OO Implementation for the LHCb Rich Niko Neufeld Dietrich Liko.

OO Implementationfor the LHCb Rich

Niko NeufeldDietrich Liko

LHCb Software WeekWednesday, May 3, 2023

Introduction Study of OO Implementation of a

Reconstruction program Based on Standalone Program

by Roger Forty et al.

Present a comparison Review Object Oriented features


Objective Results of the FORTRAN

Physics Resources

To be better then FORTRAN Object Orientation

Modularity Interfaces


UML Process Specification using UML

Use cases

Development using UML case tool Rational Rose

Iterative Development Several internal iterations

UMLUnified Modeling

Language

by

Booch, Jacobson &

Rumbaugh


Program Specification Technical Proposal LHCb Note FORTRAN Program

Summary with all information Partial capture in use cases


One page on physics Cherenkov Effect Emission of Photons

Aerogel & Gas Radiator Reflection of Photons Observation of Photons

Quantum Efficiency Detector Geometry


One page on algorithm Local Likelihood Global Likelihood

Very effective CPU intensive

Other Algorithm possible Average emission angle


Framework OO Framework to implement

reconstruction algorithms Simulation also possible

Here the Global Likelihood will be implemented

Benchmark for usability


Use Cases Question a Physicist might ask ...

to a particle ... to a pixel ...

Global Likelihood

ChanhLN

jjij

M

ii

N

jjj

111

lnln


Use cases

MomentumTrue Particle Code

Physicist Emitted Number of Photons

Expected Number Photons

Geometrical Efficiency


Detector


Detector

Rich

Radiator

Reflector

Detector

SimplifiedUML

Class Diagram

Static relations of Classes


Event Event

TrackPixel

TrackExtrapolation

TrackSegment

Photon

I should be called DetectorElement !


Other Entities

PhotonSpectrum

PixelID

GeneratedPhoton


Lifetime Present for all Events

Rich, Radiator, Reflector, Detector

Present for one Event Tracks, TrackExtrapolations,

Pixel, Photons

Temporary Photon Spectrum, PixelID, Single Photon


Pixelid

tube

RecPixelsignal

globalPositionlocalPosition

size

PhotonDetector

But I am smart!

• Example trivial

• expensive calculations

• context questions

The PhotonDetector does all the

work for me !

I am not so smart ...


Architecture

Interface

DetectorEvent

StrategyAlgorithm


Standalone Program Minimal Environment Contains its own Transient Event

Model Parameter Files Histograms from CLHEP

Only for this test!


Optimisations Since last presentation

two weeks ago Program about a factor 2 slower

Profiling and Debugging Allocation of STL container operator[] Algorithmic improvements


Technical Proposal

Rec True Pe K p X

e 6233 7 328 0.95 8 224 554 31 0.27 5 10 13114 1 8 0.99K 1 39 1083 11 0.96p 1 4 1 427 1 0.98X 3 8 197 27 3990 0.94 0.99 0.90 0.92 0.97 1.00 0.99

500 Events

B

background

“Clean”


Results

Rec True Pe K p X

e 8848 7 426 3 51 0.95 20 230 1163 3 57 0.16 8 13 10891 9 29 0.99K 2 1 39 1083 11 0.97p 1 4 1 427 1 0.98X 154 2 67 12 9700 0.98 0.98 0.91 0.87 0.97 1.00 0.99

Difference in particle population, in particular for X particles:

Different sample, small differences in the modeling of the inner edges

Migration to Reduced Efficiency

Reduced Purity

500 Events

B

background

“Clean”


CPU Comparison

500 MhzPentium III

G77 7.52

G++ 8.32

Sec/Event

7 8 9

100 Events

B

Background

“Clean”


Kuck & Associates, Inc. Commercial C++ compiler

Standard compliant Templates Patented optimization techniques Precompiled headers http://www.kai.com

Time-locked trial version for RH6.1


CPU Comparison

500 MhzPentium III

G77 7.52

G++ 8.32

Sec/Event

7 8 9

KCC 7.32

100 Events

B

Background

“Clean”


Summary Outlined the development process Show physics results Show CPU comparisons

Why an OO program should be better ?


Track Segment Length

length

Aerogel Radiator

Track


FORTRANREAL FUNCTION DIST(POS,DIR)

C A line is given by POS and DISREAL POS(3), DIR(3)

C Radiator wall is described by its z positionREAL ZPOS(2)COMMON /RADIATOR/ ZPOS

DIST = ACOS(DIR(3),VMOD(DIR,3))*(ZPOS(2)-ZPOS(1))

END


FORTRAN Does what it should Math is simple Probably more complicated in praxis

walls not normal to z more then one radiator

Some variables which are interpreted in the context

But your program works soon!


Sometimes later ... … you want to improve the program

More realistic tracks More realistic radiators

But assumptions are not isolated There will be other places which depend on

these variables

You have to find all uses of the variables In your program at n places In other people programs at

unknown places


Object Based Assume two classes present

Plane Ray (can intersect with plane)

My program has ... class Algorithm dist method


Object Basedclass Algorithm {

Plane Radiator[2];

virtual double dist(const Ray & track) const;

}

double Algorithm::dist(const Ray & track) const {

return Radiator[1].intersect(track) - Radiator[0].intersect(track);

}


Object Based More compact Probably more general Math is done by somebody else

But main critic remains

If you want to improve the program,you have to find ...

n places in your own program unknown places in other programs


Object Orientedclass Track {

public:virtual double dist() const;virtual double intersect(const Plane & plane) const;virtual double intersect(……) const;

private:Radiator * radiator_;

}

class Radiator {public:

virtual double dist(const Track & track) const;}


Sequence DiagramRadiatorTrack

dist

dist

intersect

intersect

return dist

return dist

SimplifiedUML Sequence

Diagram

dynamic relation of classes


Object Oriented If one changes the Radiator ...

One place to do the modifications

If one changes the Track ... Another single place to do the change

Implementation is hidden behind the interface

No dependency on the implementation details

Visitor Pattern


Summarize FORTRAN

does the job difficult to maintain

Object Based C++ does the job probably better still difficult to maintain

Object Oriented C++ dependencies are reduced


Our Program does not depend on ... Track implementation Pixel implementation General Detector Geometry Photon radiation process Mirror choice Type of Photon Detector Photon Detector Assembly Details Reconstruction Strategy …..


Integration to GAUDI Algorithm is interfaced Package is nearly ready Release next week

Detailed documentation from the Rose Model available

We plan to include some “hand written” documentation for the release


Future in GAUDI Next steps …

Detector Description Other Algorithms Photon Detector Implementation

Not addressed

Structure of a general LHCb reconstruction program


Final Summary UML process for software development Standalone program has similar

performance as the TP Pleasant surprise: you can do a lot OO

for reconstruction applications There is the promise for a program that

will be easier to maintain You can try it yourself in GAUDI

OO Implementation for the LHCb Rich Niko Neufeld Dietrich Liko.

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