int WalkerControlBase::branch(int iter, MCWalkerConfiguration& W, RealType trigger) {
int prefill_numwalkers = sortWalkers(W);
measureProperties(iter);
W.EnsembleProperty=EnsembleProperty;
//un-biased variance but we use the saimple one
//W.EnsembleProperty.Variance=(e2sum*wsum-esum*esum)/(wsum*wsum-w2sum);
////add to the accumData for block average: REMOVE THIS
//accumData[ENERGY_INDEX] += curData[ENERGY_INDEX]*wgtInv;
//accumData[ENERGY_SQ_INDEX] += curData[ENERGY_SQ_INDEX]*wgtInv;
//accumData[WALKERSIZE_INDEX] += curData[WALKERSIZE_INDEX];
//accumData[WEIGHT_INDEX] += curData[WEIGHT_INDEX];
int nw_tot = copyWalkers(W);
//set Weight and Multiplicity to default values
MCWalkerConfiguration::iterator it(W.begin()),it_end(W.end());
while(it != it_end) {
(*it)->Weight= 1.0;
(*it)->Multiplicity=1.0;
++it;
}
//set the global number of walkers
W.setGlobalNumWalkers(nw_tot);
return prefill_numwalkers;
}
int
WalkerControlMPI::branch(int iter, MCWalkerConfiguration& W, RealType trigger) {
std::fill(curData.begin(),curData.end(),0);
//std::fill(NumPerNode.begin(),NumPerNode.end(),0);
sortWalkers(W);
//update the number of walkers for this node
curData[LE_MAX+MyContext]=NumWalkers;
int nw = copyWalkers(W);
myComm->allreduce(curData);
RealType wgtInv(1.0/curData[WEIGHT_INDEX]);
accumData[ENERGY_INDEX] += curData[ENERGY_INDEX]*wgtInv;
accumData[ENERGY_SQ_INDEX] += curData[ENERGY_SQ_INDEX]*wgtInv;
accumData[WALKERSIZE_INDEX] += curData[WALKERSIZE_INDEX];
accumData[WEIGHT_INDEX] += curData[WEIGHT_INDEX];
Cur_pop=0;
for(int i=0, j=LE_MAX; i<NumContexts; i++,j++) {
Cur_pop+= NumPerNode[i]=static_cast<int>(curData[j]);
}
swapWalkersSimple(W);
//Do not need to use a trigger.
//Cur_min=Nmax;
//Cur_max=0;
//Cur_pop=0;
//for(int i=0, j=LE_MAX; i<NumContexts; i++,j++) {
// Cur_pop+= NumPerNode[i]=static_cast<int>(curData[j]);
// Cur_min = std::min(Cur_min,NumPerNode[i]);
// Cur_max = std::max(Cur_max,NumPerNode[i]);
//}
//int max_diff = std::max(Cur_max*NumContexts-Cur_pop,Cur_pop-Cur_min*NumContexts);
//double diff_pop = static_cast<double>(max_diff)/static_cast<double>(Cur_pop);
//if(diff_pop > trigger) {
// swapWalkersSimple(W);
// //swapWalkersMap(W);
//}
//set Weight and Multiplicity to default values
MCWalkerConfiguration::iterator it(W.begin()),it_end(W.end());
while(it != it_end) {
(*it)->Weight= 1.0;
(*it)->Multiplicity=1.0;
++it;
}
//update the global number of walkers
W.setGlobalNumWalkers(Cur_pop);
return Cur_pop;
}
void SimpleFixedNodeBranch::initWalkerController(MCWalkerConfiguration& walkers, RealType tau, bool fixW)
{
vParam[B_TAU]=tau;
if(!BranchMode[B_DMCSTAGE])
vParam[B_TAUEFF]=tau*R2Accepted.result()/R2Proposed.result();
if(WalkerController == 0)
{
if(iParam[B_TARGETWALKERS]==0)
{
Communicate* acomm=MyEstimator->getCommunicator();
int ncontexts=acomm->size();
vector<int> nw(ncontexts,0),nwoff(ncontexts+1,0);
nw[acomm->rank()]=walkers.getActiveWalkers();
acomm->allreduce(nw);
for(int ip=0; ip<ncontexts; ++ip) nwoff[ip+1]=nwoff[ip]+nw[ip];
walkers.setGlobalNumWalkers(nwoff[ncontexts]);
walkers.setWalkerOffsets(nwoff);
iParam[B_TARGETWALKERS]=nwoff[ncontexts];
}
BranchMode.set(B_DMC,1);//set DMC
BranchMode.set(B_POPCONTROL,!fixW);//fixW -> 0
WalkerController = createWalkerController(iParam[B_TARGETWALKERS], MyEstimator->getCommunicator(), myNode);
iParam[B_MAXWALKERS]=WalkerController->Nmax;
iParam[B_MINWALKERS]=WalkerController->Nmin;
if(!fixW && sParam[MIXDMCOPT]=="yes")
{
app_log() << "Warmup DMC is done with a fixed population " << iParam[B_TARGETWALKERS] << endl;
BackupWalkerController=WalkerController; //save the main controller
WalkerController=createWalkerController(iParam[B_TARGETWALKERS],MyEstimator->getCommunicator(), myNode,true);
BranchMode.set(B_POPCONTROL,0);
}
WalkerController->setWalkerID(walkers);
PopHist.clear();
PopHist.reserve(std::max(iParam[B_ENERGYUPDATEINTERVAL],5));
}
//save the BranchMode in anticipating state changes in reset
bitset<B_MODE_MAX> bmode(BranchMode);
//reset Feedback pararmeter
this->reset();
MyEstimator->reset();
//update the simulation parameters
WalkerController->put(myNode);
//assign current Eref and a large number for variance
WalkerController->setEnergyAndVariance(vParam[B_EREF],vParam[B_SIGMA]);
//determine the branch cutoff to limit wild weights based on the sigma and sigmaBound
RealType sigma=std::max(std::sqrt(static_cast<RealType>(iParam[B_TARGETWALKERS]))*vParam[B_SIGMA]*WalkerController->targetSigma,
static_cast<RealType>(100.0));
vParam[B_BRANCHCUTOFF]=std::min(sigma,5.0/tau);
//vParam[B_BRANCHCUTOFF]=vParam[B_SIGMA]*WalkerController->targetSigma;
vParam[B_BRANCHMAX]=vParam[B_BRANCHCUTOFF]*1.5;
vParam[B_BRANCHFILTER]=1.0/(vParam[B_BRANCHMAX]-vParam[B_BRANCHCUTOFF]);
//reset controller
WalkerController->reset();
if(BackupWalkerController) BackupWalkerController->reset();
BranchMode=bmode;
app_log() << " QMC counter = " << iParam[B_COUNTER] << endl;
app_log() << " time step = " << vParam[B_TAU] << endl;
app_log() << " effective time step = " << vParam[B_TAUEFF] << endl;
app_log() << " trial energy = " << vParam[B_ETRIAL] << endl;
app_log() << " reference energy = " << vParam[B_EREF] << endl;
app_log() << " Feedback = " << Feedback << endl;
app_log() << " reference variance = " << vParam[B_SIGMA] << endl;
app_log() << " target walkers = " << iParam[B_TARGETWALKERS] << endl;
app_log() << " branch cutoff = " << vParam[B_BRANCHCUTOFF] << " " << vParam[B_BRANCHMAX] << endl;
app_log() << " Max and mimum walkers per node= " << iParam[B_MAXWALKERS] << " " << iParam[B_MINWALKERS] << endl;
app_log() << " QMC Status (BranchMode) = " << BranchMode << endl;
}
//void SimpleFixedNodeBranch::initWalkerController(MCWalkerConfiguration& walkers, RealType tau, bool fixW, bool killwalker)
void SimpleFixedNodeBranch::initWalkerController(MCWalkerConfiguration& walkers, bool fixW, bool killwalker)
{
BranchMode.set(B_DMC,1);//set DMC
BranchMode.set(B_DMCSTAGE,iParam[B_WARMUPSTEPS]==0);//use warmup
//this is not necessary
//check if tau is different and set the initial values
//vParam[B_TAU]=tau;
bool fromscratch=false;
RealType tau=vParam[B_TAU];
//this is the first time DMC is used
if(WalkerController == 0)
{
if(iParam[B_TARGETWALKERS]==0)
{
Communicate* acomm=MyEstimator->getCommunicator();
int ncontexts=acomm->size();
vector<int> nw(ncontexts,0),nwoff(ncontexts+1,0);
nw[acomm->rank()]=walkers.getActiveWalkers();
acomm->allreduce(nw);
for(int ip=0; ip<ncontexts; ++ip) nwoff[ip+1]=nwoff[ip]+nw[ip];
walkers.setGlobalNumWalkers(nwoff[ncontexts]);
walkers.setWalkerOffsets(nwoff);
iParam[B_TARGETWALKERS]=nwoff[ncontexts];
}
WalkerController = createWalkerController(iParam[B_TARGETWALKERS], MyEstimator->getCommunicator(), myNode);
if(!BranchMode[B_RESTART])
{
fromscratch=true;
app_log() << " START ALL OVER " << endl;
vParam[B_TAUEFF]=tau;
BranchMode.set(B_POPCONTROL,!fixW);//fixW -> 0
BranchMode.set(B_KILLNODES,killwalker);
iParam[B_MAXWALKERS]=WalkerController->Nmax;
iParam[B_MINWALKERS]=WalkerController->Nmin;
if(!fixW && sParam[MIXDMCOPT]=="yes")
{
app_log() << "Warmup DMC is done with a fixed population " << iParam[B_TARGETWALKERS] << endl;
BackupWalkerController=WalkerController; //save the main controller
WalkerController=createWalkerController(iParam[B_TARGETWALKERS],MyEstimator->getCommunicator(), myNode,true);
BranchMode.set(B_POPCONTROL,0);
}
//PopHist.clear();
//PopHist.reserve(std::max(iParam[B_ENERGYUPDATEINTERVAL],5));
}
WalkerController->setWalkerID(walkers);
}
//else
//{
// BranchMode.set(B_DMCSTAGE,0);//always reset warmup
//}
MyEstimator->reset();
//update the simulation parameters
WalkerController->put(myNode);
//assign current Eref and a large number for variance
WalkerController->setEnergyAndVariance(vParam[B_EREF],vParam[B_SIGMA]);
this->reset();
if(fromscratch)
{
//determine the branch cutoff to limit wild weights based on the sigma and sigmaBound
//RealType sigma=std::max(std::sqrt(static_cast<RealType>(iParam[B_TARGETWALKERS]))*vParam[B_SIGMA]*WalkerController->targetSigma,100.0);
RealType sigma=std::max(std::sqrt(vParam[B_SIGMA])*WalkerController->targetSigma,50.0);
vParam[B_BRANCHCUTOFF]=std::min(sigma,2.5/tau);
//vParam[B_BRANCHCUTOFF]=vParam[B_SIGMA]*WalkerController->targetSigma;
vParam[B_BRANCHMAX]=vParam[B_BRANCHCUTOFF]*1.5;
vParam[B_BRANCHFILTER]=1.0/(vParam[B_BRANCHMAX]-vParam[B_BRANCHCUTOFF]);
vParam[B_TAUEFF]=tau*R2Accepted.result()/R2Proposed.result();
}
//reset controller
WalkerController->reset();
if(BackupWalkerController) BackupWalkerController->reset();
app_log() << " QMC counter = " << iParam[B_COUNTER] << endl;
app_log() << " time step = " << vParam[B_TAU] << endl;
app_log() << " effective time step = " << vParam[B_TAUEFF] << endl;
app_log() << " trial energy = " << vParam[B_ETRIAL] << endl;
app_log() << " reference energy = " << vParam[B_EREF] << endl;
app_log() << " Feedback = " << vParam[B_FEEDBACK] << endl;
app_log() << " reference variance = " << vParam[B_SIGMA] << endl;
app_log() << " target walkers = " << iParam[B_TARGETWALKERS] << endl;
app_log() << " branch cutoff = " << vParam[B_BRANCHCUTOFF] << " " << vParam[B_BRANCHMAX] << endl;
app_log() << " Max and mimum walkers per node= " << iParam[B_MAXWALKERS] << " " << iParam[B_MINWALKERS] << endl;
app_log() << " QMC Status (BranchMode) = " << BranchMode << endl;
}
