Table of contents

This volume of the IOP Conference Series is dedicated to scientific contributions presented at the 16th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2014), this year the motto was ''bridging disciplines''. The conference took place on September 1-5, 2014, at the Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic.

The 16th edition of ACAT explored the boundaries of computing system architectures, data analysis algorithmics, automatic calculations, and theoretical calculation technologies. It provided a forum for confronting and exchanging ideas among these fields, where new approaches in computing technologies for scientific research were explored and promoted.

This year's edition of the workshop brought together over 140 participants from all over the world. The workshop's 16 invited speakers presented key topics on advanced computing and analysis techniques in physics. During the workshop, 60 talks and 40 posters were presented in three tracks: Computing Technology for Physics Research, Data Analysis - Algorithms and Tools, and Computations in Theoretical Physics: Techniques and Methods. The round table enabled discussions on expanding software, knowledge sharing and scientific collaboration in the respective areas.

ACAT 2014 was generously sponsored by Western Digital, Brookhaven National Laboratory, Hewlett Packard, DataDirect Networks, M Computers, Bright Computing, Huawei and PDV-Systemhaus.

Special appreciations go to the track liaisons Lorenzo Moneta, Axel Naumann and Grigory Rubtsov for their work on the scientific program and the publication preparation. ACAT's IACC would also like to express its gratitude to all referees for their work on making sure the contributions are published in the proceedings. Our thanks extend to the conference liaisons Andrei Kataev and Jerome Lauret who worked with the local contacts and made this conference possible as well as to the program coordinator Federico Carminati and the conference chair Denis Perret-Gallix for their global supervision.

Further information on ACAT 2014 can be found at http://www.particle.cz/acat2014

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the proceedings Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

High Performance Computing is relevant in many applications around the world, particularly high energy physics. Experiments such as ATLAS, CMS, ALICE and LHCb generate huge amounts of data which need to be stored and analyzed at server farms located on site at CERN and around the world. Apart from the initial cost of setting up an effective server farm the cost of power consumption and cooling are significant. The proposed solution to reduce costs without losing performance is to make use of ARM^® processors found in nearly all smartphones and tablet computers. Their low power consumption, low cost and respectable processing speed makes them an interesting choice for future large scale parallel data processing centers. Benchmarks on the Cortex^TM-A series of ARM^® processors including the HPL and PMBW suites will be presented as well as preliminary results from the PROOF benchmark in the context of high energy physics will be analyzed.

Scientific experiments are becoming highly data intensive to the point where offline processing of stored data is infeasible. High data throughput computing or High Volume throughput Computing, for future projects is required to deal with terabytes of data per second. Conventional data-centres based on typical server-grade hardware are expensive and are biased towards processing power rather than I/O bandwidth. This system imbalance can be solved with massive parallelism to increase the I/O capabilities, at the expense of excessive processing power and high energy consumption. The Massive Affordable Computing Project aims to use low-cost, ARM System on Chips to address the issue of system balance, affordability and energy efficiency. An ARM-based Processing Unit prototype is currently being developed, with a design goal of 20 Gb/s I/O throughput and significant processing power. Novel use of PCI-Express is used to address the typically limited I/O capabilities of consumer ARM System on Chips.

The GeantV project is focused on the R&D of new particle transport techniques to maximize parallelism on multiple levels, profiting from the use of both SIMD instructions and co-processors for the CPU-intensive calculations specific to this type of applications. In our approach, vectors of tracks belonging to multiple events and matching different locality criteria must be gathered and dispatched to algorithms having vector signatures. While the transport propagates tracks and changes their individual states, data locality becomes harder to maintain. The scheduling policy has to be changed to maintain efficient vectors while keeping an optimal level of concurrency. The model has complex dynamics requiring tuning the thresholds to switch between the normal regime and special modes, i.e. prioritizing events to allow flushing memory, adding new events in the transport pipeline to boost locality, dynamically adjusting the particle vector size or switching between vector to single track mode when vectorization causes only overhead. This work requires a comprehensive study for optimizing these parameters to make the behaviour of the scheduler self-adapting, presenting here its initial results.

The ATLAS Error Reporting provides a service that allows experts and shift crew to track and address errors relating to the data taking components and applications. This service, called the Error Reporting Service (ERS), gives to software applications the opportunity to collect and send comprehensive data about run-time errors, to a place where it can be intercepted in real-time by any other system component. Other ATLAS online control and monitoring tools use the ERS as one of their main inputs to address system problems in a timely manner and to improve the quality of acquired data.

The actual destination of the error messages depends solely on the run-time environment, in which the online applications are operating. When an application sends information to ERS, depending on the configuration, it may end up in a local file, a database, distributed middleware which can transport it to an expert system or display it to users. Thanks to the open framework design of ERS, new information destinations can be added at any moment without touching the reporting and receiving applications.

The ERS Application Program Interface (API) is provided in three programming languages used in the ATLAS online environment: C++, Java and Python. All APIs use exceptions for error reporting but each of them exploits advanced features of a given language to simplify the end-user program writing. For example, as C++ lacks language support for exceptions, a number of macros have been designed to generate hierarchies of C++ exception classes at compile time. Using this approach a software developer can write a single line of code to generate a boilerplate code for a fully qualified C++ exception class declaration with arbitrary number of parameters and multiple constructors, which encapsulates all relevant static information about the given type of issues. When a corresponding error occurs at run time, the program just need to create an instance of that class passing relevant values to one of the available class constructors and send this instance to ERS.

This paper presents the original design solutions exploited for the ERS implementation and describes how it was used during the first ATLAS run period. The cross-system error reporting standardization introduced by ERS was one of the key points for the successful implementation of automated mechanisms for online error recovery.

The ATLAS detector at CERN records proton-proton collisions delivered by the Large Hadron Collider (LHC). The ATLAS Trigger and Data-Acquisition (TDAQ) system identifies, selects, and stores interesting collision data. These are received from the detector readout electronics at an average rate of 100 kHz. The typical event data size is 1 to 2 MB. Overall, the ATLAS TDAQ system can be seen as a distributed software system executed on a farm of roughly 2000 commodity PCs. The worker nodes are interconnected by an Ethernet network that at the restart of the LHC in 2015 is expected to experience a sustained throughput of several 10 GB/s.

A particular type of challenge posed by this system, and by DAQ systems in general, is the inherently burstynature of the data traffic from the readout buffers to the worker nodes. This can cause instantaneous network congestion and therefore performance degradation. The effect is particularly pronounced for unreliable network interconnections, such as Ethernet.

In this paper we report on the design of the data-flow software for the 2015-2018 data-taking period of the ATLAS experiment. This software will be responsible for transporting the data across the distributed Data-Acquisition system. We will focus on the strategies employed to manage the network congestion and therefore minimisethe data-collection latency and maximisethe system performance. We will discuss the results of systematic measurements performed on different types of networking hardware. These results highlight the causes of network congestion and the effects on the overall system performance.

ATLAS is a Physics experiment that explores high-energy particle collisions at the Large Hadron Collider at CERN. It uses tens of millions of electronics channels to capture the outcome of the particle bunches crossing each other every 25 ns. Since reading out and storing the complete information is not feasible (~100 TB/s), ATLAS makes use of a complex and highly distributed Trigger and Data Acquisition (TDAQ) system, in charge of selecting only interesting data and transporting those to permanent mass storage (~1 GB/s) for later analysis. The data reduction is carried out in two stages: first, custom electronics performs an initial level of data rejection for each bunch crossing based on partial and localized information. Only data corresponding to collisions passing this stage of selection will be actually read-out from the on-detector electronics. Then, a large computer farm (~17 k cores) analyses these data in real-time and decides which ones are worth being stored for Physics analysis. A large network allows moving the data from ~2000 front-end buffers to the location where they are processed and from there to mass storage. The overall TDAQ system is embedded in a common software framework that allows controlling, configuring and monitoring the data taking process. The experience gained during the first period of data taking of the ATLAS experiment (Run I, 2010-2012) has inspired a number of ideas for improvement of the TDAQ system that are being put in place during the so-called Long Shutdown 1 of the Large Hadron Collider (LHC), in 2013/14. This paper summarizes the main changes that have been applied to the ATLAS TDAQ system and highlights the expected performance and functional improvements that will be available for the LHC Run II. Particular emphasis will be put on the evolution of the software-based data selection and of the flow of data in the system. The reasons for the modified architectural and technical choices will be explained, and details will be provided on the simulation and testing approach used to validate this system.

The ATLAS experiment at the Large Hadron Collider at CERN relies on a complex and highly distributed Trigger and Data Acquisition (TDAQ) system to gather and select particle collision data obtained at unprecedented energy and rates. The Run Control (RC) system is the component steering the data acquisition by starting and stopping processes and by carrying all data-taking elements through well-defined states in a coherent way. Taking into account all the lessons learnt during LHC's Run 1, the RC has been completely re-designed and re-implemented during the LHC Long Shutdown 1 (LS1) phase. As a result of the new design, the RC is assisted by the Central Hint and Information Processor (CHIP) service that can be truly considered its "brain". CHIP is an intelligent system able to supervise the ATLAS data taking, take operational decisions and handle abnormal conditions. In this paper, the design, implementation and performances of the RC/CHIP system will be described. A particular emphasis will be put on the way the RC and CHIP cooperate and on the huge benefits brought by the Complex Event Processing engine. Additionally, some error recovery scenarios will be analysed for which the intervention of human experts is now rendered unnecessary.

A redesign of the tracking algorithms for the ATLAS trigger for LHC's Run 2 starting in 2015 is in progress. The ATLAS HLT software has been restructured to run as a more flexible single stage HLT, instead of two separate stages (Level 2 and Event Filter) as in Run 1. The new tracking strategy employed for Run 2 will use a Fast Track Finder (FTF) algorithm to seed subsequent Precision Tracking, and will result in improved track parameter resolution and faster execution times than achieved during Run 1. The performance of the new algorithms has been evaluated to identify those aspects where code optimisation would be most beneficial. The performance and timing of the algorithms for electron and muon reconstruction in the trigger are presented. The profiling infrastructure, constructed to provide prompt feedback from the optimisation, is described, including the methods used to monitor the relative performance improvements as the code evolves.

EOS is an open source distributed filesystem developed and used mainly at CERN. It provides low latency, high availability, strong authentication, multiple replication schemas as well as multiple access protocols and features. Deployment and operations remain simple and EOS is currently being used by multiple experiments at CERN providing a total raw storage space of 86PB. A brief overview of EOS's architecture is given then its main and latest features are reviewed and some operations facts are reported. Finally, emphasis is laid on the new infrastructure aware file placement and access engine.

An OpenStack based private cloud with the Cluster File System has been built and used with both CMS analysis and Monte Carlo simulation jobs in the Datacenter Indirection Infrastructure for Secure High Energy Physics (DII-HEP) project. On the cloud we run the ARC middleware that allows running CMS applications without changes on the job submission side. Our test results indicate that the adopted approach provides a scalable and resilient solution for managing resources without compromising on performance and high availability.

To manage the virtual machines (VM) dynamically in an elastic fasion, we are testing the EMI authorization service (Argus) and the Execution Environment Service (Argus-EES). An OpenStackplugin has been developed for Argus-EES.

The Host Identity Protocol (HIP) has been designed for mobile networks and it provides a secure method for IP multihoming. HIP separates the end-point identifier and locator role for IP address which increases the network availability for the applications. Our solution leverages HIP for traffic management.

This presentation gives an update on the status of the work and our lessons learned in creating an OpenStackbased cloud for HEP.

Higher order corrections in perturbative quantum field theory are required for precise theoretical analysis to investigate new physics beyond the Standard Model. This indicates that we need to evaluate Feynman loop diagrams with multi-loop integrals which may require multi-precision calculation. We developed a dedicated accelerator system for multiprecision calculations (GRAPE9-MPX). We present performance results of our system for the case of Feynman two-loop box and three-loop selfenergy diagrams with multi-precision.

In the last years the problem of preservation of scientific data has become one of the most important topics inside international scientific communities. In particular the long term preservation of experimental data, raw and all related derived formats including calibration information, is one of the emerging requirements within the High Energy Physics (HEP) community for experiments that have already concluded the data taking phase. The DPHEP group (Data Preservation in HEP) coordinates the local teams within the whole collaboration and the different Tiers (computing centers). The INFN-CNAF Tier-1 is one of the reference sites for data storage and computing in the LHC community but it also offers resources to many other HEP and non-HEP collaborations. In particular the CDF experiment has used the INFN-CNAF Tier-1 resources for many years and after the end of data taking in 2011, it is now facing the challenge to both preserve the large amount of data produced during several years and to retain the ability to access and reuse the whole amount of it in the future. According to this task the CDF Italian collaboration, together with the INFN-CNAF computing center, has developed and is now implementing a long term future data preservation project in collaboration with Fermilab (FNAL) computing sector. The project comprises the copy of all CDF raw data and user level ntuples (about 4 PB) at the INFN-CNAF site and the setup of a framework which will allow to access and analyze the data in the long term future. A portion of the 4 PB of data (raw data and analysis-level ntuples) are currently being copied from FNAL to the INFN-CNAF tape library backend and a system to allow data access is being setup. In addition to this data access system, a data analysis framework is being developed in order to run the complete CDF analysis chain in the long term future, from raw data reprocessing to analysis-level ntuples production and analysis. In this contribution we first illustrate the difficulties and the technical solutions adopted to copy, store and maintain CDF data at the INFN-CNAF Tier-1 computing center. In addition we describe how we are exploiting virtualization techniques for the purpose of building the long term future analysis framework.

The consolidation of Mass Storage services at the INFN-CNAF Tier1 Storage department that has occurred during the last 5 years, resulted in a reliable, high performance and moderately easy-to-manage facility that provides data access, archive, backup and database services to several different use cases. At present, the GEMSS Mass Storage System, developed and installed at CNAF and based upon an integration between the IBM GPFS parallel filesystem and the Tivoli Storage Manager (TSM) tape management software, is one of the largest hierarchical storage sites in Europe. It provides storage resources for about 12% of LHC data, as well as for data of other non-LHC experiments. Files are accessed using standard SRM Grid services provided by the Storage Resource Manager (StoRM), also developed at CNAF. Data access is also provided by XRootD and HTTP/WebDaV endpoints. Besides these services, an Oracle database facility is in production characterized by an effective level of parallelism, redundancy and availability. This facility is running databases for storing and accessing relational data objects and for providing database services to the currently active use cases. It takes advantage of several Oracle technologies, like Real Application Cluster (RAC), Automatic Storage Manager (ASM) and Enterprise Manager centralized management tools, together with other technologies for performance optimization, ease of management and downtime reduction. The aim of the present paper is to illustrate the state-of-the-art of the INFN-CNAF Tier1 Storage department infrastructures and software services, and to give a brief outlook to forthcoming projects. A description of the administrative, monitoring and problem-tracking tools that play a primary role in managing the whole storage framework is also given.

The storage and farming departments at the INFN-CNAF Tier1[1] manage approximately thousands of computing nodes and several hundreds of servers that provides access to the disk and tape storage. In particular, the storage server machines should provide the following services: an efficient access to about 15 petabytes of disk space with different cluster of GPFS file system, the data transfers between LHC Tiers sites (Tier0, Tier1 and Tier2) via GridFTP cluster and Xrootd protocol and finally the writing and reading data operations on magnetic tape backend. One of the most important and essential point in order to get a reliable service is a control system that can warn if problems arise and which is able to perform automatic recovery operations in case of service interruptions or major failures. Moreover, during daily operations the configurations can change, i.e. if the GPFS cluster nodes roles can be modified and therefore the obsolete nodes must be removed from the control system production, and the new servers should be added to the ones that are already present. The manual management of all these changes is an operation that can be somewhat difficult in case of several changes, it can also take a long time and is easily subject to human error or misconfiguration. For these reasons we have developed a control system with the feature of self-configure itself if any change occurs. Currently, this system has been in production for about a year at the INFN-CNAF Tier1 with good results and hardly any major drawback. There are three major key points in this system. The first is a software configurator service (e.g. Quattor or Puppet) for the servers machines that we want to monitor with the control system; this service must ensure the presence of appropriate sensors and custom scripts on the nodes to check and should be able to install and update software packages on them. The second key element is a database containing information, according to a suitable format, on all the machines in production and able to provide for each of them the principal information such as the type of hardware, the network switch to which the machine is connected, if the machine is real (physical) or virtual, the possible hypervisor to which it belongs and so on. The last key point is a control system software (in our implementation we choose the Nagios software), capable of assessing the status of the servers and services, and that can attempt to restore the working state, restart or inhibit software services and send suitable alarm messages to the site administrators. The integration of these three elements was made by appropriate scripts and custom implementation that allow the self-configuration of the system according to a decisional logic and the whole combination of all the above-mentioned components will be deeply discussed in this paper.

The ATLAS experiment is scaling up Big Data processing for the next LHC run using a multilevel workflow system comprised of many layers. In Big Data processing ATLAS deals with datasets, not individual files. Similarly a task (comprised of many jobs) has become a unit of the ATLAS workflow in distributed computing, with about 0.8M tasks processed per year. In order to manage the diversity of LHC physics (exceeding 35K physics samples per year), the individual data processing tasks are organized into workflows. For example, the Monte Carlo workflow is composed of many steps: generate or configure hard-processes, hadronize signal and minimum-bias (pileup) events, simulate energy deposition in the ATLAS detector, digitize electronics response, simulate triggers, reconstruct data, convert the reconstructed data into ROOT ntuples for physics analysis, etc. Outputs are merged and/or filtered as necessary to optimize the chain. The bi-level workflow manager - ProdSys2 - generates actual workflow tasks and their jobs are executed across more than a hundred distributed computing sites by PanDA - the ATLAS job-level workload management system. On the outer level, the Database Engine for Tasks (DEfT) empowers production managers with templated workflow definitions. On the next level, the Job Execution and Definition Interface (JEDI) is integrated with PanDA to provide dynamic job definition tailored to the sites capabilities. We report on scaling up the production system to accommodate a growing number of requirements from main ATLAS areas: Trigger, Physics and Data Preparation.

The private Cloud at the Torino INFN computing centre offers IaaS services to different scientific computing applications. The infrastructure is managed with the OpenNebula cloud controller. The main stakeholders of the facility are a grid Tier-2 site for the ALICE collaboration at LHC, an interactive analysis facility for the same experiment and a grid Tier-2 site for the BES-III collaboration, plus an increasing number of other small tenants. Besides keeping track of the usage, the automation of dynamic allocation of resources to tenants requires detailed monitoring and accounting of the resource usage. As a first investigation towards this, we set up a monitoring system to inspect the site activities both in terms of IaaS and applications running on the hosted virtual instances. For this purpose we used the Elasticsearch, Logstash and Kibana stack. In the current implementation, the heterogeneous accounting information is fed to different MySQL databases and sent to Elasticsearch via a custom Logstash plugin. For the IaaS metering, we developed sensors for the OpenNebula API. The IaaS level information gathered through the API is sent to the MySQL database through an ad-hoc developed RESTful web service, which is also used for other accounting purposes. Concerning the application level, we used the Root plugin TProofMonSenderSQL to collect accounting data from the interactive analysis facility. The BES-III virtual instances used to be monitored with Zabbix, as a proof of concept we also retrieve the information contained in the Zabbix database. Each of these three cases is indexed separately in Elasticsearch. We are now starting to consider dismissing the intermediate level provided by the SQL database and evaluating a NoSQL option as a unique central database for all the monitoring information. We setup a set of Kibana dashboards with pre-defined queries in order to monitor the relevant information in each case. In this way we have achieved a uniform monitoring interface for both the IaaS and the scientific applications, mostly leveraging off-the-shelf tools.

With the introduction of federated data access to the workflows of WLCG, it is becoming increasingly important for data centers to understand specific data flows regarding storage element accesses, firewall configurations, as well as the scheduling of batch jobs themselves. As existing batch system monitoring and related system monitoring tools do not support measurements at batch job level, a new tool has been developed and put into operation at the GridKa Tier 1 center for monitoring continuous data streams and characteristics of WLCG jobs and pilots. Long term measurements and data collection are in progress. These measurements already have been proven to be useful analyzing misbehaviors and various issues. Therefore we aim for an automated, realtime approach for anomaly detection. As a requirement, prototypes for standard workflows have to be examined. Based on measurements of several months, different features of HEP jobs are evaluated regarding their effectiveness for data mining approaches to identify these common workflows. The paper will introduce the actual measurement approach and statistics as well as the general concept and first results classifying different HEP job workflows derived from the measurements at GridKa.

Modern data processing increasingly relies on data locality for performance and scalability, whereas the common HEP approaches aim for uniform resource pools with minimal locality, recently even across site boundaries. To combine advantages of both, the High- Performance Data Analysis (HPDA) Tier 3 concept opportunistically establishes data locality via coordinated caches.

In accordance with HEP Tier 3 activities, the design incorporates two major assumptions: First, only a fraction of data is accessed regularly and thus the deciding factor for overall throughput. Second, data access may fallback to non-local, making permanent local data availability an inefficient resource usage strategy. Based on this, the HPDA design generically extends available storage hierarchies into the batch system. Using the batch system itself for scheduling file locality, an array of independent caches on the worker nodes is dynamically populated with high-profile data. Cache state information is exposed to the batch system both for managing caches and scheduling jobs. As a result, users directly work with a regular, adequately sized storage system. However, their automated batch processes are presented with local replications of data whenever possible.

In the ALICE experiment hundreds of users are analyzing big datasets on a Grid system. High throughput and short turn-around times are achieved by a centralized system called the LEGO trains. This system combines analysis from different users in so-called analysis trains which are then executed within the same Grid jobs thereby reducing the number of times the data needs to be read from the storage systems. The centralized trains improve the performance, the usability for users and the bookkeeping in comparison to single user analysis. The train system builds upon the already existing ALICE tools, i.e. the analysis framework as well as the Grid submission and monitoring infrastructure. The entry point to the train system is a web interface which is used to configure the analysis and the desired datasets as well as to test and submit the train. Several measures have been implemented to reduce the time a train needs to finish and to increase the CPU efficiency.

The File Access Monitoring Service (FAMoS) leverages the information stored in the central AliEn file catalogue, which describes every file in a Unix-like directory structure, as well as metadata on file location and its replicas. In addition, it uses the access information provided by a set of API servers, used by all Grid clients to access the catalogue. The main functions of FAMoS are to sort the file accesses by logical groups, access time, user and storage element. The collected data identifies rarely used groups of files, as well as those with high popularity over different time periods. This information can be further used to optimize file distribution and replication factors, thus increasing the data processing efficiency. The paper describes the FAMoS structure and user interface and presents the results obtained in one year of service operation.

HEP experiments produce enormous data sets at an ever-growing rate. To cope with the challenge posed by these data sets, experiments' software needs to embrace all capabilities modern CPUs offer. With decreasing ^memory/_core ratio, the one-process-per-core approach of recent years becomes less feasible. Instead, multi-threading with fine-grained parallelism needs to be exploited to benefit from memory sharing among threads.

Gaudi is an experiment-independent data processing framework, used for instance by the ATLAS and LHCbexperiments at CERN's Large Hadron Collider. It has originally been designed with only sequential processing in mind. In a recent effort, the frame work has been extended to allow for multi-threaded processing. This includes components for concurrent scheduling of several algorithms - either processingthe same or multiple events, thread-safe data store access and resource management.

In the sequential case, the relationships between algorithms are encoded implicitly in their pre-determined execution order. For parallel processing, these relationships need to be expressed explicitly, in order for the scheduler to be able to exploit maximum parallelism while respecting dependencies between algorithms. Therefore, means to express and automatically track these dependencies need to be provided by the framework.

In this paper, we present components introduced to express and track dependencies of algorithms to deduce a precedence-constrained directed acyclic graph, which serves as basis for our algorithmically sophisticated scheduling approach for tasks with dynamic priorities. We introduce an incremental migration path for existing experiments towards parallel processing and highlight the benefits of explicit dependencies even in the sequential case, such as sanity checks and sequence optimization by graph analysis.

We present a prototype of a scalable computing cloud. It is intended to be deployed on the basis of a cluster without the separate dedicated storage. The dedicated storage is replaced by the distributed software storage. In addition, all cluster nodes are used both as computing nodes and as storage nodes. This solution increases utilization of the cluster resources as well as improves fault tolerance and performance of the distributed storage. Another advantage of this solution is high scalability with a relatively low initial and maintenance cost. The solution is built on the basis of the open source components like OpenStack, CEPH, etc.

Thread-parallelisation and single-instruction multiple data (SIMD) "vectorisation" of software components in HEP computing has become a necessity to fully benefit from current and future computing hardware. In this context, the Geant-Vector/GPU simulation project aims to re-engineer current software for the simulation of the passage of particles through detectors in order to increase the overall event throughput. As one of the core modules in this area, the geometry library plays a central role and vectorising its algorithms will be one of the cornerstones towards achieving good CPU performance. Here, we report on the progress made in vectorising the shape primitives, as well as in applying new C++ template based optimisations of existing code available in the Geant4, ROOT or USolids geometry libraries. We will focus on a presentation of our software development approach that aims to provide optimised code for all use cases of the library (e.g., single particle and many-particle APIs) and to support different architectures (CPU and GPU) while keeping the code base small, manageable and maintainable. We report on a generic and templated C++ geometry library as a continuation of the AIDA USolids project. The experience gained with these developments will be beneficial to other parts of the simulation software, such as for the optimisation of the physics library, and possibly to other parts of the experiment software stack, such as reconstruction and analysis.

Modern high energy physics analysis is complex. It typically requires multiple passes over different datasets, and is often held together with a series of scripts and programs. For example, one has to first reweight the jet energy spectrum in Monte Carlo to match data before plots of any other jet related variable can be made. This requires a pass over the Monte Carlo and the Data to derive the reweighting, and then another pass over the Monte Carlo to plot the variables the analyser is really interested in. With most modern ROOT based tools this requires separate analysis loops for each pass, and script files to glue to the results of the two analysis loops together. A framework has been developed that uses the functional and declarative features of the C# language and its Language Integrated Query (LINQ) extensions to declare the analysis. The framework uses language tools to convert the analysis into C++ and runs ROOT or PROOF as a backend to get the results. This gives the analyser the full power of an object-oriented programming language to put together the analysis and at the same time the speed of C++ for the analysis loop. The tool allows one to incorporate C++ algorithms written for ROOT by others. A by-product of the design is the ability to cache results between runs, dramatically reducing the cost of adding one-more-plot and also to keep a complete record associated with each plot for data preservation reasons. The code is mature enough to have been used in ATLAS analyses. The package is open source and available on the open source site CodePlex.

MetaCentrum - The Czech National Grid provides access to various resources across the Czech Republic. The utilized resource management and scheduling system is based on a heavily modified version of the Torque Batch System. This open source resource manager is maintained in a local fork and was extended to facilitate the requirements of such a large installation. This paper provides an overview of unique features deployed in MetaCentrum. Notably, we describe our distributed setup that encompasses several standalone independent servers while still maintaining full cooperative scheduling across the grid. We also present the benefits of our virtualized infrastructure that enables our schedulers to dynamically request ondemand virtual machines, that are then used to facilitate the varied requirements of users in our system, as well as enabling support for user requested virtual clusters that can be further interconnected using a private VLAN.

The next generation B factory experiment Belle II will collect huge data samples which are a challenge for the computing system. To cope with the high data volume and rate, Belle II is setting up a distributed computing system based on existing technologies and infrastructure, plus Belle II specific extensions for workflow abstraction. This paper describes the highlights of the Belle II computing and the current status. We will also present the experience of the latest MC production campaign in 2014.

The VISPA project provides a graphical frontend to computing infrastructures. Currently, the focus of the project is to give an online environment for the development of data analyses. Access is provided through a web GUI, which has all functionality needed for working conditions comparable to a personal computer. This includes a new preference system as well as user configurable shortkeys. As all relevant software, data and computing resources are supplied on a common remote infrastructure the VISPA web framework offers a new way of collaborative work where analyses of colleagues can be reviewed and executed with just one click. Furthermore, VISPA can be extended to the specific needs of an experiment or other scientific use cases. This is presented in the form of a new GUI to the analysis framework Offline of the Pierre Auger collaboration.

The LHCb Data Acquisition (DAQ) will be upgraded in 2020 to a trigger-free readout. In order to achieve this goal we will need to connect around 500 nodes with a total network capacity of 32 Tb/s. To get such an high network capacity we are testing zero-copy technology in order to maximize the theoretical link throughput without adding excessive CPU and memory bandwidth overhead, leaving free resources for data processing resulting in less power, space and money used for the same result. We develop a modular test application which can be used with different transport layers. For the zero-copy implementation we choose the OFED IBVerbs API because it can provide low level access and high throughput. We present throughput and CPU usage measurements of 40 GbE solutions using Remote Direct Memory Access (RDMA), for several network configurations to test the scalability of the system.

Programming language evolution brought to us the domain-specific languages (DSL). They proved to be very useful for expressing specific concepts, turning into a vital ingredient even for general-purpose frameworks. Supporting declarative DSLs (such as SQL) in imperative languages (such as C++) can happen in the manner of language integrated query (LINQ).

We investigate approaches to integrate LINQ programming language, native to C++. We review its usability in the context of high energy physics. We present examples using CppLINQ for a few types data analysis workflows done by the end-users doing data analysis. We discuss evidences how this DSL technology can simplify massively parallel grid system such as PROOF.

The CernVM File System (CernVM-FS) is a snapshotting read-only file system designed to deliver software to grid worker nodes over HTTP in a fast, scalable and reliable way. In recent years it became the de-facto standard method to distribute HEP experiment software in the WLCG and starts to be adopted by other grid computing communities outside HEP. This paper focusses on the recent developments of the CernVM-FS Server, the central publishing point of new file system snapshots. Using a union file system, the CernVM-FS Server allows for direct manipulation of a (normally read-only) CernVM-FS volume with copy-on-write semantics. Eventually the collected changeset is transformed into a new CernVM-FS snapshot, constituting a transactional feedback loop. The generated repository data is pushed into a content addressable storage requiring only a RESTful interface and gets distributed through a hierarchy of caches to individual grid worker nodes. Additonally we describe recent features, such as file chunking, repository garbage collection and file system history that enable CernVM- FS for a wider range of use cases.

The scale of scientific High Performance Computing (HPC) and High Throughput Computing (HTC) has increased significantly in recent years, and is becoming sensitive to total energy use and cost. Energy-efficiency has thus become an important concern in scientific fields such as High Energy Physics (HEP). There has been a growing interest in utilizing alternate architectures, such as low power ARM processors, to replace traditional Intel x86 architectures. Nevertheless, even though such solutions have been successfully used in mobile applications with low I/O and memory demands, it is unclear if they are suitable and more energy-efficient in the scientific computing environment. Furthermore, there is a lack of tools and experience to derive and compare power consumption between the architectures for various workloads, and eventually to support software optimizations for energy efficiency. To that end, we have performed several physical and software-based measurements of workloads from HEP applications running on ARM and Intel architectures, and compare their power consumption and performance. We leverage several profiling tools (both in hardware and software) to extract different characteristics of the power use. We report the results of these measurements and the experience gained in developing a set of measurement techniques and profiling tools to accurately assess the power consumption for scientific workloads.

Electrical power requirements will be a constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics. Performance-per-watt is a critical metric for the evaluation of computer architectures for cost- efficient computing. Additionally, future performance growth will come from heterogeneous, many-core, and high computing density platforms with specialized processors. In this paper, we examine the Intel Xeon Phi Many Integrated Cores (MIC) co-processor and Applied Micro X-Gene ARMv8 64-bit low-power server system-on-a-chip (SoC) solutions for scientific computing applications. We report our experience on software porting, performance and energy efficiency and evaluate the potential for use of such technologies in the context of distributed computing systems such as the Worldwide LHC Computing Grid (WLCG).

The CMS experiment has recently completed the development of a multi-threaded capable application framework. In this paper, we will discuss the design, implementation and application of this framework to production applications in CMS. For the 2015 LHC run, this functionality is particularly critical for both our online and offline production applications, which depend on faster turn-around times and a reduced memory footprint relative to before. These applications are complex codes, each including a large number of physics-driven algorithms. While the framework is capable of running a mix of thread-safe and "legacy" modules, algorithms running in our production applications need to be thread-safe for optimal use of this multi-threaded framework at a large scale. Towards this end, we discuss the types of changes, which were necessary for our algorithms to achieve good performance of our multithreaded applications in a full-scale application. Finally performance numbers for what has been achieved for the 2015 run are presented.

High Energy Physics is one of the research areas where the accomplishment of scientific results is inconceivable without a complex computing infrastructure. This includes also the experiments at the Large Hadron Collider (LHC) at CERN where the production and analysis environment is provided by the Worldwide LHC Computing Grid (WLCG). A very important part of this system is represented by sites classified as Tier-2s: they deliver a half of the computing and disk storage capacity of the whole WLCG. In this contribution we present an overview of the Tier-2 site praguelcg2 in Prague, the largest site in the Czech Republic providing computing and storage services for particle physics experiments. A brief history flashback, current status report and future perspectives of the site will be presented.

In preparation for the new era of RHIC running (RHIC-II upgrades and possibly, the eRHIC era), the STAR experiment is expanding its modular Message Interface and Reliable Architecture framework (MIRA). MIRA allowed STAR to integrate meta-data collection, monitoring, and online QA components in a very agile and efficient manner using a messaging infrastructure approach. In this paper, we briefly summarize our past achievements, provide an overview of the recent development activities focused on messaging patterns and describe our experience with the complex event processor (CEP) recently integrated into the MIRA framework. CEP was used in the recent RHIC Run 14, which provided practical use cases. Finally, we present our requirements and expectations for the planned expansion of our systems, which will allow our framework to acquire features typically associated with Detector Control Systems. Special attention is given to aspects related to latency, scalability and interoperability within heterogeneous set of services, various data and meta-data acquisition components coexisting in STAR online domain.

The ATLAS experiment has successfully used its Gaudi/Athena software framework for data taking and analysis during the first LHC run, with billions of events successfully processed. However, the design of Gaudi/Athena dates from early 2000 and the software and the physics code has been written using a single threaded, serial design. This programming model has increasing difficulty in exploiting the potential of current CPUs, which offer their best performance only through taking full advantage of multiple cores and wide vector registers. Future CPU evolution will intensify this trend, with core counts increasing and memory per core falling. Maximising performance per watt will be a key metric, so all of these cores must be used as efficiently as possible. In order to address the deficiencies of the current framework, ATLAS has embarked upon two projects: first, a practical demonstration of the use of multi-threading in our reconstruction software, using the GaudiHive framework; second, an exercise to gather requirements for an updated framework, going back to the first principles of how event processing occurs. In this paper we report on both these aspects of our work. For the hive based demonstrators, we discuss what changes were necessary in order to allow the serially designed ATLAS code to run, both to the framework and to the tools and algorithms used. We report on what general lessons were learned about the code patterns that had been employed in the software and which patterns were identified as particularly problematic for multi-threading. These lessons were fed into our considerations of a new framework and we present preliminary conclusions on this work. In particular we identify areas where the framework can be simplified in order to aid the implementation of a concurrent event processing scheme. Finally, we discuss the practical difficulties involved in migrating a large established code base to a multi-threaded framework and how this can be achieved for LHC Run 3.

Modern software applications rarely live in isolation and nowadays it is common practice to rely on services or consume information provided by remote entities. In such a distributed architecture, integration is key. Messaging, for more than a decade, is the reference solution to tackle challenges of a distributed nature, such as network unreliability, strong-coupling of producers and consumers and the heterogeneity of applications. Thanks to a strong community and a common effort towards standards and consolidation, message brokers are today the transport layer building blocks in many projects and services, both within the physics community and outside. Moreover, in recent years, a new generation of messaging services has appeared, with a focus on low-latency and high-performance use cases, pushing the boundaries of messaging applications. This paper will present messaging solutions for distributed applications going through an overview of the main concepts, technologies and services.

Distributed file systems provide a fundamental abstraction to location-transparent, permanent storage. They allow distributed processes to co-operate on hierarchically organized data beyond the life-time of each individual process. The great power of the file system interface lies in the fact that applications do not need to be modified in order to use distributed storage. On the other hand, the general and simple file system interface makes it notoriously difficult for a distributed file system to perform well under a variety of different workloads. This has lead to today's landscape with a number of popular distributed file systems, each tailored to a specific use case. Early distributed file systems merely execute file system calls on a remote server, which limits scalability and resilience to failures. Such limitations have been greatly reduced by modern techniques such as distributed hash tables, content-addressable storage, distributed consensus algorithms, or erasure codes. In the light of upcoming scientific data volumes at the exabyte scale, two trends are emerging. First, the previously monolithic design of distributed file systems is decomposed into services that independently provide a hierarchical namespace, data access, and distributed coordination. Secondly, the segregation of storage and computing resources yields to a storage architecture in which every compute node also participates in providing persistent storage.

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS and ALICE are the largest collaborations ever assembled in the sciences and are at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, both experiments rely on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System (WMS) for managing the workflow for all data processing on hundreds of data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. The scale is demonstrated by the following numbers: PanDA manages O(10²) sites, O(10⁵) cores, O(10⁸) jobs per year, O(10³) users, and ATLAS data volume is O(10¹⁷) bytes. In 2013 we started an ambitious program to expand PanDA to all available computing resources, including opportunistic use of commercial and academic clouds and Leadership Computing Facilities (LCF). The project titled 'Next Generation Workload Management and Analysis System for Big Data' (BigPanDA) is funded by DOE ASCR and HEP. Extending PanDA to clouds and LCF presents new challenges in managing heterogeneity and supporting workflow. The BigPanDA project is underway to setup and tailor PanDA at the Oak Ridge Leadership Computing Facility (OLCF) and at the National Research Center "Kurchatov Institute" together with ALICE distributed computing and ORNL computing professionals. Our approach to integration of HPC platforms at the OLCF and elsewhere is to reuse, as much as possible, existing components of the PanDA system. We will present our current accomplishments with running the PanDA WMS at OLCF and other supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications.

The CMS experiment at the CERN LHC has been designed with a 2-level trigger system. The Level 1 Trigger (L1) is implemented on custom-designed electronics. The High Level Trigger (HLT) is a streamlined version of the CMS offline reconstruction software running on a computer farm. Being able to identify b-quark jets (b-tagging) at trigger level will play a crucial role during the Run II data taking to ensure the top quark, beyond the Standard Model and Higgs boson physics program of the experiment. It will help to significantly reduce the trigger output rate which will increase due to the higher instantaneous luminosity and higher cross sections at 13 TeV. B-tagging algorithms based on the identification of tracks displaced from the primary proton-proton collision and/or on the reconstruction of secondary vertices have been successfully used during Run I. We will present their design and performance with an emphasis on the dedicated aspects of track and primary vertex reconstruction, as well as the improvements foreseen to meet the challenges of the Run II data taking.

GENFIT is an experiment-independent track-fitting toolkit that combines fitting algorithms, track representations, and measurement geometries into a modular framework. We report on a significantly improved version of GENFIT, based on experience gained in the Belle II, ANDA, and FOPI experiments. Improvements concern the implementation of additional track-fitting algorithms, enhanced implementations of Kalman fitters, enhanced visualization capabilities, and additional implementations of measurement types suited for various kinds of tracking detectors. The data model has been revised, allowing for efficient track merging, smoothing, residual calculation, alignment, and storage.

The ATLAS Tile Calorimeter (TileCal) is the detector used in the reconstruction of hadrons, jets and missing transverse energy from the proton-proton collisions at the Large Hadron Collider (LHC). It covers the central part of the ATLAS detector (|η| < 1.6). The energy deposited by the particles is read out by approximately 5,000 cells, with double readout channels. The signal provided by the readout electronics for each channel is digitized at 40 MHz and its amplitude is estimated by an optimal filtering algorithm, which expects a single signal with a well-defined shape. However, the LHC luminosity is expected to increase leading to pile-up that deforms the signal of interest. Due to limited resources, the current hardware setup, which is based on Digital Signal Processors (DSP), does not allow the implementation of sophisticated energy estimation methods that deal with the pile-up. Therefore, the technique to be employed for online energy estimation in TileCal for next LHC operation period must be based on fast filters such as the Optimal Filter (OF) and the Matched Filter (MF). Both the OF and MF methods envisage the use of the background second order statistics in its design, more precisely the covariance matrix. However, the identity matrix has been used to describe this quantity. Although this approximation can be valid for low luminosity LHC, it leads to biased estimators under pile- up conditions. Since most of the TileCal cell present low occupancy, the pile-up, which is often modeled by a non-Gaussian distribution, can be seen as outlier events. Consequently, the classical covariance matrix estimation does not describe correctly the second order statistics of the background for the majority of the events, as this approach is very sensitive to outliers. As a result, the OF (or MF) coefficients are miscalculated leading to a larger variance and biased energy estimator. This work evaluates the usage of a robust covariance estimator, namely the Minimum Covariance Determinant (MCD) algorithm, to be applied in the OF design. The goal of the MCD estimator is to find a number of observations whose classical covariance matrix has the lowest determinant. Hence, this procedure avoids taking into account low likelihood events to describe the background. It is worth mentioning that the background covariance matrix as well as the OF coefficients for each TileCal channel are computed offline and stored for both online and offline use. In order to evaluate the impact of the MCD estimator on the performance of the OF, simulated data sets were used. Different average numbers of interactions per bunch crossing and bunch spacings were tested. The results show that the estimation of the background covariance matrix through MCD improves significantly the final energy resolution with respect to the identity matrix which is currently used. Particularly, for high occupancy cells, the final energy resolution is improved by more than 20%. Moreover, the use of the classical covariance matrix degrades the energy resolution for the majority of TileCal cells.

The Tile Calorimeter (TileCal) is the central section of the hadronic calorimeter of ATLAS experiment of the Large Hadron Collider (LHC) and has about 10,000 eletronic channels. An Optimal Filter (OF) has been used to estimate the energy sampled by the calorimeter and applies a Quality Factor (QF) for signal acceptance. An approach using Matched Filter (MF) has also been pursued. In order to cope with the luminosity rising foreseen for LHC operation upgrade, different algorithms have been developed. Currently, the OF measurement for signal acceptance is implemented through a chi-square test. At a low luminosity scenario, such QF measurement has been used as a way to describe how the acquired signal is compatible to the pulse shape pattern. However, at high-luminosity conditions, due to pile up, this QF acceptance is no longer possible when OF is employed, and the QF becomes a measurement to indicate whether the reconstructed signal suffers or not from pile up. Methods are being developed in order to recover the superimposed information, and the QF may be used again as signal acceptance criterion. In this work, a new QF measurement is introduced. It is based on divergence statistics, which measures the similarity of probability density functions.

Delphes is an open source C++ framework to perform the fast simulation of the response of a multipurpose detector. The simulation includes a tracking system, embedded into a magnetic field, calorimeters and a muon system. The framework is interfaced to standard file formats and outputs observables such as isolated leptons, missing transverse energy and collection of jets that can be used for dedicated analyses. The simulation of the detector response takes into account the effect of magnetic field, the granularity of the calorimeters and subdetector resolutions. The program contains parmetrizations for the CMS and ATLAS detectors, based on published performances. Basic parametrizations for the LHCb and FCC detectors are also available. The Delphes framework also includes a simple event display.

Several new features are discussed, such as an emulation of the particle-flow algorithm, pile- up simulation, N-subjettiness and a simple b-tagging algorithm based on counting tracks with large impact parameter.

Clustering analysis is one of multivariate data analysis techniques which allows to gather statistical data units into groups, in order to minimize the logical distance within each group and to maximize the one between different groups. In these proceedings, the authors present a novel approach to the muontomography data analysis based on clustering algorithms. As a case study we present the Muon Portal project that aims to build and operate a dedicated particle detector for the inspection of harbor containers to hinder the smuggling of nuclear materials. Clustering techniques, working directly on scattering points, help to detect the presence of suspicious items inside the container, acting, as it will be shown, as a filter for a preliminary analysis of the data.

After a hugely successful first run, the Large Hadron Collider (LHC) is currently in a shut-down period, during which essential maintenance and upgrades are being performed on the accelerator. The ATLAS experiment, one of the four large LHC experiments has also used this period for consolidation and further developments of the detector and of its software framework, ahead of the new challenges that will be brought by the increased centre-of-mass energy and instantaneous luminosity in the next run period. This is of particular relevance for the ATLAS Tracking software, responsible for reconstructing the trajectory of charged particles through the detector, which faces a steep increase in CPU consumption due to the additional combinatorics of the high-multiplicity environment. The steps taken to mitigate this increase and stay within the available computing resources while maintaining the excellent performance of the tracking software in terms of the information provided to the physics analyses will be presented. Particular focus will be given to changes to the Event Data Model, replacement of the maths library, and adoption of a new persistent output format. The resulting CPU profiling results will be discussed, as well as the performance of the algorithms for physics processes under the expected conditions for the next LHC run.

We present a software framework for Belle II that reconstructs B mesons in many decay modes with minimal user intervention. It does so by reconstructing particles in user-supplied decay channels, and then in turn using these reconstructed particles in higher-level decays. This hierarchical reconstruction allows one to cover a relatively high fraction of all B decays by specifying a limited number of particle decays. Multivariate classification methods are used to achieve a high signal-to-background ratio in each individual channel. The entire reconstruction, including the application of pre-cuts and classifier trainings, is automated to a high degree and will allow users to retrain to account for analysis-specific signal-side selections.

We present a software framework for statistical data analysis, called HistFitter, that has extensively been used in the ATLAS Collaboration to analyze data of proton-proton collisions produced by the Large Hadron Collider at CERN. Most notably, HistFitter has become a de-facto standard in searches for supersymmetric particles since 2012, with some usage for Exotic and Higgs boson physics. HistFitter coherently combines several statistics tools in a programmable and flexible framework that is capable of bookkeeping hundreds of data models under study using thousands of generated input histograms. The key innovations of HistFitter are to weave the concepts of control, validation and signal regions into its very fabric, and to treat them with rigorous statistical methods, while providing multiple tools to visualize and interpret the results through a simple configuration interface.

The CHIPS-TPT physics library for simulation of neutron-nuclear reactions on the new exclusive level is being developed in CFAR VNIIA. The exclusive modeling conserves energy, momentum and quantum numbers in each neutron-nuclear interaction. The CHIPS-TPT algorithms are based on the exclusive CHIPS library, which is compatible with Geant4. Special CHIPS-TPT physics lists in the Geant4 format are provided. The calculation time for an exclusive CHIPS-TPT simulation is comparable to the time of the corresponding Geant4- HP simulation. In addition to the reduction of the deposited energy fluctuations, which is a consequence of the energy conservation, the CHIPS-TPT libraries provide a possibility of simulation of the secondary particles correlation, e.g. secondary gammas, and of the Doppler broadening of gamma lines in the spectrum, which can be measured by germanium detectors.

The open source project HERAFitter was established to increase applicability of the QCD analysis in the hadron collider experiments. The framework may be used to extract parton density functions from a variety of experimental measurements and to assess the impact of different data on the parton density determination. It may also be employed to perform data consistency checks and to test theoretical models.

This short article covers a parallel session contribution of the ACAT2014 conference.

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D (r — ) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.

The high luminosity that will be reached in the new generation of High Energy Particle and Nuclear physics experiments implies large high background rate and large tracker occupancy, representing therefore a new challenge for particle tracking algorithms. For instance, at Jefferson Laboratory (JLab) (VA,USA), one of the most demanding experiment in this respect, performed with a 12 GeV electron beam, is characterized by a luminosity up to 10³⁹cm^-2s^-1. To this scope, Gaseous Electron Multiplier (GEM) based trackers are under development for a new spectrometer that will operate at these high rates in the Hall A of JLab. Within this context, we developed a new tracking algorithm, based on a multistep approach: (i) all hardware - time and charge - information are exploited to minimize the number of hits to associate; (ii) a dedicated Neural Network (NN) has been designed for a fast and efficient association of the hits measured by the GEM detector; (iii) the measurements of the associated hits are further improved in resolution through the application of Kalman filter and Rauch- Tung-Striebel smoother. The algorithm is shortly presented along with a discussion of the promising first results.

We present MadAnalysis 5, an analysis package dedicated to phenomenological studies of simulated collisions occurring in high-energy physics experiments. Within this framework, users are invited, through a user-friendly Python interpreter, to implement physics analyses in a very simple manner. A C++ code is then automatically generated, compiled and executed. Very recently, the expert mode of the program has been extended so that analyses with multiple signal/control regions can be handled. Additional observables have also been included, and an interface to several fast detector simulation packages has been developed, one of them being a tune of the Delphes 3 software. As a result, a recasting of existing ATLAS and CMS analyses can be achieved straightforwardly.

Differentiation is ubiquitous in high energy physics, for instance in minimization algorithms and statistical analysis, in detector alignment and calibration, and in theory. Automatic differentiation (AD) avoids well-known limitations in round-offs and speed, which symbolic and numerical differentiation suffer from, by transforming the source code of functions. We will present how AD can be used to compute the gradient of multi-variate functions and functor objects. We will explain approaches to implement an AD tool. We will show how LLVM, Clang and Cling (ROOT's C++11 interpreter) simplifies creation of such a tool. We describe how the tool could be integrated within any framework. We will demonstrate a simple proof-of-concept prototype, called Clad, which is able to generate n-th order derivatives of C++ functions and other language constructs. We also demonstrate how Clad can offload laborious computations from the CPU using OpenCL.

Over the past several years, the CMS experiment has made significant changes to its detector simulation application. The geometry has been generalized to include modifications being made to the CMS detector for 2015 operations, as well as model improvements to the simulation geometry of the current CMS detector and the implementation of a number of approved and possible future detector configurations. These include both completely new tracker and calorimetry systems. We have completed the transition to Geant4 version 10, we have made significant progress in reducing the CPU resources required to run our Geant4 simulation. These have been achieved through both technical improvements and through numerical techniques. Substantial speed improvements have been achieved without changing the physics validation benchmarks that the experiment uses to validate our simulation application for use in production. In this presentation, we will discuss the methods that we implemented and the corresponding demonstrated performance improvements deployed for our 2015 simulation application.

Power density constraints are limiting the performance improvements of modern CPUs. To address this, we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware.

Multivariate techniques using machine learning algorithms have become an integral part in many High Energy Physics data analyses. This article is intended to sketch how this development took place by pointing out a few analyses that pushed forward the exploitation of these powerful analysis techniques. This article does not focus on controversial issues like for example how systematic uncertainties can be dealt with when using such techniques, which have been widely discussed previously by other authors. The main purpose here is to point to the gain in physics reach of the physics experiments due to the adaptation of machine learning techniques and to the challenges the HEP community faces in the light a rapid development in the field of machine learning if we want to make successful use of these powerful selection and reconstruction techniques.

Modeling of detector response, modeling of physics, and software tools for modeling and analyzing are three fields among others that were discussed during 16^th International workshop on Advanced Computing and Analysis Techniques in physics research - ACAT 2014. This short report represents a summary of track two where the current status and progress in these fields were reported and discussed.

In this paper we focus on the main aspects of computer-aided calculations with tensors and present a new computer algebra system Redberry which was specifically designed for algebraic tensor manipulation. We touch upon distinctive features of tensor software in comparison with pure scalar systems, discuss the main approaches used to handle tensorial expressions and present the comparison of Redberry performance with other relevant tools.

We present the version 2.0 of the program package GoSAM, which is a public program package to compute one-loop QCD and/or electroweak corrections to multi-particle processes within and beyond the Standard Model. The extended version of the Binoth-Les- Houches-Accord interface to Monte Carlo programs is also implemented. This allows a large flexibility regarding the combination of the code with various Monte Carlo programs to produce fully differential NLO results, including the possibility of parton showering and hadronisation. We illustrate the wide range of applicability of the code by showing phenomenological results for multi-particle processes at NLO, both within and beyond the Standard Model.

SecDec is a program which can be used for the evaluation of parametric integrals, in particular multi-loop integrals. For a given set of propagators defining the graph, the program constructs the graph polynomials, factorizes the endpoint singularities, and finally produces a Laurent series in the dimensional regularization parameter, whose coefficients are evaluated numerically. In this talk we discuss various features of the program, which extend the range of applicability. We also present a recent phenomenological example of an application entering the momentum dependent two-loop corrections to neutral Higgs boson masses in the MSSM.

We describe the multi-purpose Monte-Carlo event generator WHIZARD for the simulation of high-energy particle physics experiments. Besides the presentation of the general features of the program like SM physics, BSM physics, and QCD effects, special emphasis will be given to the support of the most accurate simulation of the collider environments at hadron colliders and especially at future linear lepton colliders. On the more technical side, the very recent code refactoring towards a completely object-oriented software package to improve maintainability, flexibility and code development will be discussed. Finally, we present ongoing work and future plans regarding higher-order corrections, more general model support including the setup to search for new physics in vector boson scattering at the LHC, as well as several lines of performance improvements.

We outline here the motivation for the existence of analytic QCD models, i.e., QCD frameworks in which the running coupling A(Q²) has no Landau singularities. The analytic (holomorphic) coupling A(Q²) is the analog of the underlying pQCD coupling a(Q²) = α_s(Q²)/π, and any such A(Q²) defines an analytic QCD model. We present the general construction procedure for the couplings A_v (Q²) which are analytic analogs of the powers a(Q²)^v. Three analytic QCD models are presented. Applications of our program (in Mathematica) for calculation of A_v (Q²) in such models are presented. Programs in both Mathematica and Fortran can be downloaded from the web page: gcvetic.usm.cl.

Two programs, feyngen and feyncop, were developed. feyngen is designed to generate high loop order Feynman graphs for Yang-Mills, QED and ^k theories. feyncop can compute the coproduct of these graphs on the underlying Hopf algebra of Feynman graphs. The programs can be validated by exploiting zero dimensional field theory combinatorics and identities on the Hopf algebra which follow from the renormalizability of the theories. A benchmark for both programs was made.

Parallel features of the multidimensional numerical integration package Cuba are presented and achievable speed-ups discussed.

The Telescope Array surface detector stations record the temporal development of the signal from the extensive air shower front which carries information about the type of the primary particle. We develop the method to study the primary mass composition of the ultra-high-energy cosmic rays based on multivariate analysis (MVA). We report the preliminary mass composition results based on the Telescope array 5 years data.

We present the toolbox for analytical calculation of UV-counterterm of Feynman diagrams. It combines the power of R*'-operation with modern analytical methods. Written in pure Python our toolbox can be easily used and extended.

We present the Monte Carlo event generator LePaProGen for lepton pair production at hadron colliders. LePaProGen focuses on the description of higher-order electroweak radiative corrections. The generator is implementing a new algorithm for the selection of the optimal variables for phase space parametrization.

We introduce the Mellin-Barnes representation of general Feynman integrals and discuss their evaluation. The Mathematica package AMBRE has been recently extended in order to cover consistently non-planar Feynman integrals with two loops. Prospects for the near future are outlined. This write-up is an introduction to new results which have also been presented elsewhere.

We give numerical integration results for Feynman loop diagrams through 3-loop such as those covered by Laporta [1]. The methods are based on automatic adaptive integration, using iterated integration and extrapolation with programs from the QUADPACK package, or multivariate techniques from the ParInt package. The Dqags algorithm from QuadPack accommodates boundary singularities of fairly general types. PARINT is a package for multivariate integration layered over MPI (Message Passing Interface), which runs on clusters and incorporates advanced parallel/distributed techniques such as load balancing among processes that may be distributed over a network of nodes. Results are included for 3-loop self-energy diagrams without IR (infra-red) or UV (ultra-violet) singularities. A procedure based on iterated integration and extrapolation yields a novel method of numerical regularization for integrals with UV terms, and is applied to a set of 2-loop self-energy diagrams with UV singularities.

In this article we will discuss the basic calculational concepts to simulate particle physics events at high energy colliders. We will mainly focus on the physics in hadron colliders and particularly on the simulation of the perturbative parts, where we will in turn focus on the next-to-leading order QCD corrections.

The main theoretical tool to provide precise predictions for scattering cross sections of strongly interacting particles is perturbative QCD. Starting at next-to-leading order (NLO) the calculation suffers from unphysical IR-divergences that cancel in the final result. At NLO there exist general subtraction algorithms to treat these divergences during a calculation. Since the LHC demands for more precise theoretical predictions, general subtraction methods at next- to-next-to-leading order (NNLO) are needed.

These proceedings outline the four-dimensional formulation of the sector improved residue subtraction. The subtraction scheme STRIPPER and in particular its extension to arbitrary multiplicities is explained. Therefore, it furnishes a general framework for the calculation of NNLO cross sections in perturbative QCD.

A short review of recent renormalization group analyses of the self-consistence of the Standard Model is presented.

In this presentation some aspects of Next-to-Leading Order (NLO) calculations in QCD are presented. The focus is brought to aspects of such calculations for processes involving a high final-state particle multiplicity.

Solving the question of the origin of ultra-high energy cosmic rays (UHECRs) requires the development of detailed simulation tools in order to interpret the experimental data and draw conclusions on the UHECR universe. CRPropa is a public Monte Carlo code for the galactic and extragalactic propagation of cosmic ray nuclei above ~ 10¹⁷ eV, as well as their photon and neutrino secondaries. In this contribution the new algorithms and features of CRPropa 3, the next major release, are presented. CRPropa 3 introduces time-dependent scenarios to include cosmic evolution in the presence of cosmic ray deflections in magnetic fields. The usage of high resolution magnetic fields is facilitated by shared memory parallelism, modulated fields and fields with heterogeneous resolution. Galactic propagation is enabled through the implementation of galactic magnetic field models, as well as an efficient forward propagation technique through transformation matrices. To make use of the large Python ecosystem in astrophysics CRPropa 3 can be steered and extended in Python.

Cosmic-ray particles with ultra-high energies (above 10¹⁸ eV) are studied through the properties of extensive air showers which they initiate in the atmosphere. The Pierre Auger Observatory detects these showers with unprecedented exposure and precision and the collected data are processed via dedicated software codes. Monte Carlo simulations of extensive air showers are very computationally expensive, especially at the highest energies and calculations are performed on the GRID for this purpose. The processing of measured and simulated data is described, together with a brief list of physics results which have been achieved.

I present a brief review of the generalized Brodsky-Lepage-McKenzie (BLM) approaches to fix the scale-dependence of the renormalization group (RG) invariant quantities in QCD. At first, these approaches are based on the expansions of the coefficients of the perturbative series for the RG-invariant quantities in the products of the coefficients β_i of the QCD β-function, which are evaluated in the MS-like schemes. As a next step all β_i-dependent terms are absorbed into the BLM-type scale(s) of the powers of the QCD couplings. The difference between two existing formulations of the above mentioned generalizations based on the seBLM approach and the Principle of Maximal Conformality (PMC) are clarified in the case of the Bjorken polarized deep-inelastic scattering sum rule. Using the conformal symmetry-based relations for the non-singlet coefficient functions of the Adler D-function and of Bjorken polarized deep-inelastic scattering sum rules C^Bjp_NS (a_s) the β_i-dependent structure of the NNLO approximation for C^Bjp_NS (a_s) is predicted in QCD with n_gl-multiplet of gluino degrees of freedom, which appear in SUSY extension of QCD. The importance of performing the analytical calculation of the N³LO additional contributions of n_gl gluino multiplet to C^Bjp_NS (a_s) for checking the presented in the report NNLO prediction and for the studies of the possibility to determine the discussed {β}-expansion pattern of this sum rule at the O(a⁴_s)-level is emphasised.

The chiral SU(N) nonlinear sigma model represents one of the simplest case of effective field theory. For the last several decades it has played an extremely important role not only for the low energy phenomenology but also in many other areas of theoretical physics. In this talk we will focus on the tree-level scattering amplitudes of the n-Goldstone bosons. It will be shown that it can be reconstructed using the BCFW-like recursion relations. This method, which does not rely on the Lagrangian description, is much more efficient than the standard Feynman diagram techniques.

When running data intensive applications on distributed computational resources long I/O overheads may be observed as access to remotely stored data is performed. Latencies and bandwidth can become the major limiting factor for the overall computation performance and can reduce the CPU/WallTime ratio to excessive IO wait. Reusing the knowledge of our previous research, we propose a constraint programming based planner that schedules computational jobs and data placements (transfers) in a distributed environment in order to optimize resource utilization and reduce the overall processing completion time. The optimization is achieved by ensuring that none of the resources (network links, data storages and CPUs) are oversaturated at any moment of time and either (a) that the data is pre-placed at the site where the job runs or (b) that the jobs are scheduled where the data is already present. Such an approach eliminates the idle CPU cycles occurring when the job is waiting for the I/O from a remote site and would have wide application in the community. Our planner was evaluated and simulated based on data extracted from log files of batch and data management systems of the STAR experiment. The results of evaluation and estimation of performance improvements are discussed in this paper.