CSC2007 Physics Computing Theme

Coordinators:

Rudi Frühwirth, HEPHY Vienna
Pere Mato, CERN

The track will introduce the fundamental concepts of Physics Computing and will then address the ROOT Technologies, on on-line Data Acquisition.

The first series of lectures gives an overview of the software and hardware components required for the processing of the experimental data, from the source - the detector - to the physics analysis. The emphasis is on the concepts, but some implementation details are discussed as well. The key concept is data reduction, both in terms of rate and in terms of information density. The various algorithms used for data reduction, both online and offline, are described. The flow of the real data is the main topic, but the need for and the production of simulated data is discussed as well.

The second series of lectures introduces the data analysis framework ROOT, covering all basic parts that are needed for a future LHC data analysis. The lectures will present by example how key requirements like performance, reliability, flexibility, platform independence, ease-of-use, and support for extensions are put into practice. Combined with the accompanying tutorials they will give an overview of the software techniques ROOT brings to life and hands-on experience of using ROOT.

The third lecture series focuses on on-line Data Acquisition Techniques.

Glossary of the different acronyms: http://www.gridpp.ac.uk/gas/

Overview

Series	Type	Lecture	Description	Lecturer

General Introduction to Physics Computing	Lectures	Series	General Introduction to Physics Computing The two lectures give an overview of the software and hardware components required for the processing of the experimental data, from the source - the detector - to the physics analysis. The emphasis is on the concepts, but some implementation details are discussed as well. The key concept is data reduction, both in terms of rate and in terms of information density. The various algorithms used for data reduction, both online and offline, are described. The flow of the real data is the main topic, but the need for and the production of simulated data is discussed as well.	Rudi Frühwirth
		Lecture 1	Event filtering The first lecture deals with the multi-level event filters (triggers) that are used to select the physically interesting events and to bring down the event rate to an acceptable figure. Some examples of the hardware and software that is deployed by the LHC experiments are presented.
		Lecture 2	Reconstruction and simulation The second lecture describes the various stages of event reconstruction, including calibration and alignment. The emphasis is on algorithms and data structures. The need for large amounts of simulated data is explained. The lecture concludes with a brief resume of the principles of physics analysis and the tools that are currently employed.

ROOT Technologies	Lectures	Lecture 1	Basics To lay the foundation for the lectures of the coming days, we start by introducing the purpose of ROOT and its primary contexts of use. This will cover e.g. the C++ interpreter CINT and the just-in-time compiler ACLiC.	Axel Naumann Bertrand Bellenot
		Lecture 2	Persistency As ROOT is non-democratic, we will have to answer the question of who owns whom (object ownership). The exabytes of LHC data will be saved using ROOT's i/o. We will explain how ROOT persistency is integrated into C++ and the basics of ROOT's storage structure. As things change, modified classes must be taken into account for persistency. With ROOT, this mechanism is called schema evolution.
		Lecture 3	Tree For huge amounts of data and within the special context of high energy physics (event-based data, write-once, read-many), TTrees combined with TClonesArrays provide ideal collections for data storage. We will explain why in this context they are superior to e.g. STL collections, and which efficiency optimizations they provide for processing data (splitting, data access without library). Two mechanisms for combining TTrees, friends and chains, will be introduced.
		Lecture 4	Analysis Typical ingredients of analyzes are data selection and histogramming of values. ROOT provides facilities and tools for that; we will present TTree::Draw(), TSelector, and the highly efficient, interactive, parallel analysis facility PROOF.
	Exercises	Exercise 1	We will play with a few example macros, to get a feeling for the pros and cons of compiled versus interpreted mode. We will try to come up with code that is beyond CINT's abilities.
		Exercise 2	There is no LHC data yet, so we will create our own. We will modify the class layout used to store data to see schema evolution in action. We will go through the steps of building a library including a dictionary.
		Exercise 3	We will store millions of your own objects into a TTree, and compare its performance with STL.
		Exercise 4	Some data in the exercises' TTree is not under your control. You will determine its underlying probability distribution. We will close with a wrap-up.
	Pre-requisite Knowledge	Mandatory pre-requisite	Install ROOT if you don't have it; start it up. Create a one-dimensional histogram with 10 bins spanning the range 0..5. Fill it with the values 4., 4.2, 5.8, 3.8, 4.7, and 2.7. Draw it. Fit it with a Gaussian using the default options. Check that the mean of the fit should is 4.0 - otherwise you've done something wrong.
	Pre-requisite Knowledge	Desirable pre-requisite and references to further information	If you need to install ROOT, use the recommended version mentioned at: http://root.cern.ch/root/Availability.html To learn how to create, fill, draw, and fit histograms look at the User's Guide at: http://root.cern.ch/root/doc/RootDoc.html chapters "Histograms" and "Fitting Histograms". To see examples on how to create, fill, draw, and fit histograms look at the macros in $ROOTSYS/tutorials, esp. hsimple.C and fit1.C. The reference guide for ROOT's histogram base class TH1 is located at : http://root.cern.ch/root/html/TH1.html

On-line Data Acquisition	Lectures	Lecture 1	A general introduction to data acquisition systems will be given by focusing on the four LHC experiments. The principle data flow, the qualitative/quantitative requirements and the architecture of these data acquisition systems will be discussed. Their relations with the other on-line systems for triggering, high-level filtering, and control will be explained.	Klaus Schossmaier
		Lecture 2	The functional elements of data acquisition systems (e.g. readout, event building, control, interfaces) will be addressed in terms of components, concepts, and technologies. In addition, testing techniques, performance measurements as well as some practical aspects of running on-line systems will be covered.
		Lecture 3	A case study of the ALICE data acquisition system will be presented. The chosen technologies will be discussed and the software framework (called DATE) including the add-on software for performance monitoring and data quality monitoring will be introduced. Also some simulation results will be shown.
	Exercises	Exercise 1	A demonstration of the ALICE data acquisition system framework DATE will be given.
	Prerequisite Knowledge	Desirable prerequisite and references to further information	A good knowledge of programming languages, Linux operating system, and computing technologies is considered as useful to benefit most from this series of lectures. References: CERN Summer Student Lecture Programme - 2005 Trigger and Data Acquisition Systems, by Paris Sphicas Part 1 Trigger and Data Acquisition Systems, by Paris Sphicas Part 2 CERN Summer Student Lecture Programme - 2002 Trigger and Data Acquisition Systems, by Clara Gaspar Part 1 Trigger and Data Acquisition Systems, by Clara Gaspar Part 2 Trigger and Data Acquisition Systems, by Clara Gaspar Part3 Additional material will be available in the CSC handbook provided at the school.