## From \$ROOTSYS/tutorials/roofit/rf603_multicpu.C

```//////////////////////////////////////////////////////////////////////////
//
// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #603
//
// Setting up a multi-core parallelized unbinned maximum likelihood fit
//
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////

#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooPolynomial.h"
#include "RooProdPdf.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit ;

void rf603_multicpu()
{

// C r e a t e   3 D   p d f   a n d   d a t a
// -------------------------------------------

// Create observables
RooRealVar x("x","x",-5,5) ;
RooRealVar y("y","y",-5,5) ;
RooRealVar z("z","z",-5,5) ;

// Create signal pdf gauss(x)*gauss(y)*gauss(z)
RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;

// Create background pdf poly(x)*poly(y)*poly(z)
RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
RooPolynomial pz("pz","pz",z) ;
RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;

// Create composite pdf sig+bkg
RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;

// Generate large dataset
RooDataSet* data = model.generate(RooArgSet(x,y,z),200000) ;

// P a r a l l e l   f i t t i n g
// -------------------------------

// In parallel mode the likelihood calculation is split in N pieces,
// that are calculated in parallel and added a posteriori before passing
// it back to MINUIT.

// Use four processes and time results both in wall time and CPU time
model.fitTo(*data,NumCPU(4),Timer(kTRUE)) ;

// P a r a l l e l   M C   p r o j e c t i o n s
// ----------------------------------------------

// Construct signal, total likelihood projection on (y,z) observables and likelihood ratio
RooAbsPdf* sigyz = sig.createProjection(x) ;
RooAbsPdf* totyz = model.createProjection(x) ;
RooFormulaVar llratio_func("llratio","log10(@0)-log10(@1)",RooArgList(*sigyz,*totyz)) ;

// Calculate likelihood ratio for each event, define subset of events with high signal likelihood
RooDataSet* dataSel = (RooDataSet*) data->reduce(Cut("llratio>0.7")) ;

// Make plot frame and plot data
RooPlot* frame = x.frame(Title("Projection on X with LLratio(y,z)>0.7"),Bins(40)) ;
dataSel->plotOn(frame) ;

// Perform parallel projection using MC integration of pdf using given input dataSet.
// In this mode the data-weighted average of the pdf is calculated by splitting the
// input dataset in N equal pieces and calculating in parallel the weighted average
// one each subset. The N results of those calculations are then weighted into the
// final result

// Use four processes
model.plotOn(frame,ProjWData(*dataSel),NumCPU(4)) ;

new TCanvas("rf603_multicpu","rf603_multicpu",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;

}
```
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