & comm)
{
if (Status < Prepared) throw (exStatusMismatch());
if (Status >= Computed) return;
if (!Vanishing) {
// Create a "skeleton" class with pointers to part that can call a compute method
pMPI::mpi_skel<ComputeAndClearWrap> skel;
bool fill_container = m_data_.NBosonic() > 0 && m_data_.NFermionic() > 0;
skel.parts.reserve(parts.size());
for (size_t i=0; i<parts.size(); i++) {
skel.parts.push_back(ComputeAndClearWrap(&m_data_, parts[i], clear, fill_container, 1));
};
std::map<pMPI::JobId, pMPI::WorkerId> job_map = skel.run(comm, true); // actual running - very costly
int rank = comm.rank();
int comm_size = comm.size();
// Start distributing data
//DEBUG(comm.rank() << getIndex(0) << getIndex(1) << getIndex(2) << getIndex(3) << " Start distributing data");
comm.barrier();
if (!clear) {
for (size_t p = 0; p<parts.size(); p++) {
boost::mpi::broadcast(comm, parts[p]->NonResonantTerms, job_map[p]);
boost::mpi::broadcast(comm, parts[p]->ResonantTerms, job_map[p]);
if (rank == job_map[p]) {
parts[p]->Status = TwoParticleGFPart::Computed;
};
};
comm.barrier();
}
};
Status = Computed;
}
void
printit(const boost::mpi::communicator& comm,
const std::vector<T>& things,
const std::string& caption)
{
if (!caption.empty() && comm.rank() == 0) {
std::cout << caption << std::endl;
std::cout.flush();
}
for (int p = 0; p < comm.size(); ++p) {
if (comm.rank() == p) {
std::cout << p << ": ";
std::copy(things.begin(), things.end(),
std::ostream_iterator<T>(std::cout, ","));
std::cout << std::endl;
std::cout.flush();
}
comm.barrier();
}
comm.barrier();
size_t global_size;
boost::mpi::reduce(comm, things.size(), global_size, std::plus<size_t>(), 0);
if (comm.rank() == 0) {
if (!caption.empty()) {
std::cout << caption;
}
std::cout << "Number of things: " << global_size << std::endl;
}
comm.barrier();
std::cout << comm.rank() << ": leaving printit()" << std::endl;
}
void TwoParticleGFContainer::computeAll_split(bool clearTerms, const boost::mpi::communicator & comm)
{
// split communicator
size_t ncomponents = NonTrivialElements.size();
size_t ncolors = std::min(int(comm.size()), int(NonTrivialElements.size()));
RealType color_size = 1.0*comm.size()/ncolors;
std::map<int,int> proc_colors;
std::map<int,int> elem_colors;
std::map<int,int> color_roots;
bool calc = false;
for (size_t p=0; p<comm.size(); p++) {
int color = int (1.0*p / color_size);
proc_colors[p] = color;
color_roots[color]=p;
}
for (size_t i=0; i<ncomponents; i++) {
int color = i*ncolors/ncomponents;
elem_colors[i] = color;
};
if (!comm.rank()) {
INFO("Splitting " << ncomponents << " components in " << ncolors << " communicators");
for (size_t i=0; i<ncomponents; i++)
INFO("2pgf " << i << " color: " << elem_colors[i] << " color_root: " << color_roots[elem_colors[i]]);
};
comm.barrier();
int comp = 0;
boost::mpi::communicator comm_split = comm.split(proc_colors[comm.rank()]);
for(std::map<IndexCombination4, boost::shared_ptr<TwoParticleGF> >::iterator iter = NonTrivialElements.begin(); iter != NonTrivialElements.end(); iter++, comp++) {
bool calc = (elem_colors[comp] == proc_colors[comm.rank()]);
if (calc) {
INFO("C" << elem_colors[comp] << "p" << comm.rank() << ": computing 2PGF for " << iter->first);
if (calc) static_cast<TwoParticleGF&>(*(iter->second)).compute(clearTerms, comm_split);
};
};
comm.barrier();
// distribute data
if (!comm.rank()) INFO_NONEWLINE("Distributing 2PGF container...");
comp = 0;
for(std::map<IndexCombination4, boost::shared_ptr<TwoParticleGF> >::iterator iter = NonTrivialElements.begin(); iter != NonTrivialElements.end(); iter++, comp++) {
int sender = color_roots[elem_colors[comp]];
TwoParticleGF& chi = *((iter)->second);
for (size_t p = 0; p<chi.parts.size(); p++) {
// if (comm.rank() == sender) INFO("P" << comm.rank() << " 2pgf " << p << " " << chi.parts[p]->NonResonantTerms.size());
boost::mpi::broadcast(comm, chi.parts[p]->NonResonantTerms, sender);
boost::mpi::broadcast(comm, chi.parts[p]->ResonantTerms, sender);
if (comm.rank() != sender) {
chi.setStatus(TwoParticleGF::Computed);
};
};
}
comm.barrier();
if (!comm.rank()) INFO("done.");
}
/**
* workerloop runs on all workers and executes worker functions
*
* the worker loop waits for wrapper objects to be broadcastet to all
* workers, if the wrapper is a quit objects it shuts down, if the wrapper is
* a work object it executes it
*
* @params comm the communicator object the workers and the master live in
*
* @params root the id of the root or master node that distribute the jobs
*/
inline void workerloop(boost::mpi::communicator & comm, int root)
{
while(true)
{
masterworker::wrapper w;
mpi_broadcast_workaround(comm, w, root);
if(w.quit())
{
comm.barrier();
return;
}
else
{
w.execute(comm, root);
}
}
}
void
broadcast_test(const mpi::communicator& comm, const T& bc_value,
std::string const& kind, int root) {
using boost::mpi::broadcast;
T value;
if (comm.rank() == root) {
value = bc_value;
std::cout << "Broadcasting " << kind << " from root " << root << "...";
std::cout.flush();
}
broadcast(comm, value, root);
BOOST_CHECK(value == bc_value);
if (comm.rank() == root) {
if (value == bc_value) {
std::cout << "OK." << std::endl;
} else {
std::cout << "FAIL." << std::endl;
}
}
comm.barrier();
}
inline void barrier(void) { world->barrier(); }
int run(const boost::mpi::communicator& comm, skylark::base::context_t& context,
hilbert_options_t& options) {
int rank = comm.rank();
InputType X, Xv, Xt;
LabelType Y, Yv, Yt;
if(!options.trainfile.empty()) { //training mode
read(comm, options.fileformat, options.trainfile, X, Y);
int dimensions = skylark::base::Height(X);
int targets = GetNumTargets<LabelType>(comm, Y);
bool shift = false;
if ((options.lossfunction == LOGISTIC) && (targets == 1)) {
ShiftForLogistic(Y);
targets = 2;
shift = true;
}
BlockADMMSolver<InputType>* Solver =
GetSolver<InputType>(context, options, dimensions);
if(!options.valfile.empty()) {
comm.barrier();
if(rank == 0) std::cout << "Loading validation data." << std::endl;
read(comm, options.fileformat, options.valfile, Xv, Yv,
skylark::base::Height(X));
if ((options.lossfunction == LOGISTIC) && shift) {
ShiftForLogistic(Yv);
}
}
skylark::ml::model_t<InputType, LabelType>* model =
Solver->train(X, Y, Xv, Yv, comm);
if (comm.rank() == 0)
model->save(options.modelfile, options.print());
}
else {
std::cout << "Testing Mode (currently loads test data in memory)" << std::endl;
skylark::ml::model_t<InputType, LabelType> model(options.modelfile);
read(comm, options.fileformat, options.testfile, Xt, Yt,
model.get_input_size());
LabelType DecisionValues(Yt.Height(), model.get_num_outputs());
LabelType PredictedLabels(Yt.Height(), 1);
El::Zero(DecisionValues);
El::Zero(PredictedLabels);
std::cout << "Starting predictions" << std::endl;
model.predict(Xt, PredictedLabels, DecisionValues, options.numthreads);
double accuracy = model.evaluate(Yt, DecisionValues, comm);
if(rank == 0)
std::cout << "Test Accuracy = " << accuracy << " %" << std::endl;
// fix logistic case -- provide mechanism to dump predictions -- clean up evaluate
}
return 0;
}
