Int_t mp102_readNtuplesFillHistosAndFit()
{
// No nuisance for batch execution
gROOT->SetBatch();
// Perform the operation sequentially ---------------------------------------
TChain inputChain("multiCore");
inputChain.Add("mp101_multiCore_*.root");
TH1F outHisto("outHisto", "Random Numbers", 128, -4, 4);
{
TimerRAII t("Sequential read and fit");
inputChain.Draw("r >> outHisto");
outHisto.Fit("gaus");
}
// We now go MP! ------------------------------------------------------------
// TProcPool offers an interface to directly process trees and chains without
// the need for the user to go through the low level implementation of a
// map-reduce.
// We adapt our parallelisation to the number of input files
const auto nFiles = inputChain.GetListOfFiles()->GetEntries();
// This is the function invoked during the processing of the trees.
auto workItem = [](TTreeReader & reader) {
TTreeReaderValue<Float_t> randomRV(reader, "r");
auto partialHisto = new TH1F("outHistoMP", "Random Numbers", 128, -4, 4);
while (reader.Next()) {
partialHisto->Fill(*randomRV);
}
return partialHisto;
};
// Create the pool of processes
TProcPool workers(nFiles);
// Process the TChain
{
TimerRAII t("Parallel execution");
TH1F *sumHistogram = workers.ProcTree(inputChain, workItem, "multiCore");
sumHistogram->Fit("gaus", 0);
}
return 0;
}
Int_t mt102_readNtuplesFillHistosAndFit()
{
// No nuisance for batch execution
gROOT->SetBatch();
// Perform the operation sequentially ---------------------------------------
TChain inputChain("multiCore");
inputChain.Add("mc101_multiCore_*.root");
TH1F outHisto("outHisto", "Random Numbers", 128, -4, 4);
{
TimerRAII t("Sequential read and fit");
inputChain.Draw("r >> outHisto");
outHisto.Fit("gaus");
}
// We now go MT! ------------------------------------------------------------
// The first, fundamental operation to be performed in order to make ROOT
// thread-aware.
ROOT::EnableMT();
// We adapt our parallelisation to the number of input files
const auto nFiles = inputChain.GetListOfFiles()->GetEntries();
std::forward_list<UInt_t> workerIDs(nFiles);
std::iota(std::begin(workerIDs), std::end(workerIDs), 0);
// We define the histograms we'll fill
std::vector<TH1F> histograms;
histograms.reserve(nFiles);
for (auto workerID : workerIDs){
histograms.emplace_back(TH1F(Form("outHisto_%u", workerID), "Random Numbers", 128, -4, 4));
}
// We define our work item
auto workItem = [&histograms](UInt_t workerID) {
TFile f(Form("mc101_multiCore_%u.root", workerID));
TNtuple *ntuple = nullptr;
f.GetObject("multiCore", ntuple);
auto &histo = histograms.at(workerID);
for (UInt_t index = 0; index < ntuple->GetEntriesFast(); ++index) {
ntuple->GetEntry(index);
histo.Fill(ntuple->GetArgs()[0]);
}
};
TH1F sumHistogram("SumHisto", "Random Numbers", 128, -4, 4);
// Create the collection which will hold the threads, our "pool"
std::vector<std::thread> workers;
// We measure time here as well
{
TimerRAII t("Parallel execution");
// Spawn workers
// Fill the "pool" with workers
for (auto workerID : workerIDs) {
workers.emplace_back(workItem, workerID);
}
// Now join them
for (auto&& worker : workers) worker.join();
// And reduce
std::for_each(std::begin(histograms), std::end(histograms),
[&sumHistogram](const TH1F & h) {
sumHistogram.Add(&h);
});
sumHistogram.Fit("gaus",0);
}
return 0;
}