ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, the RooFit package allows the user to perform complex data modeling and fitting while the RooStats library provides abstractions and implementations for advanced statistical tools. Multivariate classification methods based on machine learning techniques are available via the TMVA package. A central piece in these analysis tools are the histogram classes which provide binning of one-and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using onthe-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks -e.g. data mining in HEP -by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way. Antcheva et al. / Computer Physics Communications 180 (2009) [2499][2500][2501][2502][2503][2504][2505][2506][2507][2508][2509][2510][2511][2512] Program summary

The GridPP Collaboration is building a UK computing Grid for particle physics, as part of the international effort towards computing for the Large Hadron Collider. The project, funded by the UK Particle Physics and Astronomy Research Council (PPARC), began in September 2001 and completed its first phase 3 years later. GridPP is a collaboration of approximately 100 researchers in 19 UK university particle physics groups, the Council for the Central Laboratory of the Research Councils and CERN, reflecting the strategic importance of the project. In collaboration with other European and US efforts, the first phase of the project demonstrated the feasibility of developing, deploying and operating a Grid-based computing system to meet the UK needs of the Large Hadron Collider experiments. This note describes the work undertaken to achieve this goal. S Supplementary documentation is available from stacks.iop.org/JPhysG/32/N1. References to sections S1, S2.1, etc are to sections within this online supplement.

Advanced mathematical and statistical computational methods are required by the LHC experiments for analyzing their data. Some of these methods are provided by the Math work package of the ROOT project, a C++ Object Oriented framework for large scale data handling applications. We present in detail the recent developments of this work package, in particular the recent improvements in the fitting and minimization classes, which have been redesigned and reimplemented with an object-oriented approach. New minimization algorithms have been added recently in ROOT and they can be used consistently for fitting via a common interface. These algorithms include Minuit2, the new object-oriented version of Minuit, various minimization methods from the GNU Scientific libraries, stochastic and genetic algorithms. Furthermore, a new graphical user interface has been also developed for performing and monitoring fits on ROOT data objects such as histograms, graphs and threes in both one or multi-dimensions. We will describe in detail the new capabilities provided by the new fitting and minimization classes and the functionality of the new user interface. * Speaker.

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