Table of contents

FairRoot is the simulation and analysis framework used by CBM and PANDA experiments at FAIR/GSI. The use of graphics processor units (GPUs) for event reconstruction in FairRoot will be presented. The fact that CUDA (Nvidia's Compute Unified Device Architecture) development tools work alongside the conventional C/C++ compiler, makes it possible to mix GPU code with general-purpose code for the host CPU, based on this some of the reconstruction tasks can be send to the graphic cards. Moreover, tasks that run on the GPU's can also run in emulation mode on the host CPU, which has the advantage that the same code is used on both CPU and GPU.

Computers are no longer getting faster: instead, they are growing more and more CPUs, each of which is no faster than the previous generation. This increase in the number of cores evidently calls for more parallelism in HENP software. If end-users' stand-alone analysis applications are relatively easy to modify, LHC experiments frameworks, being mostly written with a single 'thread' of execution in mind and consequent code bases, are on the other hand more challenging to parallelize. Widespread and inconsiderate changes so close to data taking are out of the equation: we need clear strategies and guidelines to reap the benefits out of the multicore/manycore era while minimizing the code changes.

CernVM is a Virtual Software Appliance capable of running physics applications from the LHC experiments at CERN. It aims to provide a complete and portable environment for developing and running LHC data analysis on any end-user computer (laptop, desktop) as well as on the Grid, independently of Operating System platforms (Linux, Windows, MacOS). The experiment application software and its specific dependencies are built independently from CernVM and delivered to the appliance just in time by means of a CernVM File System (CVMFS) specifically designed for efficient software distribution. The procedures for building, installing and validating software releases remains under the control and responsibility of each user community. We provide a mechanism to publish pre-built and configured experiment software releases to a central distribution point from where it finds its way to the running CernVM instances via the hierarchy of proxy servers or content delivery networks. In this paper, we present current state of CernVM project and compare performance of CVMFS to performance of traditional network file system like AFS and discuss possible scenarios that could further improve its performance and scalability.

Like many HEP researchers, the members of the Fermi collaboration have chosen to store their experiment data within ROOT trees. A frequent activity of such physicists is the tuning of selection criteria which define the events of interest, thus cutting and pruning the ROOT trees so to extract all the data linked to those specific physical events. It is rather straightforward to write a ROOT script to skim a single kind of data, for example the raw measurements of Fermi LAT detector. This proves to be trickier if one wants to process also some simulated or analysis data at the same time, because each kind of data is structured with its own rules for what concerns file names and sizes, tree names, identification of events, etc. TSkim has been designed to facilitate this task. Thanks to a user-defined configuration file which says where to find the run and event identifications in the different kind of trees, TSkim is able to collect all the tree elements which match a given ROOT cut. The tool will also help when loading the shared libraries which describe the experiment data, or when pruning the tree branches. Initially a pair of PERL and ROOT scripts, TSkim is today a fully compiled C++ application, enclosing our ROOT know-how and offering a panel of features going far beyond the original Fermi requirements. In this manuscript, we present TSkim concepts and key features, including a new kind of event list. Any collaboration using ROOT IO could profit from the use of this tool.

Virtualization is a proven software technology that is rapidly transforming the IT landscape and fundamentally changing the way that people compute. Recently all major software producers (e.g. Microsoft and RedHat) developed or acquired virtualization technologies. Our institute is a Tier1 for LHC experiments and is experiencing lots of benefits from virtualization technologies, like improving fault tolerance, providing efficient hardware resource usage and increasing security. Currently the virtualization solution adopted is xen, which is well supported by the Scientific Linux distribution, widely adopted by the HEP community. Since the HEP linux distribution is based on RedHat ES, we feel the need to investigate performance and usability differences with the new kvm technology recently acquired by RedHat. The case study of this work will be the LHCb experiment Tier2 site hosted at our institute, where all major grid elements run on xen virtual machines smoothly. We will investigate the impact on performance and stability that a migration to kvm would entail on the Tier2 site, as well as the effort required by a system administrator to deploy the migration. Several quantitative test results will be shown and explained in detail.

After ten years from its first version, the Gaudi software framework underwent many changes and improvements with a subsequent increase of the code base. Those changes were almost always introduced preserving the backward compatibility and reducing as much as possible changes in the framework itself; obsolete code has been removed only rarely. After a release of Gaudi targeted to the data taking of 2008, it has been decided to have a review of the code of the framework with the aim of a general consolidation in view of the data taking of 2009. We also decided to take the occasion to introduce those improvements never implemented because of the big impact they have on the rest of the code, and those changes of the framework needed to solve some intrinsic problems of the implementation, but never made because they were considered too disruptive. With this contribution we want to describe which are the problems we addressed and the improvements we made to the framework during this review.

ATLAS has developed and deployed event-level selection services based upon event metadata records ("TAGS") and supporting file and database technology. These services allow physicists to extract events that satisfy their selection predicates from any stage of data processing and use them as input to later analyses. One component of these services is a web-based Event-Level Selection Service Interface (ELSSI). ELSSI supports event selection by integrating run-level metadata, luminosity-block-level metadata (e.g., detector status and quality information), and event-by-event information (e.g., triggers passed and physics content). The list of events that survive after some selection criterion is returned in a form that can be used directly as input to local or distributed analysis; indeed, it is possible to submit a skimming job directly from the ELSSI interface using grid proxy credential delegation. ELSSI allows physicists to explore ATLAS event metadata as a means to understand, qualitatively and quantitatively, the distributional characteristics of ATLAS data. In fact, the ELSSI service provides an easy interface to see the highest missing ET events or the events with the most leptons, to count how many events passed a given set of triggers, or to find events that failed a given trigger but nonetheless look relevant to an analysis based upon the results of offline reconstruction, and more. This work provides an overview of ATLAS event-level selection services, with an emphasis upon the interactive Event-Level Selection Service Interface.

The job configuration system of the CMS experiment is based on the Python programming language. Software modules and their order of execution are both represented by Python objects. In order to investigate and verify configuration parameters and dependencies naturally appearing in modular software, CMS employs a graphical tool. This tool visualizes the configuration objects, their dependencies, and the information flow. Furthermore it can be used for documentation purposes. The underlying software concepts as well as the visualization are presented.

Metadata (data about data) arise in many contexts, from many diverse sources, and at many levels in ATLAS. Familiar examples include run-level, luminosity-block-level, and event-level metadata, and, related to processing and organization, dataset-level and file-level metadata, but these categories are neither exhaustive nor orthogonal. Some metadata are known a priori, in advance of data taking or simulation; other metadata are known only after processing, and occasionally, quite late (e.g., detector status or quality updates that may appear after initial reconstruction is complete). Metadata that may seem relevant only internally to the distributed computing infrastructure under ordinary conditions may become relevant to physics analysis under error conditions ("What can I discover about data I failed to process?"). This talk provides an overview of metadata and metadata handling in ATLAS, and describes ongoing work to deliver integrated metadata services in support of physics analysis.

ROOT, as a scientific data analysis framework, provides extensive capabilities via Graphical User Interfaces (GUI) for performing interactive analysis and visualizing data objects like histograms and graphs. A new interface for fitting has been developed for performing, exploring and comparing fits on data point sets such as histograms, multi-dimensional graphs or trees. With this new interface, users can build interactively the fit model function, set parameter values and constraints and select fit and minimization methods with their options. Functionality for visualizing the fit results is as well provided, with the possibility of drawing residuals or confidence intervals. Furthermore, the new fit panel reacts as a standalone application and it does not prevent users from interacting with other windows. We will describe in great detail the functionality of this user interface, covering as well new capabilities provided by the new fitting and minimization tools introduced recently in the ROOT framework.

Calibrations and conditions databases can be accessed from within the JANA Event Processing framework through the API defined in its JCalibration base class. The API is designed to support everything from databases, to web services to flat files for the backend. A Web Service backend using the gSOAP toolkit has been implemented which is particularly interesting since it addresses many modern cybersecurity issues including support for SSL. The API allows constants to be retrieved through a single line of C++ code with most of the context, including the transport mechanism, being implied by the run currently being analyzed and the environment relieving developers from implementing such details.

We update our CHEP06 [2] presentation on the ATLAS experiment software infrastructure used to build, validate, distribute, and document the ATLAS offline software. The ATLAS collaboration's computational resources and software developers are distributed around the globe in about 35 counties. The ATLAS offline code base is currently over 7 million source lines of code in 10,000+ C++ classes organized into about 2,000 packages. More than 400 developers contribute code each month. Since our last report, we have developed a powerful, flexible system to request code versions to be included in software builds, made changes to our software building tools, increased the number of daily builds used to validate significant code changes, improved the tools for distributing the code to our computational sites around the world, and made many advancements in the tools to document the code.

The ATLAS detector consists of four major components: inner tracker, calorimeter, muon spectrometer and magnet system. In the Tile Calorimeter (TileCal), there are 4 partitions, each partition has 64 modules and each module has up to 48 channels. During the ATLAS pre-operation phase, a group of physicists need to analyze the Tile Calorimeter data quality, generate reports and update the official database, when necessary. The Tile Commissioning Web System (TCWS) retrieves information from different directories and databases, executes programs that generate results, stores comments and verifies the calorimeter status. TCWS integrates different applications, each one presenting a unique data view. The Web Interface for Shifters (WIS) supports monitoring tasks by managing test parameters and all the calorimeter status. The TileComm Analysis stores plots, automatic analyses results and comments concerning the tests. With the necessity of increasing granularity, a new application was created: the Monitoring and Calibration Web System (MCWS). This application supports data quality analyses at the channel level by presenting the automatic analyses results, the problematic known channels and the channels masked by the shifters. Through the web system, it is possible to generate plots and reports, related to the channels, identify new bad channels and update the Bad Channels List at the ATLAS official database (COOL DB). The Data Quality Monitoring Viewer (DQM Viewer) displays the data quality automatic results through an oriented visualization.

The LCG File Catalog is a key component of the LHC Computing Grid middleware [1], as it contains the mapping between Logical File Names and Physical File Names on the Grid. The Atlas computing model foresees multiple local LFC housed in each Tier-1 and Tier-0, containing all information about files stored in the regional cloud. As the local LFC contents are presently not replicated anywhere, this turns out in a dangerous single point of failure for all of the Atlas regional clouds. In order to solve this problem we propose a novel solution for high availability (HA) of Oracle based Grid services, obtained by composing an Oracle Data Guard deployment and a series of application level scripts. This approach has the advantage of being very easy to deploy and maintain, and represents a good candidate solution for all Tier-2s which are usually little centres with little manpower dedicated to service operations. We also present the results of a wide range of functionality and performance tests run on a test-bed having characteristics similar to the ones required for production. The test-bed consists of a failover deployment between the Italian LHC Tier-1 (INFN – CNAF) and an Atlas Tier-2 located at INFN – Roma1. Moreover, we explain how the proposed strategy can be deployed on the present Grid infrastructure, without requiring any change to the middleware and in a way that is totally transparent to end users and applications.

GAUDI is a software framework in C++ used to build event data processing applications using a set of standard components with well-defined interfaces. Simulation, high-level trigger, reconstruction, and analysis programs used by several experiments are developed using GAUDI. These applications can be configured and driven by simple Python scripts. Given the fact that a considerable amount of existing software has been developed using serial methodology, and has existed in some cases for many years, implementation of parallelisation techniques at the framework level may offer a way of exploiting current multi-core technologies to maximize performance and reduce latencies without re-writing thousands/millions of lines of code. In the solution we have developed, the parallelization techniques are introduced to the high level Python scripts which configure and drive the applications, such that the core C++ application code requires no modification, and that end users need make only minimal changes to their scripts. The developed solution leverages from existing generic Python modules that support parallel processing. Naturally, the parallel version of a given program should produce results consistent with its serial execution. The evaluation of several prototypes incorporating various parallelization techniques are presented and discussed.

The ANDA experiment at the future facility FAIR will provide valuable data for our present understanding of the strong interaction. In preparation for the experiments, large-scale simulations for design and feasibility studies are performed exploiting a new software framework, ANDAROOT, which is based on FairROOT and the Virtual Monte Carlo interface, and which runs on a large-scale computing GRID environment exploiting the AliEn² middleware. In this paper, an overview is given of the ANDA experiment with the emphasis on the various developments which are pursuit to provide a user and developer friendly computing environment for the ANDA collaboration.

Many secure communication libraries used by distributed systems, such as SSL, TLS, and Kerberos, fail to make a clear distinction between the authentication, session, and communication layers. In this paper we introduce CEDAR, the secure communication library used by the Condor High Throughput Computing software, and present the advantages to a distributed computing system resulting from CEDAR's separation of these layers. Regardless of the authentication method used, CEDAR establishes a secure session key, which has the flexibility to be used for multiple capabilities. We demonstrate how a layered approach to security sessions can avoid round-trips and latency inherent in network authentication. The creation of a distinct session management layer allows for optimizations to improve scalability by way of delegating sessions to other components in the system. This session delegation creates a chain of trust that reduces the overhead of establishing secure connections and enables centralized enforcement of system-wide security policies. Additionally, secure channels based upon UDP datagrams are often overlooked by existing libraries; we show how CEDAR's structure accommodates this as well. As an example of the utility of this work, we show how the use of delegated security sessions and other techniques inherent in CEDAR's architecture enables US CMS to meet their scalability requirements in deploying Condor over large-scale, wide-area grid systems.

The ATLAS experiment at the Large Hadron Collider reads out 100 Million electronic channels at a rate of 200 Hz. Before the data are shipped to storage and analysis centres across the world, they have to be checked to be free from irregularities which render them scientifically useless. Data quality offline monitoring provides prompt feedback from full first-pass event reconstruction at the Tier-0 computing centre and can unveil problems in the detector hardware and in the data processing chain. Detector information and reconstructed proton-proton collision event characteristics are distilled into a few key histograms and numbers which are automatically compared with a reference. The results of the comparisons are saved as status flags in a database and are published together with the histograms on a web server. They are inspected by a 24/7 shift crew who can notify on-call experts in case of problems and in extreme cases signal data taking abort.

A R&D project has been recently launched to investigate Geant4 architectural design in view of addressing new experimental issues in HEP and other related physics disciplines. In the context of this project the use of generic programming techniques besides the conventional object oriented is investigated. Software design features and preliminary results from a new prototype implementation of Geant4 electromagnetic physics are illustrated. Performance evaluations are presented. Issues related to quality assurance in Geant4 physics modelling are discussed.

The combination of three relatively recent technologies is described which allows an easy path from database retrieval to interactive web display. SQL queries on an Oracle database can be performed in a manner which directly return an XML description of the result, and Ajax techniques (Asynchronous JavaScript And XML) are used to dynamically inject the data into a web display accompanied by an XSLT transform template which determines how the data will be formatted. By tuning the transform to generate SVG (Scalable Vector Graphics) a direct graphical representation can be produced in the web page while retaining the database data as the XML source, allowing dynamic links to be generated in the web representation, but programmatic use of the data when used from a user application. With the release of the SVG 1.2 Tiny draft specification, the display can also be tailored for display on mobile devices. The technologies are described and a sample application demonstrated, showing conditions data from the ATLAS Semiconductor Tracker.

The COOL database in ATLAS is primarily used for storing detector conditions data, but also status flags which are uploaded summaries of information to indicate the detector reliability during a run. This paper introduces the use of CherryPy, a Python application server which acts as an intermediate layer between a web interface and the database, providing a simple means of storing to and retrieving from the COOL database which has found use in many web applications. The software layer is designed to be RESTful, implementing the common CRUD (Create, Read, Update, Delete) database methods by means of interpreting the HTTP method (POST, GET, PUT, DELETE) on the server along with a URL identifying the database resource to be operated on. The format of the data (text, xml etc) is also determined by the HTTP protocol. The details of this layer are described along with a popular application demonstrating its use, the ATLAS run list web page.

The LCG Applications Area at CERN provides basic software components for the LHC experiments such as ROOT, POOL, COOL which are developed in house and also a set of "external" software packages (70) which are needed in addition such as Python, Boost, Qt, CLHEP, etc. These packages target many different areas of HEP computing such as data persistency, math, simulation, grid computing, databases, graphics, etc. Other packages provide tools for documentation, debugging, scripting languages and compilers. All these packages are provided in a consistent manner on different compilers, architectures and operating systems. The Software Process and Infrastructure project (SPI) [1] is responsible for the continous testing, coordination, release and deployment of these software packages. The main driving force for the actions carried out by SPI are the needs of the LHC experiments, but also other HEP experiments could profit from the set of consistent libraries provided and receive a stable and well tested foundation to build their experiment software frameworks.

This presentation will first provide a brief description of the tools and services provided for the coordination, testing, release, deployment and presentation of LCG/AA software packages and then focus on a second set of tools provided for outside LHC experiments to deploy a stable set of HEP related software packages both as binary distribution or from source.

The ATLAS experiment's RunTimeTester (RTT) is a software testing framework into which software package developers can plug their tests, have them run automatically, and obtain feedback via email and the web. The RTT processes the ATLAS nightly build releases, using acron to launch runs on a dedicated cluster at CERN, and submitting user jobs to dedicated LSF batch queues. Running computationally longer tests, up to 24 hours long, it is thus complementary to the ATLAS ATN framework which feeds back rapidly on few event tests run directly on ATLAS build machines.

Scientific analysis and simulation requires the processing and generation of millions of data samples. These tasks are often comprised of multiple smaller tasks divided over multiple (computing) sites. This paper discusses the Compact Muon Solenoid (CMS) workflow infrastructure, and specifically the Python based workflow library which is used for so called task lifecycle management. The CMS workflow infrastructure consists of three layers: high level specification of the various tasks based on input/output data sets, life cycle management of task instances derived from the high level specification and execution management. The workflow library is the result of a convergence of three CMS sub projects that respectively deal with scientific analysis, simulation and real time data aggregation from the experiment. This will reduce duplication and hence development and maintenance costs.

During massive data reprocessing operations an ATLAS Conditions Database application must support concurrent access from numerous ATLAS data processing jobs running on the Grid. By simulating realistic work-flow, ATLAS database scalability tests provided feedback for Conditions Db software optimization and allowed precise determination of required distributed database resources. In distributed data processing one must take into account the chaotic nature of Grid computing characterized by peak loads, which can be much higher than average access rates. To validate database performance at peak loads, we tested database scalability at very high concurrent jobs rates. This has been achieved through coordinated database stress tests performed in series of ATLAS reprocessing exercises at the Tier-1 sites. The goal of database stress tests is to detect scalability limits of the hardware deployed at the Tier-1 sites, so that the server overload conditions can be safely avoided in a production environment. Our analysis of server performance under stress tests indicates that Conditions Db data access is limited by the disk I/O throughput. An unacceptable side-effect of the disk I/O saturation is a degradation of the WLCG 3D Services that update Conditions Db data at all ten ATLAS Tier-1 sites using the technology of Oracle Streams. To avoid such bottlenecks we prototyped and tested a novel approach for database peak load avoidance in Grid computing. Our approach is based upon the proven idea of pilot job submission on the Grid: instead of the actual query, an ATLAS utility library sends to the database server a pilot query first.

Being a highly dynamic language and allowing reliable programming with quick turnarounds, Python is a widely used programming language in CMS. Most of the tools used in workflow management and the GRID interface tools are written in this language. Also most of the tools used in the context of release management: integration builds, release building and deploying, as well as performance measurements are in Python. With an interface to the CMS data formats, rapid prototyping of analyses and debugging is an additional use case. Finally in 2008 the CMS experiment switched to using Python as its configuration language. This paper will give an overview of the general usage of Python in the CMS experiment and discuss which features of the language make it well-suited for the existing use cases.

Non-event data describing detector conditions change with time and come from different data sources. They are accessible by physicists within the offline event-processing applications for precise calibration of reconstructed data as well as for data-quality control purposes. Over the past years CMS has developed and deployed a software system managing such data. Object-relational mapping and the relational abstraction layer of the LHC persistency framework are the foundation; the offline condition framework updates and delivers C++ data objects according to their validity. A high-level tag versioning system allows production managers to organize data in hierarchical view. A scripting API in python, command-line tools and a web service serve physicists in daily work. A mini-framework is available for handling data coming from external sources. Efficient data distribution over the worldwide network is guaranteed by a system of hierarchical web caches. The system has been tested and used in all major productions, test-beams and cosmic runs.

ATLAS software has been developed mostly on CERN linux cluster lxplus or on similar facilities at the experiment Tier 1 centers. The fast rise of virtualization technology has the potential to change this model, turning every laptop or desktop into an ATLAS analysis platform. In the context of the CernVM project we are developing a suite of tools and CernVM plug-in extensions to promote the use of virtualization for ATLAS analysis and software development.

The Virtual Machine Logbook (VML), in particular, is an application to organize work of physicists on multiple projects, logging their progress, and speeding up "context switches" from one project to another. An important feature of VML is the ability to share with a single "click" the status of a given project with other colleagues. VML builds upon the save and restore capabilities of mainstream virtualization software like VMware, and provides a technology-independent client interface to them. A lot of emphasis in the design and implementation has gone into optimizing the save and restore process to makepractical to store many VML entries on a typical laptop disk or to share a VML entry over the network.

At the same time, taking advantage of CernVM's plugin capabilities, we are extending the CernVM platform to help increase the usability of ATLAS software. For example, we added the ability to start the ATLAS event display on any computer running CernVM simply by clicking a button in a web browser.

We want to integrate seamlessly VML with CernVM unique file system design to distribute efficiently ATLAS software on every physicist computer. The CernVM File System (CVMFS) download files on-demand via HTTP, and cache it locally for future use. This reduces by one order of magnitude the download sizes, making practical for a developer to work with multiple software releases on a virtual machine.

Hierarchy Software Development Framework provides a lightweight tool for building portable modular applications for performing automated data analysis tasks in a batch mode. The history of design and development activities devoted to the project has begun in March 2005 and from the very beginning it was targeting the case of building experimental data processing applications for the CMD-3 experiment which is being commissioned at Budker Institute of Nuclear Physics (BINP, Novosibirsk, Russia). Its design addresses the generic case of modular data processing application operating within the well defined distributed computing environment. The main features of the framework are modularity, built-in message and data exchange mechanisms, XInclude and XML schema enabled XML configuration management tools, dedicated log management tools, internal debugging tools, both dynamic and static module chains support, internal DSO version and consistency checking, well defined API for developing specialized frameworks. It is supported on Scientific Linux 4 and 5 and planned to be ported to other platforms as well. The project is provided with the comprehensive set of technical documentation and users' guides. The licensing schema for the source code, binaries and documentation implies that the product is free for non-commercial use. Although the development phase is not over and many features are to be implemented yet the project is considered ready for public use and creating applications in various fields including development of events reconstruction software for small and moderate scale HEP experiments.

AMI is the main interface for searching for ATLAS datasets using physics metadata criteria. AMI has been implemented as a generic database management framework that allows parallel searching over many catalogues, which may have differing schema, and may be distributed geographically, using different RDBMS.

The main features of the web interface will be described; in particular the powerful graphic query builder. The use of XML/XLST technology ensures that all commands can be used either on the web or from a command line interface via a web service.

Partial wave analysis is an important tool for determining resonance properties in hadron spectroscopy. For large data samples however, the un-binned likelihood fits employed are computationally very expensive. At the Beijing Spectrometer (BES) III experiment, an increase in statistics compared to earlier experiments of up to two orders of magnitude is expected. In order to allow for a timely analysis of these datasets, additional computing power with short turnover times has to be made available. It turns out that graphics processing units (GPUs) originally developed for 3D computer games have an architecture of massively parallel single instruction multiple data floating point units that is almost ideally suited for the algorithms employed in partial wave analysis. We have implemented a framework for tensor manipulation and partial wave fits called GPUPWA. The user writes a program in pure C++ whilst the GPUPWA classes handle computations on the GPU, memory transfers, caching and other technical details. In conjunction with a recent graphics processor, the framework provides a speed-up of the partial wave fit by more than two orders of magnitude compared to legacy FORTRAN code.

Within the last years, HepMC has established itself as the standard event format for simulation of high-energy physics interactions and is commonly used by all LHC experiments. At the energies of the proton-proton collisisions at the LHC, a full description of the generation of these events and the subsequent interactions with the detector typically involves several thousand particles and several hundred vertices. Currently, the HepMC library only provides a text-based representation of these events.

HepMCVisual is a visualization package for HepMC events, allowing to interactively browse through the event. Intuitive user guiding and the possibility of expanding/collapsing specific branches of the interaction tree allow quick navigation and visualization of the specific parts of the event of interest to the user. Thus, it may be a valuble tool not only for physics users but also for debugging MonteCarlo event generators.

Being based on the ROOT graphics libraries, HepMC visual can be used as a standalone library, as well as interactively from the ROOT console or in combination with the HepMCBrowser interface within the ATLAS software framework.

DIRAC, the LHCb community Grid solution, provides access to a vast amount of computing and storage resources to a large number of users. In DIRAC users are organized in groups with different needs and permissions. In order to ensure that only allowed users can access the resources and to enforce that there are no abuses, security is mandatory. All DIRAC services and clients use secure connections that are authenticated using certificates and grid proxies. Once a client has been authenticated, authorization rules are applied to the requested action based on the presented credentials. These authorization rules and the list of users and groups are centrally managed in the DIRAC Configuration Service. Users submit jobs to DIRAC using their local credentials. From then on, DIRAC has to interact with different Grid services on behalf of this user. DIRAC has a proxy management service where users upload short-lived proxies to be used when DIRAC needs to act on behalf of them. Long duration proxies are uploaded by users to a MyProxy service, and DIRAC retrieves new short delegated proxies when necessary. This contribution discusses the details of the implementation of this security infrastructure in DIRAC.

During operation of high energy physics experiments a big amount of slow control data is recorded. It is necessary to examine all collected data checking the integrity and validity of measurements. With growing maturity of AJAX technologies it becomes possible to construct sophisticated interfaces using web technologies only.

Our solution for handling time series, generally slow control data, has a modular architecture: backend system for data analysis and preparation, a web service interface for data access and a fast AJAX web display. In order to provide fast interactive access the time series are aggregated over time slices of few predefined lengths. The aggregated values are stored in the temporary caching database and, then, are used to create generalizing data plots. These plots may include indication of data quality and are generated within few hundreds of milliseconds even if very high data rates are involved. The extensible export subsystem provides data in multiple formats including CSV, Excel, ROOT, and TDMS. The search engine can be used to find periods of time where indications of selected sensors are falling into the specified ranges. Utilization of the caching database allows performing most of such lookups within a second. Based on this functionality a web interface facilitating fast (Google-maps style) navigation through the data has been implemented.

The solution is at the moment used by several slow control systems at Test Facility for Fusion Magnets (TOSKA) and Karlsruhe Tritium Neutrino (KATRIN).

The ATLAS detector has been designed to exploit the full discovery potential of the LHC proton-proton collider at CERN. Its Muon Spectrometer (MS) has been optimized to measure final state muons from those interactions with good momentum resolution in a wide momentum range. In order to ensure that the hardware, DAQ and reconstruction software of the ATLAS MS is functioning properly, Data Quality Monitoring (DQM) tools have been developed both for the online and the offline environment. The offline DQM is performed on histograms of interesting quantities, which are filled during data processing with the ATLAS software framework at the CERN Tier0 facility. Then those histograms can be displayed and browsed by shifters and experts. They are also given as input to the Data Quality Monitoring Framework (DQMF) application, which performs the actual data quality assessment and sets status flags. The offline muon DQM structure and content, as well as the corresponding tools developed, are presented, with examples from the cosmic ray data collected for the MS Barrel during the commissioning phase.

High Energy and Nuclear Physics (HENP) experiments store Petabytes of event data and Terabytes of calibration data in ROOT files. The Petaminer project is developing a custom MySQL storage engine to enable the MySQL query processor to directly access experimental data stored in ROOT files. Our project is addressing the problem of efficient navigation to PetaBytes of HENP experimental data described with event-level TAG metadata, which is required by data intensive physics communities such as the LHC and RHIC experiments. Physicists need to be able to compose a metadata query and rapidly retrieve the set of matching events, where improved efficiency will facilitate the discovery process by permitting rapid iterations of data evaluation and retrieval. Our custom MySQL storage engine enables the MySQL query processor to directly access TAG data stored in ROOT TTrees. As ROOT TTrees are column-oriented, reading them directly provides improved performance over traditional row-oriented TAG databases. Leveraging the flexible and powerful SQL query language to access data stored in ROOT TTrees, the Petaminer approach enables rich MySQL index-building capabilities for further performance optimization.

The measurement of the experiment software performance is a very important metric in order to choose the most effective resources to be used and to discover the bottlenecks of the code implementation. In this work we present the benchmark techniques used to measure the ATLAS software performance through the ATLAS offline testing engine Kit Validation and the online portal Global Kit Validation. The performance measurements, the data collection, the online analysis and display of the results will be presented. The results of the measurement on different platforms and architectures will be shown, giving a full report on the CPU power and memory consumption of the Monte Carlo generation, simulation, digitization and reconstruction of the most CPU-intensive channels. The impact of the multi-core computing on the ATLAS software performance will also be presented, comparing the behavior of different architectures when increasing the number of concurrent processes. The benchmark techniques described in this paper have been used in the HEPiX group since the beginning of 2008 to help defining the performance metrics for the High Energy Physics applications, based on the real experiment software.

Geant4 is a toolkit to simulate the passage of particles through matter, and is widely used in HEP, in medical physics and for space applications. Ongoing developments and improvements require regular testing for new or modified code. Geant4 is a world-wide collaboration and it is developed by many different organizations and people, so integration of new and modified code needs to be tested regularly. Geant4 integration testing has been migrated to the LCG Applications Area nightly builds system, a system which unifies building and testing of the Applications Area projects.

In recent years, it has become more and more evident that software threat communities are taking an increasing interest in Grid infrastructures. To mitigate the security risk associated with the increased numbers of attacks, the Grid software development community needs to scale up effort to reduce software vulnerabilities. This can be achieved by introducing security review processes as a standard project management practice.

The Grid Facilities Department of the Fermilab Computing Division has developed a code inspection process, tailored to reviewing security properties of software. The goal of the process is to identify technical risks associated with an application and their impact.

This is achieved by focusing on the business needs of the application (what it does and protects), on understanding threats and exploit communities (what an exploiter gains), and on uncovering potential vulnerabilities (what defects can be exploited). The desired outcome of the process is an improvement of the quality of the software artifact and an enhanced understanding of possible mitigation strategies for residual risks.

This paper describes the inspection process and lessons learned on applying it to Grid middleware.

The CMS software stack currently consists of more than 2 Million lines of code developed by over 250 authors with a new version being released every week. CMS has setup a validation process for quality assurance which enables the developers to compare the performance of a release to previous releases and references.

The validation process provides the developers with reconstructed datasets of real data and MC samples. The samples span the whole range of detector effects and important physics signatures to benchmark the performance of the software. They are used to investigate interdependency effects of all CMS software components and to find and fix bugs.

The release validation process described here is an integral part of CMS software development and contributes significantly to ensure stable production and analysis. It represents a sizable contribution to the overall MC production of CMS. Its success emphasizes the importance of a streamlined release validation process for projects with a large code basis and significant number of developers and can function as a model for future projects.

VISPA is a development environment for high energy physics analyses which enables physicists to combine graphical and textual work. A physics analysis cycle consists of prototyping, performing, and verifying the analysis. The main feature of VISPA is a multipurpose window for visual steering of analysis steps, creation of analysis templates, and browsing physics event data at different steps of an analysis. VISPA follows an experiment-independent approach and incorporates various tools for steering and controlling required in a typical analysis. Connection to different frameworks of high energy physics experiments is achieved by using different types of interfaces. We present the look-and-feel for an example physics analysis at the LHC and explain the underlying software concepts of VISPA.

The core software stack both from the LCG Application Area and LHCb consists of more than 25 C++/Fortran/Python projects built for about 20 different configurations on Linux, Windows and MacOSX. To these projects, one can also add about 70 external software packages (Boost, Python, Qt, CLHEP, ...) which also have to be built for the same configurations. It order to reduce the time of the development cycle and assure the quality, a framework has been developed for the daily (in fact nightly) build and test of the software. Performing the build and the tests on several configurations and platforms increases the efficiency of the unit and integration tests. Main features: – flexible and fine grained setup (full, partial build) through a web interface; – possibility to build several "slots" with different configurations; – precise and highly granular reports on a web server; – support for CMT projects (but not only) with their cross-dependencies; – scalable client-server architecture for the control machine and its build machines; – copy of the results in a common place to allow early view of the software stack. The nightly build framework is written in Python for portability and it is easily extensible to accommodate new build procedures.

The CMS experiment has implemented a flexible and powerful system enabling users to find data within the CMS physics data catalog. The Dataset Bookkeeping Service (DBS) comprises a database and the services used to store and access metadata related to CMS physics data. To this, we have added a generalized query system in addition to the existing web and programmatic interfaces to the DBS. This query system is based on a query language that hides the complexity of the underlying database structure by discovering the join conditions between database tables. This provides a way of querying the system that is simple and straightforward for CMS data managers and physicists to use without requiring knowledge of the database tables or keys. The DBS Query Language uses the ANTLR tool to build the input query parser and tokenizer, followed by a query builder that uses a graph representation of the DBS schema to construct the SQL query sent to underlying database. We will describe the design of the query system, provide details of the language components and overview of how this component fits into the overall data discovery system architecture.

The fitting procedures are based on numerical minimization of functions. The MINUIT package is the most common package used for such procedures in High Energy Physics community. The main algorithm in this package, MIGRAD, searches the minimum of a function using the gradient information. For each minimization iteration, MIGRAD requires the calculation of the derivative for each free parameter of the function to be minimized. Minimization is required for data analysis problems based on the maximum likelihood technique. The calculation of complex likelihood functions, with several free parameters, many independent variables and large data samples, can be very CPU-time consuming. Then, the minimization process requires the calculation of the likelihood functions several times for each minimization iteration. In this paper we will show how MIGRAD algorithm and the likelihood function calculation can be easily parallelized using Message Passing Interface techniques. We will present the speed-up improvements obtained in typical physics applications such as complex maximum likelihood fits using the RooFit package.

The automated multi-platform software nightly build system is a major component in the ATLAS collaborative software organization, validation and code approval schemes. Code developers from ATLAS participating Institutes spread all around the world use about 30 branches of nightly releases for testing new packages, verification of patches to existing software, and migration to new platforms and compilers. The nightly releases lead up to, and are the basis of, stable software releases used for data processing worldwide. The ATLAS nightly builds are managed by the fully automated NICOS framework on the computing farm with 44 powerful multiprocessor nodes. The ATN test tool is embedded within the nightly system and provides results shortly after full compilations complete. Other test frameworks are synchronized with NICOS jobs and run larger scale validation jobs using the nightly releases. NICOS web pages dynamically provide information about the progress and results of the builds. For faster feedback, e-mail notifications about nightly releases problems are automatically distributed to the developers responsible.

Reliable population of the condition databases is critical for the correct operation of the online selection as well as of the offline reconstruction and analysis of data. We will describe here the system put in place in the CMS experiment to populate the database and make condition data promptly available both online for the high-level trigger and offline for reconstruction. The system, designed for high flexibility to cope with very different data sources, uses POOL-ORA technology in order to store data in an object format that best matches the object oriented paradigm for C++ programming language used in the CMS offline software. In order to ensure consistency among the various subdetectors, a dedicated package, PopCon (Populator of Condition Objects), is used to store data online. The data are then automatically streamed to the offline database hence immediately accessible offline worldwide. This mechanism was intensively used during 2008 in the test-runs with cosmic rays. The experience of this first months of operation will be discussed in detail.

CMS software consists of over two million lines of code actively developed by hundreds of developers from all around the world. Optimal build, release and distribution of such a large-scale system for production and analysis activities for hundreds of sites and multiple platforms are quite a challenge. Its dependency on more than hundred external tools make its build and distribution more complex. We describe how parallel build of software and minimal distribution size dramatically reduced the time gap between software build and installation on remote sites, and how producing few big binary products, instead of thousands of small ones, helped finding out the integration and runtime issues.

At CHEP2007 we reported on the perfmon2 subsystem as a tool for interfacing to the PMUs (Performance Monitoring Units) which are found in the hardware of all modern processors (from AMD, Intel, SUN, IBM, MIPS, etc.). The intent was always to get the subsystem into the Linux kernel by default. This paper reports on how progress was made (after long discussions) and will also show the latest additions to the subsystems.

FROG [1] [2] is a generic framework dedicated to visualisation of events in high energy physics experiment. It is suitable to any particular physics experiment or detector design. The code is light (< 3 MB) and fast (browsing time ~ 20 events per second for a large High Energy Physics experiment) and can run on various operating systems, as its object-oriented structure (C++) relies on the cross-platform OpenGL[3] and Glut [4] libraries. Moreover, Frog does not require installation of heavy third party libraries for the visualisation. This documents describes the features and principles of Frog version 1.106, its working scheme and numerous functionalities such as: 3D and 2D visualisation, graphical user interface, mouse interface, configuration files, production of pictures of various format, integration of personal objects, etc. Finally the application of FROG for physic experiment/environement, such as Gastof, CMS, ILD, Delphes will be presented for illustration.

During the operation, maintenance, and dismantling periods of the ATLAS Experiment, the traceability of all detector equipment must be guaranteed for logistic and safety matters. The running of the Large Hadron Collider will expose the ATLAS detector to radiation. Therefore, CERN must follow specific regulations from both the French and Swiss authorities for equipment removal, transport, repair, and disposal. GLANCE Traceability, implemented in C++ and Java/Java3D, has been developed to fulfill the requirements. The system registers and associates each equipment part to either a functional position in the detector or a zone outside the underground area through a 3D graphical user interface. Radiation control of the equipment is performed using a radiation monitor connected to the system: the local background gets stored and the threshold is automatically calculated. The system classifies the equipment as non radioactive if its radiation dose does not exceed that limit value. History for both location traceability and radiation measurements is ensured, as well as simultaneous management of multiples equipment. The software is fully operational, being used by the Radiation Protection Experts of ATLAS and trained users since the first beam of the LHC. Initially developed for the ATLAS detector, the flexibility of the system has allowed its adaptation for the LHCb detector.

With the integration of all CMS software packages into one release, the CMS software release management team faced the problem that for some applications a big distribution size and a large number of unused packages have become a real issue. TWe describe a solution to this problem. Based on functionality requirements and dependency analysis, we define a self-contained subset of the full CMS software release and create a Partial Release for such applications. We describe a high level architecture for this model, and tools that are used to automate the release preparation. Finally we discuss the two most important use cases for which this approach is currently implemented.

With the start of Large Hadron Collider approaching, storage and management of raw event data, as well as reconstruction and analysis data, is of crucial importance for the researchers. The CERN Advanced STORage system (CASTOR) is a hierarchical system developed at CERN, used to store physics production files and user files. CASTOR, as one of the essential software tools used by the LHC experiments, has to provide reliable services for storing and managing data. Monitoring of this complicated system is mandatory in order to assure its stable operation and improve its future performance. This paper presents the new monitoring system of CASTOR which provides operation and user request specific metrics. This system is build around a dedicated, optimized database schema. The schema is populated by PL/SQL procedures, which process a stream of incoming raw metadata from different CASTOR components, initially collected by the Distributed Logging Facility (DLF). A web interface has been developed for the visualization of the monitoring data. The different histograms and plots are created using PHP scripts which query the monitoring database.

A set of tools have been developed to ensure the Data Management operations (deletion, consistency checks) within the German cloud for ATLAS. These tools are described hereafter and presented in the context of the operational procedures of the German cloud. A particular emphasis is put on the consistency checks between the different catalogs (LFC, DQ2 Central Catalogs) and the files stored on the Storage Element. These consistency checks are crucial to be sure that all the data stored in the sites are actually available for the users and to get rid of non registered files also known as Dark Data.

CERN has a successful experience with running Server Self Service Center (S3C) for virtual server provisioning which is based on Microsoft® Virtual Server 2005. With the introduction of Windows Server 2008 and its built-in hypervisor based virtualization (Hyper-V) there are new possibilities for the expansion of the current service. This paper describes the architecture of the redesigned virtual Server Self Service based on Hyper-V which provides dynamically scalable virtualized resources on demand as needed and outlines the possible implications on the future use of virtual machines at CERN.

EVE is a high-level visualization library using ROOT's data-processing, GUI and OpenGL interfaces. It is designed as a framework for object management offering hierarchical data organization, object interaction and visualization via GUI and OpenGL representations. Automatic creation of 2D projected views is also supported. On the other hand, it can serve as an event visualization toolkit satisfying most HEP requirements: visualization of geometry, simulated and reconstructed data such as hits, clusters, tracks and calorimeter information. Special classes are available for visualization of raw-data. Object-interaction layer allows for easy selection and highlighting of objects and their derived representations (projections) across several views (3D, Rho-Z, R-Phi). Object-specific tooltips are provided in both GUI and GL views. The visual-configuration layer of EVE is built around a data-base of template objects that can be applied to specific instances of visualization objects to ensure consistent object presentation. The data-base can be retrieved from a file, edited during the framework operation and stored to file. EVE prototype was developed within the ALICE collaboration and has been included into ROOT in December 2007. Since then all EVE components have reached maturity. EVE is used as the base of AliEve visualization framework in ALICE, Firework physics-oriented event-display in CMS, and as the visualization engine of FairRoot in FAIR.

CERN, the European Laboratory for Particle Physics, located in Geneva – Switzerland, has recently started the Large Hadron Collider (LHC), a 27 km particle accelerator. The CERN Engineering and Equipment Data Management Service (EDMS) provides support for managing engineering and equipment information throughout the entire lifecycle of a project. Based on several both in-house developed and commercial data management systems, this service supports management and follow-up of different kinds of information throughout the lifecycle of the LHC project: design, manufacturing, installation, commissioning data, maintenance and more. The data collection phase, carried out by specialists, is now being replaced by a phase during which data will be consulted on an extensive basis by non-experts users. In order to address this change, a Web portal for the EDMS has been developed. It brings together in one space all the aspects covered by the EDMS: project and document management, asset tracking and safety follow-up. This paper presents the EDMS Web portal, its dynamic content management and its "one click" information search engine.

At a data rate of 200 hertz, event metadata records ("TAGs," in ATLAS parlance) provide fertile grounds for development and evaluation of tools for scalable data mining. It is easy, of course, to apply HEP-specific selection or classification rules to event records and to label such an exercise "data mining," but our interest is different. Advanced statistical methods and tools such as classification, association rule mining, and cluster analysis are common outside the high energy physics community. These tools can prove useful, not for discovery physics, but for learning about our data, our detector, and our software. A fixed and relatively simple schema makes TAG export to other storage technologies such as HDF5 straightforward. This simplifies the task of exploiting very-large-scale parallel platforms such as Argonne National Laboratory's BlueGene/P, currently the largest supercomputer in the world for open science, in the development of scalable tools for data mining. Using a domain-neutral scientific data format may also enable us to take advantage of existing data mining components from other communities. There is, further, a substantial literature on the topic of one-pass algorithms and stream mining techniques, and such tools may be inserted naturally at various points in the event data processing and distribution chain. This paper describes early experience with event metadata records from ATLAS simulation and commissioning as a testbed for scalable data mining tool development and evaluation.

The Muon System, facing the challenge requirement of the conditions data storage, has extensively started to use the conditions database project 'COOL' as the basis for all its conditions data storage both at CERN and throughout the worldwide collaboration as decided by the ATLAS Collaboration. The management of the Muon COOL conditions database will be one of the most challenging applications for Muon System, both in terms of data volumes and rates, but also in terms of the variety of data stored. The Muon conditions database is responsible for almost all of the 'non event' data and detector quality flags storage needed for debugging of the detector operations and for performing reconstruction and analysis. The COOL database allows database applications to be written independently of the underlying database technology and ensures long term compatibility with the entire ATLAS Software. COOL implements an interval of validity database, i.e. objects stored or referenced in COOL have an associated start and end time between which they are valid, the data is stored in folders, which are themselves arranged in a hierarchical structure of folder sets. The structure is simple and mainly optimized to store and retrieve object(s) associated with a particular time. In this work, an overview of the entire Muon conditions database architecture is given, including the different sources of the data and the storage model used. In addiction the software interfaces used to access to the conditions data are described, more emphasis is given to the Offline Reconstruction framework ATHENA and the services developed to provide the conditions data to the reconstruction.

The PanDA distributed production and analysis system has been in production use for ATLAS data processing and analysis since late 2005 in the US, and globally throughout ATLAS since early 2008. Its core architecture is based on a set of stateless web services served by Apache and backed by a suite of MySQL databases that are the repository for all PanDA information: active and archival job queues, dataset and file catalogs, site configuration information, monitoring information, system control parameters, and so on. This database system is one of the most critical components of PanDA, and has successfully delivered the functional and scaling performance required by PanDA, currently operating at a scale of half a million jobs per week, with much growth still to come. In this paper we describe the design and implementation of the PanDA database system, its architecture of MySQL servers deployed at BNL and CERN, backup strategy and monitoring tools. The system has been developed, thoroughly tested, and brought to production to provide highly reliable, scalable, flexible and available database services for ATLAS Monte Carlo production, reconstruction and physics analysis.