Search

Although the LHC experiments have been designed and prepared since 1984, the challenge of LHC computing was only tackled seriously much later, at the end of the '90s. This was the time at which the Grid paradigm wasemerging, and LHC computing had great hopes that most of its challenges would be solved by this new paradigm. The path to having functional and efficient distributed computing systems was in the end much more complex than anticipated. However, most obstacles were overcome, thanks to the introductionof new paradigms and a lot of manpower investment from the experiments and from the supporting IT units (for middleware development and infrastructuresetup). This contribution is briefly outlining some of the biggest hopes and disillusions of these past 20 years, and gives a brief outlook to the coming trends. [ABSTRACT FROM AUTHOR]

In LHC Run 3 the ALICE Computing Model will change. The Grid Tiers will to a large extend be specialised for a given role. 2/3 of the reconstruction and calibration will be done at the combined online and offline O2 compute facility, 1/3 will be done by the Tier1 centres in the Grid. Additionally all Tier1 centres, as already now, will take care of archiving one copy of the raw data on tape. The Tier2 centres will only do simulation. The AODs will will be collected on specialised Analysis Facilities which shall be capable of processing 10 PB of data within 24 hours. A prototype of such an Analysis Facility has been set up at GSI based on the experiences with the local ALICE Tier2 centre which has been in production since 2002. The main components are a general purpose HPC cluster with a mounted cluster file system enhanced by Grid components like an XRootD based Storage Element and an interface for being able to receive and run dedicated Grid jobs on the Analysis Facility prototype. The necessary I/O speed as well as easy local data access is facilitated by self developed XRootD PlugIns. Performance tests with real life ALICE analysis trains suggest that the target throughput rate can be achieved. [ABSTRACT FROM AUTHOR]

The CERN OpenStack cloud provides over 300,000 CPU cores to run data processing analyses for the Large Hadron Collider (LHC) experiments. To deliver these services, with high performance and reliable service levels, while at the same time ensuring a continuous high resource utilization has been one of the major challenges for the CERN cloud engineering team. Several optimizations like NUMA-aware scheduling and huge pages, have been deployed to improve scientific workloads performance, but the CERN Cloud team continues to explore new possibilities like preemptible instances and containers on bare-metal. In this paper we will dive into the concept and implementation challenges of preemptible instances and containers on bare-metal for scientific workloads. We will also explore how they can improve scientific workloads throughput and infrastructure resource utilization. We will present the ongoing collaboration with the Square Kilometer Array (SKA) community to develop the necessary upstream enhancement to further improve OpenStack Nova to support large-scale scientific workloads. [ABSTRACT FROM AUTHOR]

CERN's batch and grid services are mainly focused on High Throughput computing (HTC) for processing data from the Large Hadron Collider (LHC) and other experiments. However, part of the user community requires High Performance Computing (HPC) for massively parallel applications across many cores on MPI-enabled infrastructure. This contribution addresses the implementation of HPC infrastructure at CERN for Lattice QCD application development, as well as for different types of simulations for the accelerator and technology sector at CERN. Our approach has been to integrate the HPC facilities as far as possible with the HTC services in our data centre, and to take advantage of an agile infrastructure for updates, configuration and deployment. The HPC cluster has been orchestrated with the OpenStack Ironic component, and is hence managed with the same tools as the CERN internal OpenStack cloud. Experience and benchmarks of MPI applications across Infiniband with shared storage on CephFS is discussed, as well the setup of the SLURM scheduler for HPC jobs with a provision for backfill of HTC workloads. [ABSTRACT FROM AUTHOR]

An essential part of new physics searches at the Large Hadron Collider (LHC) at CERN involves event classification, or distinguishing potential signal events from those coming from background processes. Current machine learning techniques accomplish this using traditional hand-engineered features like particle 4-momenta, motivated by our understanding of particle decay phenomenology. While such techniques have proven useful for simple decays, they are highly dependent on our ability to model all aspects of the phenomenology and detector response. Meanwhile, powerful deep learning algorithms are capable of not only training on high-level features, but of performing feature extraction. In computer vision, convolutional neural networks have become the state-of-the-art for many applications. Motivated by their success, we apply deep learning algorithms to low-level detector data from the 2012 CMS Simulated Open Data to directly learn useful features, in what we call, end-to-end event classification. We demonstrate the power of this approach in the context of a physics search and offer solutions to some of the inherent challenges, such as image construction, image sparsity, combining multiple sub-detectors, and de-correlating the classifier from the search observable, among others. [ABSTRACT FROM AUTHOR]

The Physics programmes of LHC Run III and HL-LHC challenge the HEP community. The volume of data to be handled is unprecedented at every step of the data processing chain: analysis is no exception. Physicists must be provided with first-class analysis tools which are easy to use, exploit bleeding edge hardware technologies and allow to seamlessly express parallelism. This document discusses the declarative analysis engine of ROOT, RDataFrame, and gives details about how it allows to profitably exploit commodity hardware as well as high-end servers and manycore accelerators thanks to the synergy with the existing parallelised ROOT components. Real-life analyses of LHC experiments' data expressed in terms of RDataFrame are presented, highlighting the programming model provided to express them in a concise and powerful way. The recent developments which make RDataFrame a lightweight data processing framework are described, such as callbacks and I/O capabilities. Finally, the flexibility of RDataFrame and its ability to read data formats other than ROOT's are characterised, as an example it is discussed how RDataFrame can directly read and analyse LHCb's raw data format MDF. [ABSTRACT FROM AUTHOR]

The use of machine learning techniques for classification is well established. They are applied widely to improve the signal-to-noise ratio and the sensitivity of searches for new physics at colliders. In this study I explore the use of machine learning for optimizing the output of high precision experiments by selecting the most sensitive variables to the quantity being measured. The precise determination of the electroweak mixing angle at the Large Hadron Collider using linear or deep neural network regressors is developed as a test case. [ABSTRACT FROM AUTHOR]

One of the most important aspects of data processing at flavor physics experiments is the particle identification (PID) algorithm. In LHCb, several different sub-detector systems provide PID information: the Ring Imaging Cherenkov detectors, the hadronic and electromagnetic calorimeters, and the muon chambers. The charged PID based on the sub-detectors response is considered as a machine learning problem solved in different modes: one-vs-rest, one-vs-one and multi-classification, which affect the models training and prediction. To improve charged particle identification for pions, kaons, protons, muons and electrons, neural network and gradient boosting models have been tested. This paper presents these models and their performance evaluated on Run 2 data and simulation samples. A discussion of the performances is also presented. [ABSTRACT FROM AUTHOR]

Jet flavour identification is a fundamental component of the physics program of the LHC-based experiments. The presence of multiple flavours to be identified leads to a multiclass classification problem. We present results from a realistic simulation of the CMS detector, one of two multi-purpose detectors at the LHC, and the respective performance measured on data. Our tagger, named DeepJet, relies heavily on applying convolutions on lower level physics objects, like individual particles. This approach allows the usage of an unprecedented amount of information with respect to what is found in the literature. DeepJet stands out as the first proposal that can be applied to multi-classification for all jet flavours. We demonstrate significant improvements by the new approach on the classification capabilities of the CMS experiment in simulation in several of the tested classes. At high momentum improvements of nearly 90% less false positives at a standard operation point are reached. [ABSTRACT FROM AUTHOR]

Muons with high momentum - above 500 GeV - are an important constituent of new physics signatures in many models. Run-2 of the LHC is greatly increasing ATLAS's sensitivity to such signatures thanks to an ever-larger dataset of such particles, enhanced further by an increase in the center-of-mass energy. The ATLAS Muon Spectrometer chamber alignment contributes significantly to the uncertainty of the reconstruction of these high-momentum objects. The proper treatment of measurements during tracking and the correct propagation of the alignment effects is extremely challenging. Recently, an innovative approach that imposes Gaussian constraints on ensembles of detector hits was implemented. It provides a significant improvement to high-momentum tracking without increasing the CPU budget. Furthermore, it allows for the verification of the expected alignment quality using high-statistics collision data. A detailed discussion of the algorithmic realization is given, the expected performance gains are presented and prospects for further applications of the approach are outlined. [ABSTRACT FROM AUTHOR]

ATLAS Metadata Interface (AMI) is a generic ecosystem for metadata aggregation, transformation and cataloging. Benefiting from 18 years of feedback in the LHC context, the second major version was recently released. This paper describes the design choices and their benefits for providing high-level metadata-dedicated features. In particular, the Metadata Querying Language (MQL) - a domain-specific language allowing to query databases without knowing the relation between entities - and on the AMI Web framework are described. [ABSTRACT FROM AUTHOR]

Experiments at the Large Hadron Collider (LHC) produce tens of petabytes of new data in ROOT format per year that need to be processed and analysed. In the next decade, following the planned upgrades of the LHC and its detectors, this data production rate is expected to increase at least ten-fold. Therefore, optimizing the ROOT I/O subsystem is of critical importance to the success of the LHC physics programme. This contribution presents ROOT's approach for writing data from multiple threads to a single output file in an efficient way. Technical aspects of the implementation—the TBufferMerger class—and programming model examples are described. Measurements of runtime performance and the overall improvement relative to the case of serial data writing are also discussed. [ABSTRACT FROM AUTHOR]

Merlin++ is a C++ particle accelerator and particle tracking library originally developed at DESY for use in International Linear Collider (ILC) simulations. Merlin++ has more recently been adapted for High-Luminosity Large Hadron Collider (HL-LHC) collimation studies, utilizing advanced scattering physics. However, as is all too common in long-standing high-energy physics software, recent developments have focused on functional additions rather than code design and maintainability. This has resulted in usability issues for users and developers alike. The following presents recent improvements in adhering to modern software sustainability practices to address these issues. Quantifiable improvements in code complexity and maintainability are presented via appropriate test metrics and the evolution of the software architecture is analyzed. Experiences and conclusions of applying modern sustainability methodology to longstanding scientific software are discussed. [ABSTRACT FROM AUTHOR]

The ATLAS experiment is approaching mid-life: the long shutdown period (LS2) between LHC Runs 1 and 2 (ending in 2018) and the future collision data-taking of Runs 3 and 4 (starting in 2021). In advance of LS2, we have been assessing the future viability of existing computing infrastructure systems. This will permit changes to be implemented in time for Run 3. In systems with broad impact such as the conditions database, making assessments now is critical as the full chain of operations from online data-taking to offline processing can be considered: evaluating capacity at peak times, looking for bottlenecks, identifying areas of high maintenance, and considering where new technology may serve to do more with less. We have been considering changes to the ATLAS Conditions Database related storage and distribution infrastructure based on similar systems of other experiments. We have also examined how new technologies may help and how we might provide more RESTful services to clients. In this presentation, we give an overview of the identified constraints and considerations, and our conclusions for the best way forward: balancing preservation of critical elements of the existing system with the deployment of the new technology in areas where the existing system falls short. [ABSTRACT FROM AUTHOR]

The ATLAS Distributed Computing (ADC) Project is responsible for the off-line processing of data produced by the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. It facilitates data and workload management for ATLAS computing on the Worldwide LHC Computing Grid (WLCG). ADC Central Services operations (CSOPS) is a vital part of ADC, responsible for the deployment and configuration of services needed by ATLAS computing and operation of those services on CERN IT infrastructure, providing knowledge of CERN IT services to ATLAS service managers and developers, and supporting them in case of issues. Currently this entails the management of 43 different OpenStack projects, with more than 5000 cores allocated for these virtual machines, as well as overseeing the distribution of 29 petabytes of storage space in EOS for ATLAS. As the LHC begins to get ready for the next long shut-down, which will bring in many new upgrades to allow for more data to be captured by the on-line systems, CSOPS must not only continue to support the existing services, but plan ahead for the expected increase in data, users, and services that will be required. This paper attempts to explain the current state of CSOPS as well as the strategies in place to maintain the service functionality in the long term. [ABSTRACT FROM AUTHOR]

HammerCloud is a testing service and framework to commission, run continuous tests or on-demand large-scale stress tests, and benchmark computing resources and components of various distributed systems with realistic full-chain experiment workflows. HammerCloud, used by the ATLAS and CMS experiments in production, has been a useful service to commission both compute resources and various components of the complex distributed systems of the LHC experiments, as well as integral partof the monitoring suite that is essential for the computing operations of the experiments and their automation. In this contribution we review recent developments of the HammerCloud service that allow use of HammerCloud infrastructure to test Data Centre resources in the early phases of the infrastructure and services commissioning process. One of thebenefits we believe HammerCloud can provide is to be able to tune the commissioning of the new infrastructure, functional and also stress testing, as well as benchmarking with "standard candle" workflows, with experiment realistic workloads, that can be heavy for CPU, or I/O, or IOPS, or everything together. This extension of HammerCloud has been successfully usedin CERN IT during the prototype phase of the "BEER" Batch on EOS (Evaluation of Resources) project, and is being integrated with the continuous integration/continuous deployment suite for Batch service VMs. [ABSTRACT FROM AUTHOR]

ATLAS is one of the four experiments collecting data from the proton-proton collisions at the Large Hadron Collider. The offline processing and storage of the data is handled by a custom heterogenous distributed computing system. This paper summarizes some of the challenges and operations-driven solutions introduced in the system. [ABSTRACT FROM AUTHOR]

Since 2010 the Production and Distributed Analysis system (PanDA) for the ATLAS experiment at the Large Hadron Colliderhas seen big changes to accommodate new types of distributed computing resources: clouds, HPCs, volunteer computers and other external resources. While PanDA was originally designed for fairly homogeneous resources available through the Worldwide LHC Computing Grid, the new resources are heterogeneous, at diverse scales and with diverse interfaces. Up to a fifth of the resources available to ATLAS are of such new types and require special techniques for integration into PanDA. In this talk, we present the nature and scale of these resources. We provide an overview of the various challenges faced, spanning infrastructure, software distribution, workload requirements, scaling requirements, workflow management, data management, network provisioning, and associated software and computing facilities. We describe the strategies for integrating these heterogeneous resources into ATLAS, and the new software components being developed in PanDA to efficiently use them. Plans for software and computing evolution to meet the needs of LHC operations and upgrade in the long term future will be discussed. [ABSTRACT FROM AUTHOR]

The LHCb experiment will be upgraded for data taking in the LHC Run 3. The foreseen trigger output bandwidth trigger ofa few GB/s will result in data sets of tens of PB per year, which need to be efficiently streamed and stored offline for low-latency data analysis. In addition, simulation samples of up to two orders of magnitude larger than those currently simulated are envisaged, with big impact on offline computing and storage resources. This contribution discusses the offline computing model and the required offline resources for the LHCb upgrade, as resulting from the above requirements. [ABSTRACT FROM AUTHOR]

Federated identity management (FIM) is an arrangement that can be made among multiple organisations that lets subscribers use the same identification data to obtain access to the secured resources of all organisations in the group. In many research communities there is an increasing interest in a common approach to FIM as there is obviously a large potential for synergies. FIM4R [1] provides a forum for communities to share challenges and ideas, and to shape the future of FIM for our researchers. Current participation covers high energy physics, life sciences and humanities, to mention but a few. In 2012 FIM4R converged on a common vision for FIM, enumerated a set of requirements and proposed a number of recommendationsfor ensuring a roadmap for the uptake of FIM [2]. In summer 2018, FIM4R published an updated version of this paper [3]. The High Energy Physics (HEP) Community has been heavily involved in creating both the original white paper and the new version, which documented the progress made in FIM for Research, in addition to the current challenges. This paper presents the conclusions of this second FIM4R white paper and a summary of the identified requirements and recommendations. We focus particularly on the direction being taken by the Worldwide LHC Computing Grid (WLCG), through the WLCG Authorisation Working Group, and the requirements gathered from the HEP Community. [ABSTRACT FROM AUTHOR]

ALICE (A Large Ion Collider Experiment) is preparing for a major upgrade of the detector, readout and computing systemsfor LHC Run 3. A new facility called O2 (Online-Offline) will play a major role in data compression and event processing. To efficiently operate the experiment, we are designing a monitoring subsystem, which will provide a complete overview of the O2 overall health, detect performance degradation and component failures. The monitoring subsystem will receive and collect up to 600 kHz of performance metrics. It consists of a custom monitoring library and a server-side, distributed software covering five main functional tasks: parameter collection and processing, storage, visualisation and alarms. To select the most appropriate tools for these tasks, we evaluated three options: "Modular Stack", Zabbix and the currently used ALICE Grid monitoring tool called MonALISA. The former one consists of a toolkit including collectd, Apache Flume, Apache Spark, InfluxDB, Grafana and Riemann. This paper describes the monitoring subsystem functional architecture. It goes through a complete evaluation of the three considered options, the selection process, risk assessment and justification for the final decision. The in-depth comparison includes functional features and throughput measurement to ensure the required processing and storage performance. [ABSTRACT FROM AUTHOR]

PowerPC and high-performance computers (HPC) are important resources for computing in the ATLAS experiment. The future LHC data processing will require more resources than Grid computing, currently using approximately 100,000 cores at well over 100 sites, can provide. Supercomputers are extremely powerful as they utilize hundreds of thousands of CPUs joined together. However, their architectures have different instruction sets. ATLAS binary software distributions for x86 chipsets do not fit these architectures, as emulation of these chipsets results in huge performance loss. This paper describes the methodology of ATLAS software installation from source code on supercomputers. The installation procedure includes downloading the ATLAS simulation release code with 0.7 million C++ and Python lines as well as the source code of more than 50 external packages, such as ROOT and Geant4, followed by compilation, and rigorous unit and integration testing. The presentation reports the application of this procedure at Titan HPC and Summit PowerPC at Oak Ridge Computing Facility (OLCF). [ABSTRACT FROM AUTHOR]

The ALICE experiment will undergo extensive hardware and software upgrades for the LHC Run3. This translates in significant increase of the CPU and storage resources required for data processing, and at the same time the data access rates will grow linearly with the amount of resources. JAliEn (Java ALICE Environment) is the new Grid middleware designed to scale-out horizontally to fulfil the computing needs of the upgrade, and at the same time to modernize all parts of the distributed system software. This paper will present the architecture of the JAliEn framework, the technologies used and performance measurements. This work will also describe the next generation solution that will replace our main database backend, the AliEn File Catalogue. The catalogue is an integral part of the system, containing the metadata of all files written to the distributed Grid storage and also provides powerful search and data manipulation tools. As for JAliEn, the focus has been put onto horizontal scalability, with the aim to handle near exascale data volumes and order of magnitude more workload than the currently used Grid middleware. Lastly, this contribution will present how JAliEn manages the increased complexity of the tasks associated with the new ALICE data processing and analysis framework (ALFA) and multi-core environments. [ABSTRACT FROM AUTHOR]

Prediction for requirements for the LHC computing for Run 3 and for Run 4 (HL-LHC) over the course of the next 10 year, show a considerable gap between required and available resources, assuming budgets will globally remain flat at best. This will require some radical changes to the computing models for the data processing of the LHC experiments. The use of large scale computational resources at HPC centres worldwide is expected to increase substantially the cost-efficiency of the processing. In order to pave the path towards the HL-LHC data processing, the Swiss Institute of Particle Physics (CHIPP) has taken the strategic decision to migrate the processing of all the Tier-2 workloads for ATLAS and other LHC experiments from a dedicated x86 ̲ 64 cluster that has been in continuous operation and evolution since 2007, to Piz Daint, the current European flagship HPC, which ranks third in the TOP500 at the time of writing. We report on the technical challenges and solutions adopted to migrate to Piz Daint, and on the experience and measured performance for ATLAS in over one year of running in production. [ABSTRACT FROM AUTHOR]

The higher energy and luminosity from the LHC in Run 2 have put increased pressure on CMS computing resources. Extrapolating to even higher luminosities (and thus higher event complexities and trigger rates) beyond Run 3, it becomes clear that simply scaling up the the current model of CMS computing alone will become economically unfeasible. High Performance Computing (HPC) facilities, widely used in scientific computing outside of HEP, have the potential to help fill the gap. Here we describe the U.S.CMS efforts to integrate US HPC resources into CMS Computing via the HEPCloud project at Fermilab. We present advancements in our ability to use NERSC resources at scale and efforts to integrate other HPC sites as well. We present experience in the elastic use of HPC resources, quickly scaling up use when so required by CMS workflows. We also present performance studies of the CMS multi-threaded framework on both Haswell and KNL HPC resources. [ABSTRACT FROM AUTHOR]

The Production and Distributed Analysis (PanDA) system has been successfully used in the ATLAS experiment as a data-driven workload management system. The PanDA system has proven to be capable of operating at the Large Hadron Collider data processing scale over the last decade including the Run 1 and Run 2 data taking periods. PanDA was originally designed to be weakly coupled with the WLCG processing resources. Lately the system is revealing the difficulties to optimally integrate and exploit new resource types such as HPC and preemptible cloud resources with instant spin-up, and new workflows such as the event service, because their intrinsic nature and requirements are quite different from that of traditional grid resources. Therefore, a new component, Harvester, has been developed to mediate the control and information flow between PanDA and the resources, in order to enable more intelligent workload management and dynamic resource provisioning based on detailed knowledge of resource capabilities and their real-time state. Harvester has been designed around a modular structure to separate core functions and resource specific plugins, simplifying the operation with heterogeneous resources and providing a uniform monitoring view. This paper will give an overview of the Harvester architecture, current status with various resources, and future plans. [ABSTRACT FROM AUTHOR]

The modern security landscape for distributed computing in High Energy Physics (HEP) includes a wide range of threats employing different attack vectors. The nature of these threats is such that the most effective method for dealing with them is to work collaboratively, both within the HEP community and with partners further afield - these can, and should, include institutional and campus security teams. In parallel with this work, an appropriate technology stack is essential, incorporating current work on Big Data analytics. The work of the Worldwide LHC Computing Grid (WLCG) Security Operations Center (SOC) Working Group (WG) [1] is to pursue these goals to form a reference design (or guidelines) for WLCG sites of different types. The strategy of the group is to identify necessary components - starting with threat intelligence (MISP [2]) and network intrusion detection (Bro [3]), building a working model over time. We present on the progress of the working group thus far, in particular on the programme of workshops now underway. These workshops give an opportunity to engage with sites to allow the development of advice and procedures for deployment, as well as facilitating wider discussions on how to best work with trust groups at different levels. These trust groups vary in scope but can include institutes, National Grid Infrastructures and the WLCG as a whole. [ABSTRACT FROM AUTHOR]

This paper reviews the prototype dataset accounting developed during the EGI-Engage project and how it could be used to complement the view that the WLCG has of its datasets. This is a new feature of the EGI resource accounting system that will enable storing information on dataset usage such as who has accessed a dataset and how often. The new REST interface used for retrieving usage metrics from the EGI DataHub is described as well as further work that is required. [ABSTRACT FROM AUTHOR]

PanDA (Production and Distributed Analysis) is the workload management system for ATLAS across the Worldwide LHC Computing Grid. While analysis tasks are submitted to PanDA by over a thousand users following personal schedules (e.g. PhD or conference deadlines), production campaigns are scheduled by a central Physics Coordination group based on the organization's calendar. The Physics Coordination group needs to allocate the amount of Grid resources dedicated to each activity, in order to manage sharing of CPU resources among various parallel campaigns and to make sure that results can be achieved in time for important deadlines. While dynamic and static shares on batch systems have been around for a long time, we are trying to move away from local resource partitioning and manage shares at a global level in the PanDA system. The global solution is not straightforward, given different requirements of the activities (number of cores, memory, I/O and CPU intensity), the heterogeneity of Grid resources (site/HW capabilities, batch configuration and queue setup) and constraints on data locality. We have therefore started the Global Shares project that follows a requirements-driven multi-step execution plan, starting from definition of nestable shares, implementing share-aware job dispatch, aligning internal processes with global shares and finally implementing a pilot stream control for controlling the batch slots that keeps late binding. This contribution will explain the development work and architectural changes in PanDA to implement Global Shares, and describe how the Global Shares project has enabled the central control of resources and significantly reduced manual operations. [ABSTRACT FROM AUTHOR]

The DIRAC project is developing interware to build and operate distributed computing systems. It provides a development framework and a rich set of services for both Workload and Data Management tasks of large scientific communities. DIRAC is adopted by a growing number of collaborations, including LHCb, Belle2, CLIC, and CTA. The LHCb experiment will be upgraded during the second long LHC shutdown (2019-2020). At restart of data taking in Run 3, the instantaneous luminosity will increase by a factor of five. The LHCb computing model also need be upgraded. Oversimplifying, this translates into the need for significantly more computing power and resources, and more storage with respect to what LHCb uses right now. The DIRAC interware will keep being the tool to handle all of LHCb distributed computing resources. Within this contribution, we highlight the ongoing and planned efforts to ensure that DIRAC will be able to provide an optimal usage of its distributed computing resources. This contribution focuses on DIRAC plans for increasing the scalability of the overall system, taking in consideration that the main requirement is keeping a running system working. This requirement translates into the need of studies and developments within the current DIRAC architecture. We believe that scalability is about traffic growth, dataset growth, and maintainability: within this contribution we address all of them, showing the technical solutions we are adopting. [ABSTRACT FROM AUTHOR]

Monte-Carlo simulation is a fundamental tool for high-energy physics experiments, from the design phase to data analysis. In recent years its relevance has increased due to the ever growing measurements precision. Accuracy and reliability are essential features in simulation and particularly important in the current phase of the LHCb experiment, where physics analysis and preparation for data taking with the upgraded detector need to be performed at the same time. In this paper we will give an overview of the full chain of tests and procedures implemented for the LHCb Simulation software stack to ensure the quality of its results. The tests comprise simple checks to validate new software contributions in a nightlies system as well as more elaborate checks to probe simple physics and software quantities for performance and regression verifications. Commissioning of a new major version of the simulation software for production implies also validating its impact using a few physics anlayses. A new system for Simulation Data Quality (SimDQ) that is being put in place to help in the first phase of commissioning and for fast verification of all samples produced is also discussed. [ABSTRACT FROM AUTHOR]

Since the beginning of the WLCG Project the Spanish ATLAS computing centers have participated with reliable and stable resources as well as personnel for the ATLAS Collaboration. Our contribution to the ATLAS Tier2s and Tier1s computing resources (disk and CPUs) in the last 10 years has been around 4-5%. In 2016 an international advisory committee recommended to revise our contribution according to the participation in the ATLAS experiment. With this scenario, we are optimizing the federation of three sites located in Barcelona, Madrid and Valencia, considering that the ATLAS collaboration has developed workflows and tools to flexibly use all the resources available to the collaboration, where the tiered structure is somehow vanishing. In this contribution, we would like to show the evolution and technical updates in the ATLAS Spanish Federated Tier2 and Tier1. Some developments we are involved in, like the Event Index project, as well as the use of opportunistic resources will be useful to reach our goal. We discuss the foreseen/proposed scenario towards a sustainable computing environment for the Spanish ATLAS community in the HL-LHC period. [ABSTRACT FROM AUTHOR]

Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We describe an R&D activity aimed at providing a configurable tool capable of training a neural network to reproduce the detector response and speed-up standard Monte Carlo simulation. This represents a generic approach in the sense that such a network could be designed and trained to simulate any kind of detector and, eventually, the whole data processing chain in order to get, directly in one step, the final reconstructed quantities, in just a small fraction of time. We present the first application of three-dimensional convolutional Generative Adversarial Networks to the simulation of high granularity electromagnetic calorimeters. We describe detailed validation studies comparing our results to Geant4 Monte Carlo simulation. Finally we show how this tool could be generalized to describe a whole class of calorimeters, opening the way to a generic machine learning based fast simulation approach. [ABSTRACT FROM AUTHOR]

The LHC experiments produce many tens of petabytes of data each year, which must be stored, processed and analyzed. The CPU and storage resources required for these activities are planned for and requested on yearly basis. To make a better resource planning, a more fine-grained time prediction for the resource utilization is necessary, taking into account the LHC running conditions and schedule, the experiment-specific data management policies, scientific conferences demands and other criteria. To that end, we developed a flexible and highly configurable simulation tool, which performs a Discrete Event Simulation of the ALICE data flow processes. The tool consists of a Web GUI, through which the necessary parameters are entered and the results of the simulations are graphically visualized. In this paper, we describe the design of the simulation tool and present the preliminary results. [ABSTRACT FROM AUTHOR]

LHC@home has provided computing capacity for simulations under BOINC since 2005. Following the introduction of virtualisation with BOINC to run HEP Linux software in a virtual machine on volunteer desktops, initially started on test BOINC projects, like Test4Theory and ATLAS@home, all CERN applications distributed to volunteers have been consolidated under a single LHC@home BOINC project. As part of an effort to unite CERN's batch, cloud, grid and volunteer computing efforts, the BOINC service has been integrated with the other compute services at CERN, notably HTCondor, in terms job submission and accounting. The paper will also address contributions to improve the BOINC software and community effort to evolve BOINC for a sustainable volunteer computing environment. Furthermore, we discuss future challenges to reduce the effort required by volunteers to run virtual machines for experiment simulations and improvements to BOINC to widen the appeal of volunteer computing. [ABSTRACT FROM AUTHOR]

The volunteer computing project ATLAS@Home has been providing a stable computing resource for the ATLAS experiment since 2013. It has recently undergone some significant developments and as a result has become one of the largest resources contributing to ATLAS computing, by expanding its scope beyond traditional volunteers and into exploitation of idle computing power in ATLAS data centres. Removing the need for virtualization on Linux and instead using container technology has made the entry barrier significantly lower for data centre participation and in this paper, we describe the implementation and results of this change. We also present other recent changes and improvements in the project. In early 2017 the ATLAS@Home project was merged into a combined LHC@Home platform, providing a unified gateway to all CERN-related volunteer computing projects. The ATLAS Event Service shifts data processing from file-level to event-level and we describe how ATLAS@Home was incorporated into this new paradigm. [ABSTRACT FROM AUTHOR]

The CERN ATLAS experiment successfully uses a worldwide computing infrastructure to support the physics program during LHC Run 2. The Grid workflow system PanDA routinely manages 250 to 500 thousand concurrently running production and analysis jobs to process simulation and detector data. In total more than 370 PB of data is distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing LHC luminosity in future runs new developments are underway to even more efficiently use opportunistic resources such as HPCs and utilize new technologies. This paper will review and explain the outline and the performance of the ATLAS distributed computing system and give an outlook to new workflow and data management ideas for the beginning of the LHC Run 3. It will be discussed that the ATLAS workflow and data management systems are robust, performant and can easily cope with the higher Run 2 LHC performance. There are presently no scaling issues and each subsystem is able to sustain the large loads. [ABSTRACT FROM AUTHOR]

Hundreds of physicists analyze data collected by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider using the CMS Remote Analysis Builder and the CMS global pool to exploit the resources of the Worldwide LHC Computing Grid. Efficient use of such an extensive and expensive resource is crucial. At the same time, the CMS collaboration is committed to minimizing time to insight for every scientist, by pushing for fewer possible access restrictions to the full data sample and supports the free choice of applications to run on the computing resources. Supporting such variety of workflows while preserving efficient resource usage poses special challenges. In this paper we report on three complementary approaches adopted in CMS to improve the scheduling efficiency of user analysis jobs: automatic job splitting, automated run time estimates and automated site selection for jobs. [ABSTRACT FROM AUTHOR]

The Worldwide LHC Computing Grid (WLCG) is an innovative distributed environment which is deployed through the use of grid computing technologiesin order to provide computing and storage resources to the LHC experimentsfor data processing and physics analysis. Following increasing demands of LHC computing needs toward high luminosity era, the experiments are engagdin an ambitious program to extend the capability of WLCG distributed environment, for instance including opportunistically used resources such as High-Performance Computers (HPCs), cloud platforms and volunteer computer. norder to be effectively used by the LHC experiments, all these diverse distributed resources should be described in detail. This implies easy service discovery of shared physical resources, detailed description of service configurations and experiment-specific data structures is needed. In this contribution, we present a high-level information component of a distributed computing environment, the Computing Resource Information Catalogue (CRIC) which aims to facilitate distributed computing operations for the LHC experiments and consolidate WLCG topology information. In addition, CRIC performs data validation and provides coherent view and topology descriptinto the LHC VOs for service discovery and configuration. CRIC represents teevolution of ATLAS Grid Information System (AGIS) into the common experiment independent high-level information framework. CRIC's mission is to serve not just ATLAS Collaboration needs for the description of the distributed environment but any other virtual organization relying on large scale distributed infrastructure as well as the WLCG on the global scope. The contribution describes CRIC architecture, implementation of data model,collectors, user interfaces, advanced authentication and access control components of the system. [ABSTRACT FROM AUTHOR]

The INFN scientific computing infrastructure is composed of more than 30 sites, ranging from CNAF (Tier-1 for LHC and main data center for nearly 30 other experiments) and nine LHC Tier-2s, to ∼ 20 smaller sites, including LHC Tier-3s and not-LHC experiment farms. A comprehensive review of the installed resources, together with plans for the near future, has been collected during the second half of 2017, and provides a general view of the infrastructure, its costs and its potential for expansions; it also shows the general trends in software and hardware solutions utilized in a complex reality as INFN. As of the end of 2017, the total installed CPU power exceeded 800 kHS06 (∼ 80,000 cores) while the total storage net capacity was over 57 PB on disk and 97 PB on tape: the vast majority of resources (95% of cores and 95% of storage) are concentrated in the 16 largest centers. Future evolutions are explored and are towards the consolidation into big centers; this has required a rethinking of the access policies and protocols in order to enable diverse scientific communities, beyond LHC, to fruitfully exploit the INFN resources. On top of that, such an infrastructure will be used beyond INFN experiments, and will be part of the Italian infrastructure, comprising other research institutes, universities and HPC centers. [ABSTRACT FROM AUTHOR]

The high-luminosity data produced by the LHC leads to many proton-proton interactions per beam crossing in ATLAS, known as pile-up. In order to understand the ATLAS data and extract physics results it is important to model these effects accurately in the simulation. As the pile-up rate continues to grow towards an eventual rate of 200 for the HL-LHC, this puts increasing demands on the computing resources required for the simulation and the current approach of simulating the pile-up interactions along with the hard-scatter for each Monte Carlo production is no longer feasible. The new ATLAS "overlay" approach to pile-up simulation is presented. Here a pre-combined set of minimum bias interactions, either from simulation or from real data, is created once and a single event drawn from this set is overlaid with the hard-scatter event being simulated. This leads to significant improvements in CPU time. This contribution will discuss the technical aspects of the implementation in the ATLAS simulation and production infrastructure and compare the performance, both in terms of computing and physics, to the previous approach. [ABSTRACT FROM AUTHOR]

ATLAS is embarking on a project to multithread its reconstruction software in time for use in Run 3 of the LHC. One component that must be migrated is the histogramming infrastructure used for data quality monitoring of the reconstructed data. This poses unique challenges due to its large memory footprint which forms a bottleneck for parallelization and the need to accommodate relatively inexperienced developers. We discuss plans for the upgraded framework. [ABSTRACT FROM AUTHOR]

The Trigger and DAQ (TDAQ) system of the ATLAS experiment is a complex distributed computing system, composed of O(10, 000) of applications running on more than 2,500 computers. The system is operated by a crew of operators on shift. An important aspect of operations is to minimize the downtime of the system caused by runtime failures, such as human errors, unawareness or miscommunication. The paper describes recent developments in one of the intelligent TDAQ frameworks, the Shifter Assistant (SA) and summarizes the experience of it's use in operations of ATLAS during LHC Run 2. SA is a framework whose main aim is to automatize routine system checks, error detection and diagnosis, events correlations etc. in order to help the operators react to runtime problems promptly and effectively. The tool is based on CEP (Complex Event Processing) technology. It constantly processes this stream of operational events (O(100 kHz)) over a set of "directives" (or rules) in the knowledge base, producing human-oriented alerts and making shifters aware of the operational issues. More than 200 directives were developed by TDAQ and ATLAS detector experts for different domains. In this paper we also describe different types of directives which were developed in course of Run 2, and present few examples of most interesting and challenging ones, demonstrating the power of CEP for this type of applications. [ABSTRACT FROM AUTHOR]

In HEP experiments the CPU resources required by Monte Carlo simulations are taking up an ever larger fraction of the total computing power (greater than 75%). At the same time the pace of performance improvements given by technology is slowing down, so the only solution is a more efficient use of resources. In this context we present the ongoing development of a fast simulation of the LHCb calorimeter based on a point library, which has significant advantages in terms of library size and output accuracy compared to the cell hit library. Preliminary performance studies are presented. [ABSTRACT FROM AUTHOR]

The CMS full simulation using Geant4 has delivered billions of simulated events for analysis during Runs 1 and 2 of the LHC. However, the HL-LHC dataset will be an order of magnitude larger, with a similar increase in occupancy per event. In addition, the upgraded CMS detector will be considerably more complex, with an extended silicon tracker and a high granularity calorimeter in the endcap region. Increases in conventional computing resources are subject to both technological and budgetary limitations, so novel approaches are needed to improve software efficiency and to take advantage of new architectures and heterogeneous resources. Several projects are in development to address these needs, including the vectorized geometry library Vec-Geom and the GeantV transport engine, which uses track-level parallelization. The current computing performance of the CMS simulation will be presented as a baseline, along with an overview of the various optimizations already available for Geant4. Finally, the progress and outlook for integrating VecGeom and GeantV in the CMS software framework will be discussed. [ABSTRACT FROM AUTHOR]

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL-LHC) needs, so the experiments are in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 orders of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a sufficient amount of simulated data needed by the next HL-LHC experiments using limited computing resources. [ABSTRACT FROM AUTHOR]

Faster alternatives to a detailed, GEANT4-based simulation are being pursued within the LHCb experiment. In this context the integration of the Delphes toolkit in the LHCb simulation framework is intended to provide a fully parameterized option. Delphes is a modular software designed for general-purpose experiments such as ATLAS and CMS to quickly propagate stable particles using a parametric approach and to provide reconstructed physical objects as output. It includes propagation inside a magnetic field and parameterized response for tracking and muon systems as well as calorimeters. Particle energies are computed by smearing the initial visible particles momentaaccording to detector resolution. The present contribution illustrates the current status of the Delphes toolkit in Gauss, the LHCb simulation framework. In this integration the particle transport performed by GEANT4 and subsequent mimicking of detector response and reconstruction has been replaced with a parametric response of the various detector elements. The implementation required sig-nificant changes to Delphes itself to constrain the particle transport inside the detector acceptance and to match the LHCb dipole magnetic field. The configuration of various parametrisations of resolution and efficiency is also a major aspect of the work to provide a fully functional simulation. The output of the resulting fast simulation is formatted in such a way that can be used in the LHCb physics analysis framework. [ABSTRACT FROM AUTHOR]

The LHCb experiment has been operating in various beam configurations in Run 1 and 2 of the LHC, with collisions of lead ion beams or in a fixed-target setup. In order to analyse these data, the Gauss simulation software has been extended to be able to generate events describing these configurations, based mainly on the EPOS event generator. These proceedings give details about the methods employed. [ABSTRACT FROM AUTHOR]

The LHCb experiment uses a custom made C++ detector and geometry description toolkit, integrated with the Gaudi framework, designed when the LHCb software was first implemented. With the LHCb upgrade scheduled for 2021, it is necessary for the experiment to review this choice and adapt to the evolution of software and computing (in terms of e.g multi-threading support or vectorization) The Detector Description Toolkit for High Energy Physics (DD4hep) is a good candidate for the replacement for LHCb's geometry description framework: it is possible to integrate it with the LHCb core software framework and its features theoretically match the requirements: in terms of geometry and detector description but also concerning the possibility to add detector alignment parameters and the integration with simulation tools. In this paper we report on detailed studies undertaken to compare the feature set proposed by the DD4hep toolkit, to what is needed by LHCb. We show not only how the main description could be migrated, but also how to integrate the LHCb real-time alignment tools in this toolkit, in order to identify the main obstacles to the migration of the experiment to DD4hep. [ABSTRACT FROM AUTHOR]

The study of the different backgrounds which the muon detectors of the CMS experiment are exposed to, is fundamental to assess the system longevity and project its performance to the conditions expected for HL-LHC. In this respect, an accurate modelling of the backgrounds in simulation is of prime importance as many studies rely on simulation-based predictions while these future conditions have never been experienced in reality. This paper will present the state of the art of the work carried out to understand backgrounds observed with data collected during the LHC runs, as well as at the CERN high-intensity gamma irradiation facility. Furthermore, the effort made to improve the accuracy of FLUKA and GEANT4 based simulations of background will be thoroughly described. [ABSTRACT FROM AUTHOR]