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The Parallel ROOT Facility - PROOF is a distributed analysis system which allows to exploit inherent event level parallelism of high energy physics data. PROOF can be configured to work with centralized storage systems, but it is especially effective together with distributed local storage systems - like Xrootd, when data are distributed over computing nodes. It works efficiently on different types of hardware and scales well from a multi-core laptop to large computing farms. From that point of view it is well suited for both large central analysis facilities and Tier 3 type analysis farms. PROOF can be used in interactive or batch like regimes. The interactive regime allows user to work with typically distributed data from ROOT command prompt and get a real time feedback on analysis progress and intermediate results. We will discuss our experience with PROOF in the context of ATLAS Collaboration distributed analysis. In particular we will discuss PROOF performance in various analysis scenarios and in multi-user, multi-session environment. We will also describe PROOF integration with ATLAS distributed data management system and prospects of running PROOF on geographically distributed analysis farms.

An impressive amount of effort has been put in to realize a set of frameworks to support analysis in this new paradigm of GRID computing. However, much more than half of a physicist's time is typically spent after the GRID processing of the data. Due to the private nature of this level of analysis, there has been little common framework or methodology. While most physicists agree to use ROOT as the basis of their analysis, a number of approaches are possible for the implementation of the analysis using ROOT: conventional methods using CINT/ACLiC, development using g++, alternative interface through python, and parallel processing methods such as PROOF are some of the choices currently available on the market. Furthermore, in the ATLAS collaboration an additional layer of technology adds to the complexity because the data format is based on the POOL technology, which tends to be less portable. In this study, various modes of ROOT analysis are profiled for comparison with the main focus on the processing speed. Input data is or derived from the ATLAS Full-Dress-Rehearsal, which was meant to stress test the whole computing system of ATLAS.

The EventView Analysis Framework is currently the basis for much of the analysis software employed by various ATLAS physics groups (for example the Top, SUSY, Higgs, and Exotics working groups). In ATLAS's central data preparation, this framework provides an assessment of data quality and the first analysis of physics data for the whole collaboration. An EventView is a self-consistent interpretation of a physics event or equivalently the state of a specific analysis. Analyses are constructed at runtime by chaining and configuring modular components consisting of tools, C++ implementation of specific analysis algorithms, and modules, python grouping and configuration of various tool. A large common library of general tools and modules serve as the building blocks of nearly all of the steps of any analysis. The output is multiple simultaneous EventViews of every event, typically reflecting different choices of selections, reconstruction algorithms, combinatoric assignments, or input data (eg full or fast reconstruction or truth).

ATLAS, one of the four experiments at the CERN LHC accelerator, uses the Athena/Gaudi component architecture to build and run its software applications. Python was initially introduced as a configuration language for the hundreds of C++ components, but soon, thanks to the development of a powerful C++ binding layer, it started pervading all application domains from job management, to application control to interactive analysis. In this talk we will survey both successes and challenges brought by the introduction of Python and we will discuss our ongoing efforts to use the flexibility of Python to make easier and safer for ATLAS physicists to build, configure and run their applications.