bluuebunny/crossref_metadata_2025 huggingface dataset:
Tile-in-One: an integrated system for data quality and condition assessment for the ATLAS Tile Calorimeter
Authors: Smirnov Yuri , Smieško Juraj
Published: November 2020

The Tile Calorimeter (TileCal) is a crucial part of the ATLAS detector, which jointly with other calorimeters reconstructs hadrons, jets, tau-particles, missing transverse energy and assists in muon identification. It consists of alternating steel absorber layers and active scintillating tiles and covers the region |η| < 1.7. The TileCal is regularly monitored by several systems, which were developed mainly during the commissioning of the detector in order to meet distinct requirements. Any problem is reported and immediately investigated, which results in data quality efficiency very close to 100%, as achieved over the last few years. Although the TileCal tools are maintained, the underlying technologies are becoming gradually outdated.

The Tile-in-One web platform strives to integrate all data quality and condition assessment TileCal tools into one common system. This system is implemented as a web application with the main machine being the gateway for so-called plugins. These are standalone small web application hosted on a single virtual machine. The plugins are separated into virtual machines due to the requirement of different data sources and to avoid interference in order to increase the stability of the platform. The main server is responsible for the authentication and authorization of the users, as well as the management of the plugins. Currently the platform consists of 13 plugins in various stages of development. The implementation details of the Tile-in-One web system and selected plugins are presented in this document.
Highly Performant, Deep Neural Networks with sub-microsecond latency on FPGAs for Trigger Applications
Authors: Nottbeck Noel , Schmitt Christian , Büscher Volker
Published: November 2020

Artificial neural networks are becoming a standard tool for data analysis, but their potential remains yet to be widely used for hardware-level trigger applications. Nowadays, high-end FPGAs, often used in low-level hardware triggers, offer theoretically enough performance to include networks of considerable size. This makes it very promising and rewarding to optimize a neural network implementation for FPGAs in the trigger context.

Here an optimized neural network implementation framework is presented, which typically reaches 90 to 100% computational efficiency, requires few extra FPGA resources for data flow and controlling, and allows latencies in the order of 10s to few 100s of nanoseconds for entire (deep) networks.
The CMS Trigger Upgrade for the HL-LHC
Authors: Fernandez Perez Tomei Thiago Rafael
Published: November 2020

The CMS experiment has been designed with a two-level trigger system: the Level-1 Trigger, implemented on custom-designed electronics, and the High Level Trigger, a streamlined version of the CMS offline reconstruction software running on a computer farm. During its second phase the LHC will reach a luminosity of 7.5 1034 cm−2 s−1 with a pileup of 200 collisions, producing integrated luminosity greater than 3000 fb−1 over the full experimental run. To fully exploit the higher luminosity, the CMS experiment will introduce a more advanced Level-1 Trigger and increase the full readout rate from 100 kHz to 750 kHz. CMS is designing an efficient data-processing hardware trigger that will include tracking information and high-granularity calorimeter information. The current Level-1 conceptual design is expected to take full advantage of advances in FPGA and link technologies over the coming years, providing a high-performance, low-latency system for large throughput and sophisticated data correlation across diverse sources. The higher luminosity, event complexity and input rate present an unprecedented challenge to the High Level Trigger that aims to achieve a similar efficiency and rejection factor as today despite the higher pileup and more pure preselection. In this presentation we will discuss the ongoing studies and prospects for the online reconstruction and selection algorithms for the high-luminosity era.
Low Latency, Online Processing of the High-Bandwidth Bunch-By-Bunch Observation Data From the Transverse Feedback System in the LHC
Authors: Söderén Martin , Valuch Daniel
Published: November 2020

During long shutdown 2 (2019-2020) the transverse observation system (ADTObsBox) in the LHC will undergo a substantial upgrade. The purpose of this upgrade is to allow for true low latency, online processing of the 16 data-streams of transverse bunch-by-bunch, turn-by-turn positional data provided by the transverse feedback system in the LHC (ADT). This system makes both offline and online analysis of the data possible, where the emphasis will lie on online analysis, something that the older generation was not designed to provide. The result of the analysis is made available for accelerator physicists, machine operators, and engineers working with LHC. The new system allows users to capture buffers of various lengths for later analysis just like the older generation and it provides a platform for real-time analysis applications to directly capture the data with minimal latency while also providing a heterogeneous computing platform where the applications can utilize CPUs, GPUs and dedicated FPGAs. The analysis applications include bunch-by-bunch instability analysis and passive bunch-by-bunch tune extraction to name a few. The ADTObsBox system uses commodity server technology in combination with FPGA-based PCIe I/O cards. This paper will cover the design and status of the I/O cards, server, firmware, driver, analysis applications and results of early performance testing.
Dealing with High Background Rates in the STAR Heavy Flavor Tracker in Simulation: Embedding Simulation into Real Events
Authors: Webb Jason C. , Lauret Jérôme , Van Buren Gene , Radhakrishnan Sooraj , Dong Xin
Published: November 2020

The STAR Heavy Flavor Tracker (HFT) has enabled a rich physics program, providing important insights into heavy quark behavior in heavy ion collisions. Acquiring data during the 2014 through 2016 runs at the Relativistic Heavy Ion Collider (RHIC), the HFT consisted of four layers of precision silicon sensors. Used in concert with the Time Projection Chamber (TPC), the HFT enables the reconstruction and topological identification of tracks arising from charmed hadron decays. The ultimate understanding of the detector efficiency and resolution demands large quantities of high quality simulations, accounting for the precise alignment of sensors, and the detailed response of the detectors and electronics to the incident tracks. The background environment presented additional challenges, as simulating the significant rates from pileup events accumulated during the long integration times of the tracking detectors could have quickly exceeded the available computational resources, and the relative contributions from different sources was unknown. STAR has long addressed these issues by embedding simulations into background events directly sampled during data taking at the experiment. This technique has the advantage of providing a completely realistic picture of the dynamic background environment while introducing minimal additional computational overhead compared to simulation of the primary collision alone, thus scaling to any luminosity. We will discuss how STAR has applied this technique to the simulation of the HFT, and will show how the careful consideration of misalignment of precision detectors and calibration uncertainties results in the detailed reproduction of basic observables, such as track projection to the primary vertex. We will further summarize the experience and lessons learned in applying these techniques to heavy-flavor simulations and discuss recent results.
Generation of Belle II Pixel Detector Background Data with a GAN
Authors: Srebre Matej , Schmolz Pascal , Hashemi Hosein , Ritter Martin , Kuhr Thomas
Published: November 2020

To match the statistical precision due to the large dataset that Belle II is expected to collect, simulations that accurately describe real data are required. The effect of beam background must be considered and can be taken into account with overlaying random trigger data, but its large size is a technical challenge. This problem can be mitigated by generating beam background data with generative adversarial networks. A proof of principle is shown for the background data recorded by the pixel vertex detector.
Automatic Differentiation in ROOT
Authors: Vassilev Vassil , Efremov Aleksandr , Shadura Oksana
Published: November 2020

In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evaluate the derivative of a function specified by a computer program. AD exploits the fact that every computer program, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.), elementary functions (exp, log, sin, cos, etc.) and control flow statements. AD takes source code of a function as input and produces source code of the derived function. By applying the chain rule repeatedly to these operations, derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor more arithmetic operations than the original program. This paper presents AD techniques available in ROOT, supported by Cling, to produce derivatives of arbitrary C/C++ functions through implementing source code transformation and employing the chain rule of differential calculus in both forward mode and reverse mode. We explain its current integration for gradient computation in TFormula. We demonstrate the correctness and performance improvements in ROOT’s fitting algorithms.
Using machine learning to speed up new and upgrade detector studies: a calorimeter case
Authors: Ratnikov Fedor , Derkach Denis , Boldyrev Alexey , Shevelev Andrey , Fakanov Pavel , Matyushin Leonid
Published: November 2020

In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design concept (CDR) and technical design (TDR) phases of future detectors and existing detectors if upgraded. The machine learning approaches may improve the precision of the reconstruction methods being considered during detector R&D. Moreover, such reconstruction methods can be reproduced automatically while changing the main optimisation parameters of the detector like geometrical size, position, configuration, radiation length, Molière radius of the sensitive elements. This allows us to speed up the verification of the possible detector configurations and eventually the entire detector R&D, which is often accompanied by a large number of scattered studies. We present the approach of using machine learning for detector R&D and its optimisation cycle with an emphasis on the project of the electromagnetic calorimeter upgrade for the LHCb detector[1]. The reconstruction methods such as spatial reconstruction, timing reconstruction, and distinguishing of overlapped signals are covered in this paper.
Integration and Performance of New Technologies in the CMS Simulation
Authors: Pedro Kevin
Published: November 2020

The HL-LHC and the corresponding detector upgrades for the CMS experiment will present extreme challenges for the full simulation. In particular, increased precision in models of physics processes may be required for accurate reproduction of particle shower measurements from the upcoming High Granularity Calorimeter. The CPU performance impacts of several proposed physics models will be discussed. There are several ongoing research and development efforts to make efficient use of new computing architectures and high performance computing systems for simulation. The integration of these new R&D products in the CMS software framework and corresponding CPU performance improvements will be presented.
Calibration and Performance of the CMS Electromagnetic Calorimeter in LHC Run2
Authors: Cavallari Francesca , Rovelli Chiara
Published: November 2020

Many physics analyses using the Compact Muon Solenoid (CMS) detector at the LHC require accurate, high resolution electron and photon energy measurements. Excellent energy resolution is crucial for studies of Higgs boson decays with electromagnetic particles in the final state, as well as searches for very high mass resonances decaying to energetic photons or electrons. The CMS electromagnetic calorimeter (ECAL) is a fundamental instrument for these analyses and its energy resolution is crucial for the Higgs boson mass measurement. Recently the energy response of the calorimeter has been precisely calibrated exploiting the full Run2 data, aiming at a legacy reprocessing of the data. A dedicated calibration of each detector channel has been performed with physics events exploiting electrons from W and Z boson decays, photons from π0 and η decays, and from the azimuthally symmetric energy distribution of minimum bias events. This talk presents the calibration strategies that have been implemented and the excellent performance achieved by the CMS ECAL with the ultimate calibration of Run2 data, in terms of energy scale stability and energy resolution.
ATLAS Event Store and I/O developments in support for Production and Analysis in Run 3
Authors: Nowak Marcin , van Gemmeren Peter , Cranshaw Jack
Published: November 2020

During the long LHC shutdown, ATLAS experiment is preparing several fundamental changes to its offline event processing framework and analysis model. These include moving to multi-threaded reconstruction and simulation and reducing data duplication during derivation analysis by producing a combined mini-xAOD stream. These changes will allow ATLAS to take advantage of the higher luminosity at Run 3 without overstraining processing and storage capabilities. They also require significant improvements to the underlying event store and the I/O framework to support them. These improvements include: 1) an overhaul of the Run 2 I/O framework to be thread-safe and minimize serial bottlenecks, 2) introduction of new immutable references for object navigation, which don’t rely on storage container entry number so data can be merged in-memory, 3) using filter decisions to annotate combined output stream to allow for fast event selection on input and 4) selecting optimized compression algorithms and settings to allow efficient reading of event selections.
New developments in cost modeling for the LHC computing
Authors: Biscarat Catherine , Boccali Tommaso , Bonacorsi Daniele , Bozzi Concezio , Costanzo Davide , Duellmann Dirk , Elmsheuser Johannes , Fede Eric , Flix Molina José , Giordano Domenico , Grigoras Costin , Iven Jan , Jouvin Michel , Kemp Yves , Lange David , Maganza Riccardo , Meinhard Helge , Michelotto Michele , Roy Gareth Douglas , Sansum Andrew , Sartirana Andrea , Schulz Markus , Sciabà Andrea , Smirnova Oxana , Stewart Graeme , Valassi Andrea , Vernet Renaud , Wenaus Torre , Wuerthwein Frank
Published: November 2020

The increase in the scale of LHC computing during Run 3 and Run 4 (HL-LHC) will certainly require radical changes to the computing models and the data processing of the LHC experiments. The working group established by WLCG and the HEP Software Foundation to investigate all aspects of the cost of computing and how to optimise them has continued producing results and improving our understanding of this process. In particular, experiments have developed more sophisticated ways to calculate their resource needs, we have a much more detailed process to calculate infrastructure costs. This includes studies on the impact of HPC and GPU based resources on meeting the computing demands. We have also developed and perfected tools to quantitatively study the performance of experiments workloads and we are actively collaborating with other activities related to data access, benchmarking and technology cost evolution. In this contribution we expose our recent developments and results and outline the directions of future work.
Operational Intelligence for Distributed Computing Systems for Exascale Science
Authors: Di Girolamo Alessandro , Legger Federica , Paparrigopoulos Panos , Klimentov Alexei , Schovancová Jaroslava , Kuznetsov Valentin , Lassnig Mario , Clissa Luca , Rinaldi Lorenzo , Sharma Mayank , Bakhshiansohi Hamed , Zvada Marian , Bonacorsi Daniele , Rossi Tisbeni Simone , Giommi Luca , Decker de Sousa Leticia , Diotalevi Tommaso , Grigorieva Maria , Padolski Sergey
Published: November 2020

In the near future, large scientific collaborations will face unprecedented computing challenges. Processing and storing exabyte datasets require a federated infrastructure of distributed computing resources. The current systems have proven to be mature and capable of meeting the experiment goals, by allowing timely delivery of scientific results. However, a substantial amount of interventions from software developers, shifters and operational teams is needed to efficiently manage such heterogeneous infrastructures. A wealth of operational data can be exploited to increase the level of automation in computing operations by using adequate techniques, such as machine learning (ML), tailored to solve specific problems. The Operational Intelligence project is a joint effort from various WLCG communities aimed at increasing the level of automation in computing operations. We discuss how state-of-the-art technologies can be used to build general solutions to common problems and to reduce the operational cost of the experiment computing infrastructure.
Exploiting CRIC to streamline the configuration management of GlideinWMS factories for CMS support
Authors: Dost Jeffrey , Mascheroni Marco , Andreeva Julia , Anisenkov Alexey , Box Dennis , Di Girolamo Alessandro , Haleem Saqib , Kizinevič Edita , Majewski Krista , Letts James , Lobato Pardavila Lorena , Moreira Coimbra Bruno , Pérez-Calero Yzquierdo Antonio , Mambelli Marco , Paparrigopoulos Panos , Zvada Marian
Published: November 2020

GlideinWMS is a workload management and provisioning system that allows sharing computing resources distributed over independent sites. Based on the requests made by GlideinWMS frontends, a dynamically sized pool of resources is created by GlideinWMS pilot factories via pilot job submission to resource sites’ CEs. More than 400 CEs are currently serving more than ten virtual organizations through GlideinWMS, with CMS being the biggest user with 230 CEs. The complex configurations of the parameters defining resource requests, as submitted to those CEs, have been historically managed by manually editing a set of different XML files. New possibilities arise with CMS adopting the CRIC, an information system that collects, aggregates, stores, and exposes, among other things, computing resource data coming from various data providers. The paper will describe the challenges faced when CMS started to use CRIC to automatically generate the GlideinWMS factory configurations. The architecture of the prototype, and the ancillary tools developed to ease this transition, will be discussed. Finally, future plans and milestones will be outlined.
Harnessing the power of supercomputers using the PanDA Pilot 2 in the ATLAS Experiment
Authors: Nilsson Paul , Anisenkov Alexey , Benjamin Doug , Guan Wen , Javurek Tomas , Oleynik Danila
Published: November 2020

The unprecedented computing resource needs of the ATLAS experiment at LHC have motivated the Collaboration to become a leader in exploiting High Performance Computers (HPCs). To meet the requirements of HPCs, the PanDA system has been equipped with two new components; Pilot 2 and Harvester, that were designed with HPCs in mind. While Harvester is a resource-facing service which provides resource provisioning and workload shaping, Pilot 2 is responsible for payload execution on the resource. The presentation focuses on Pilot 2, which is a complete rewrite of the original PanDA Pilot used by ATLAS and other experiments for well over a decade. Pilot 2 has a flexible and adaptive design that allows for plugins to be defined with streamlined workflows. In particular, it has plugins for specific hardware infrastructures (HPC/GPU clusters) as well as for dedicated workflows defined by the needs of an experiment. Examples of dedicated HPC workflows are discussed in which the Pilot either uses an MPI application for processing fine-grained event level service under the control of the Harvester service or acts like an MPI application itself and runs a set of job in an assemble. In addition to describing the technical details of these workflows, results are shown from its deployment on Titan (OLCF) and other HPCs in ATLAS.
CRIC: Computing Resource Information Catalogue as a unified topology system for a large scale, heterogeneous and dynamic computing infrastructure
Authors: Anisenkov Alexey , Andreeva Julia , Di Girolamo Alessandro , Paparrigopoulos Panos , Vasilev Boris
Published: November 2020

CRIC is a high-level information system which provides flexible, reliable and complete topology and configuration description for a large scale distributed heterogeneous computing infrastructure. CRIC aims to facilitate distributed computing operations for the LHC experiments and consolidate WLCG topology information. It aggregates information coming from various low-level information sources and complements topology description with experimentspecific data structures and settings required by the LHC VOs in order to exploit computing resources.

Being an experiment-oriented but still experiment-independent information middleware, CRIC offers a generic solution, in the form of a suitable framework with appropriate interfaces implemented, which can be successfully applied on the global WLCG level or at the level of a particular LHC experiment. For example there are CRIC instances for CMS[11] and ATLAS[10]. CRIC can even be used for a special task. For example, a dedicated CRIC instance has been built to support transfer tests performed by DOMA Third Party Copy working group. Moreover, extensibility and flexibility of the system allow CRIC to follow technology evolution and easily implement concepts required to describe new types of computing and storage resources.

The contribution describes the overall CRIC architecture, the plug-in based implementation of the CRIC components as well as recent developments and future plans.
CMS data access and usage studies at PIC Tier-1 and CIEMAT Tier-2
Authors: Delgado Peris Antonio , Flix Molina José , Hernández José M. , Pérez-Calero Yzquierdo Antonio , Pérez Dengra Carlos , Planas Elena , Rodríguez Calonge Francisco Javier , Sikora Anna
Published: November 2020

The current computing models from LHC experiments indicate that much larger resource increases would be required by the HL-LHC era (2026+) than those that technology evolution at a constant budget could bring. Since worldwide budget for computing is not expected to increase, many research activities have emerged to improve the performance of the LHC processing software applications, as well as to propose more efficient resource deployment scenarios and data management techniques, which might reduce this expected increase of resources. The massively increasing amounts of data to be processed leads to enormous challenges for HEP storage systems, networks and the data distribution to end-users. These challenges are particularly important in scenarios in which the LHC data would be distributed from small numbers of centers holding the experiment’s data. Enabling data locality relative to computing tasks via local caches on sites seems a very promising approach to hide transfer latencies while reducing the deployed storage space and number of replicas overall. However, this highly depends on the workflow I/O characteristics and available network across sites. A crucial assessment of how the experiments are accessing and using the storage services deployed in WLCG sites, to evaluate and simulate the benefits for several of the new emerging proposals within WLCG/HSF. Studies on access and usage of storage, data access and popularity studies for the CMS workflows executed in the Spanish Tier-1 (PIC) and Tier-2 (CIEMAT) sites supporting CMS activities are reviewed in this report, based on local and experiment monitoring data, spanning more than one year. This is of relevance for simulation of data caches for end-user analysis data, as well as identifying potential areas for storage savings.
Design principles of the Metadata Querying Language (MQL) implemented in the ATLAS Metadata Interface (AMI) ecosystem
Authors: Fulachier Jérôme , Odier Jérôme , Lambert Fabian
Published: November 2020

This document describes the design principles of the Metadata Querying Language (MQL) implemented in ATLAS Metadata Interface (AMI), a metadata-oriented domain-specific language allowing to query databases without knowing the relation between tables. With this simplified yet generic grammar, MQL permits writing complex queries more simply than with Structured Query Language (SQL).
Implementation and performance of a DPM federated storage and integration within the ATLAS environment
Authors: Adam Bourdarios Claire , Chevaleyre Jean-Claude , Chollet Frédérique , Crépé-Renaudin Sabine , Gondrand Christine , Jezequel Stéphane , Knoops Edith , Seraphin Philippe
Published: November 2020

With the increase of storage needs at the High-Luminosity LHC horizon, data management and access will be very challenging. The evaluation of possible solutions within the WLCG Data Organization, Management and Access (DOMA) is a major activity to select the most optimal from the experiment and site point of views. Four teams hosting Tier-2s for ATLAS with storage based on DPM technology have put their expertise and computing infrastructures in common to build a testbed hosting a DPM federated storage called FR-ALPES. This note describes the infrastructure put in place, its integration within the ATLAS Grid infrastructure and presents the first results.
Concurrent data structures in the ATLAS offline software
Authors: Snyder Scott
Published: November 2020

In preparation for Run 3 of the LHC, the ATLAS experiment is modifying its offline software to be fully multithreaded. An important part of this is data structures that can be concurrently accessed from many threads both efficiently and safely. A standard way of achieving this is through mutual exclusion; however, the overhead from this can sometimes be excessive. Fully lockless implementations are known for some data structures; however, they are typically complex, and the overhead they require can sometimes be larger than that required for locking implementations. An interesting compromise is to allow lockless access only for reading but not for writing. This often allows the data structures to be much simpler, while still giving good performance for readmostly access patterns. This proceeding shows some examples of this strategy in data structures used by the ATLAS offline software. It also gives examples of synchronization strategies inspired by read-copy-update, as well as helpers for memoizing values in a multithreaded environment.
Optimization of Software on High Performance Computing Platforms for the LUX-ZEPLIN Dark Matter Experiment
Authors: Ayyar Venkitesh , Bhimji Wahid , Monzani Maria Elena , Naylor Andrew , Patton Simon , Tull Craig E.
Published: November 2020

High Energy Physics experiments like the LUX-ZEPLIN dark matter experiment face unique challenges when running their computation on High Performance Computing resources. In this paper, we describe some strategies to optimize memory usage of simulation codes with the help of profiling tools. We employed this approach and achieved memory reduction of 10-30%. While this has been performed in the context of the LZ experiment, it has wider applicability to other HEP experimental codes that face these challenges on modern computer architectures.
A software framework for FCC studies: status and plans
Authors: Cervantes Javier , Faltova Jana , Ganis Gerardo , Helsens Clement , Hrdinka Julia , Neubüser Coralie , Pezzotti Lorenzo , Selvaggi Michele , Tehrani Niloufar A. , Volkl Valentin , Zaborowska Anna
Published: November 2020

The Future Circular Collider (FCC) is designed to provide unprecedented luminosity and centre-of-mass energies. The physics reach and potential of the different FCC options e+e−, pp, ep, has been studied and published in dedicated Conceptual Design Reports (CDRs) at the end of 2018. Conceptual detector designs have been developed for such studies and tested with a mixture of fast and full simulations. The investigations for all options have been conducted using a common software framework called FCCSW. In this paper, after summarising the improvements implemented in FCCSW to achieve the results included in the CDRs, we will present the current development plans to support the continuation of the physics potential and detector concept optimisation studies in view of future strategic decisions, in particular for the electron-positron machine.
The Scalable Systems Laboratory: a Platform for Software Innovation for HEP
Authors: Gardner Robert , Bryant Lincoln , Neubauer Mark , Wuerthwein Frank , Stephen Judith , Chien Andrew
Published: November 2020

The Scalable Systems Laboratory (SSL), part of the IRIS-HEP Software Institute, provides Institute participants and HEP software developers generally with a means to transition their R&D from conceptual toys to testbeds to production-scale prototypes. The SSL enables tooling, infrastructure, and services supporting innovation of novel analysis and data architectures, development of software elements and tool-chains, reproducible functional and scalability testing of service components, and foundational systems R&D for accelerated services developed by the Institute. The SSL is constructed with a core team having expertise in scale testing and deployment of services across a wide range of cyberinfrastructure. The core team embeds and partners with other areas in the Institute, and with LHC and other HEP development and operations teams as appropriate, to define investigations and required service deployment patterns. We describe the approach and experiences with early application deployments, including analysis platforms and intelligent data delivery systems.
Awkward Arrays in Python, C++, and Numba
Authors: Pivarski Jim , Elmer Peter , Lange David
Published: November 2020

The Awkward Array library has been an important tool for physics analysis in Python since September 2018. However, some interface and implementation issues have been raised in Awkward Array’s first year that argue for a reimplementation in C++ and Numba. We describe those issues, the new architecture, and present some examples of how the new interface will look to users. Of particular importance is the separation of kernel functions from data structure management, which allows a C++ implementation and a Numba implementation to share kernel functions, and the algorithm that transforms recordoriented data into columnar Awkward Arrays.
A gateway between GitLab CI and DIRAC
Authors: Burr Chris , Couturier Ben
Published: November 2020

GitLab’s Continuous Integration has proven to be an efficient tool to manage the lifecycle of experimental software. This has sparked interest in uses that exceed simple unit tests, and therefore require more resources, such as production data configuration and physics data analysis. The default GitLab CI runner software is not appropriate for such tasks, and we show that it is possible to use the GitLab API and modern container orchestration technologies to build a custom CI runner that integrates with DIRAC, the middleware used by the LHCb experiment to run its job on the Worldwide LHC Computing Grid. This system allows for excellent utilisation of computing resources while also providing additional flexibility for defining jobs and providing authentication.
Gentoo Prefix as a Physics Software Manager
Authors: Xu Benda , Amadio Guilherme , Groﬀen Fabian , Haubenwallner Michael
Published: November 2020

Gentoo Prefix is explored to manage sophisticated physics software stacks. It will be shown that Gentoo Prefix is an advantageous package management solution for big physics experiments, for its reusability on heterogeneous host environments, its vast collection of ebuild recipes, its extensibility for the future computing architectures and its deep root in an open diverse community inside and outside science.
An ARM cluster for running CMSSW jobs
Authors: Osmani Lirim , Lindén Tomas
Published: November 2020

The ARM platform extends from the mobile phone area to development board computers and servers. It could be that in the future the importance of the ARM platform will increase for High Performance Computing/High Throughput Computing (HPC/HTC) if new more powerful (server) boards are released. For this reason Compact Muon Solenoid Software (CMSSW) has previously been ported to ARM in earlier work.

The CMSSW is deployed using the CERN Virtual Machine File System (CVMFS) and the jobs are run inside Singularity containers. Some ARM AArch64 CMSSW releases are available in CVMFS for testing and development. In this work CVMFS and Singularity have been compiled and installed on an ARM cluster and the AArch64 CMSSW releases in CVMFS have been used. We report on our experiences with this ARM cluster for CMSSW jobs.

Commodity hardware designed around the 64-bit architecture has been the basis of current virtualization trends with the advantage to emulate diverse environments for a wide range of computational scenarios. However, in parallel the mobile revolution have given a rise to ARM SoCs with primary focus on power efficiency. While still in the experimental phase, the power efficiency and 64-bit heterogeneous computing already point to an alternative option for traditional x86_64 CPUs servers for datacenters.
Knowledge sharing on deep learning in physics research using VISPA
Authors: Beer Max , Eich Niclas , Erdmann Martin , Fackeldey Peter , Fischer Benjamin , Hafner Katharina , Noll Dennis Daniel Nick , Rath Yannik Alexander , Rieger Marcel , Temme Alexander , Vieweg Max , Urban Martin
Published: November 2020

The VISPA (VISual Physics Analysis) project provides a streamlined work environment for physics analyses and hands-on teaching experiences with a focus on deep learning. VISPA has already been successfully used in HEP analyses and teaching and is now being further developed into an interactive deep learning platform. One specific example is to meet knowledge sharing needs in deep learning by combining paper, code and data at a central place. Additionally the possibility to run it directly from the web browser is a key feature of this development. Any SSH reachable resource can be accessed via the VISPA web interface. This enables a flexible and experiment agnostic computing experience. The user interface is based on JupyterLab and is extended with analysis specific tools, such as a parametric file browser and TensorBoard. Our VISPA instance is backed by extensive GPU resources and a rich software environment. We present the current status of the VISPA project and its upcoming new features.
Visualisations of Centre Vortices
Authors: Biddle James , Kamleh Waseem , Leinweber Derek
Published: November 2020

The centre vortex structure of the vacuum is visualised through the use of novel 3D visualisation techniques. These visualisations allow for a hands-on examination of the centre-vortex matter present in the QCD vacuum, and highlights some of the key features of the centre-vortex model. The connection between topological charge and singular points is also explored. This work highlights the useful role visualisations play in the exploration of the QCD vacuum.
The CMS approach to Analysis Preservation
Authors: Lloret Iglesias Lara
Published: November 2020

The CERN analysis preservation portal (CAP) comprises a set of tools and services aiming to assist researchers in describing and preserving all the components of a physics analysis such as data, software and computing environment. Together with the associated documentation, all these assets are kept in one place so that the analysis can be fully or partially reused even several years after the publication of the original scientific results. An experimentspecific submission and retrieval interface has been developed for the CMS Collaboration. It integrates with the CMS internal analysis registry (CADI) to capture all analyses with basic information, complemented with a detailed submission form for full information. The CMS data aggregation system (DAS) is interfaced to the deposit form to assist in filling in exact dataset names used in the analysis to ensure searchability. Efforts are ongoing to describe physics content for an intelligent retrieval, and to interface with container solutions for full reproducibility for selected test cases.
Exploring the 2HDM with Global Fits in GAMBIT
Authors: Rajec Filip , Su Wei , White Martin , Williams Anthony G.
Published: November 2020

In this work, we present preliminary results of a global fit of the typeII two-Higgs-doublet model (2HDM) with the tool GAMBIT. Our study includes various constraints, including the theoretical constraints (unitarity, perturbativity and vacuum stability), Higgs searches at colliders, electroweak physics and flavour constraints. With the latest experimental results, our results not only confirm past studies but also go further in probing the model. We find, for example, that the measurements of B→K*µ+µ− angular observables cannot be explained in the type-II 2HDM.
New Physics Agnostic Selections For New Physics Searches
Authors: Woźniak Kinga Anna , Cerri Olmo , Duarte Javier M. , Möller Torsten , Ngadiuba Jennifer , Nguyen Thong Q. , Pierini Maurizio , Spiropulu Maria , Vlimant Jean-Roch
Published: November 2020

We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on the loss assigned to each event, input data can be split into a background control sample and a signal enriched sample. Following this strategy, one can enhance the sensitivity to new physics with no assumption on the underlying new physics signature. Our results show that a typical BSM search on the signal enriched group is more sensitive than an equivalent search on the original dataset.
Hybrid analysis pipelines in the REANA reproducible analysis platform
Authors: Rodríguez Diego , Mačiulaitis Rokas , Okraska Jan , Šimko Tibor
Published: November 2020

We introduce the feasibility of running hybrid analysis pipelines in the REANA reproducible analysis platform. The REANA platform allows researchers to specify declarative computational workflow steps describing the analysis process and to execute analysis workload on remote containerised compute clouds. We have designed an abstract job controller component permitting to execute different parts of the analysis workflow on different compute backends, such as HTCondor, Kubernetes and SLURM. We have prototyped the designed solution including the job execution, job monitoring, and input/output file staging mechanism between the various compute backends. We have tested the prototype using several particle physics model analyses. The present work introduces support for hybrid analysis workflows in the REANA reproducible analysis platform and paves the way towards studying underlying performance advantages and challenges associated with hybrid analysis patterns in complex particle physics data analyses.
Security Mechanism for user access to Single SSID WLAN
Authors: Wang Li , Xia Mingshan , Qi Fazhi
Published: November 2020

Wireless local area network (WLAN) technology is widely used in various enterprises and institutions. In order to facilitate the use of users, they often provide a single SSID access point, resulting in different identities of users authenticated and authorized can connect to the wireless network anytime, anywhere as needed and obtain the same accessible network resources such as bandwidth, access control (ACL) and so on. Multiple SSID can solve the problem but it will be confused to users who don’t know which SSID can be connected. Although we could prevent visitors from accessing intranet resources by isolating the wireless network from the internal network, this would make it impossible for users to use the wireless network for internal office work. In this paper, we propose an access control system that grouping users according to the different identities and users authenticated and authorized can access different network resources because a wireless access point dynamically maps an SSID provided by a mobile station to a BSSID based on a VLAN assignment. The deployment experiment of the solution proves that users of different identities accessing the same wireless network can set different access policies, which effectively improves the security of the wireless network and simplifies the management of the wireless network.
Using HEP experiment workflows for the benchmarking and accounting of WLCG computing resources
Authors: Valassi Andrea , Alef Manfred , Barbet Jean-Michel , Datskova Olga , De Maria Riccardo , Fontes Medeiros Miguel , Giordano Domenico , Grigoras Costin , Hollowell Christopher , Javurkova Martina , Khristenko Viktor , Lange David , Michelotto Michele , Rinaldi Lorenzo , Sciabà Andrea , Van Der Laan Cas
Published: November 2020

Benchmarking of CPU resources in WLCG has been based on the HEP-SPEC06 (HS06) suite for over a decade. It has recently become clear that HS06, which is based on real applications from non-HEP domains, no longer describes typical HEP workloads. The aim of the HEP-Benchmarks project is to develop a new benchmark suite for WLCG compute resources, based on real applications from the LHC experiments. By construction, these new benchmarks are thus guaranteed to have a score highly correlated to the throughputs of HEP applications, and a CPU usage pattern similar to theirs. Linux containers and the CernVM-FS filesystem are the two main technologies enabling this approach, which had been considered impossible in the past. In this paper, we review the motivation, implementation and outlook of the new benchmark suite.
Offsite Data Processing for the GlueX Experiment
Authors: Lawrence David
Published: November 2020

The Thomas Jefferson National Accelerator Facility (JLab) 12GeV accelerator upgrade completed in 2015 is now producing data at volumes unprecedented for the lab. The resources required to process this data now exceed the capacity of the onsite farm necessitating the use of offsite computing resources for the first time in the history of JLab. GlueX is now utilizing NERSC and PSC for raw data production. Details of the workflow are presented.
Predicting resource usage for enhanced job scheduling for opportunistic resources in HEP
Authors: Kuehn Eileen , Fischer Max , Lange Sven , Petzold Andreas , Heiss Andreas
Published: November 2020

To overcome the computing challenge in High Energy Physics available resources must be utilized as efficiently as possible. This targets algorithmic challenges in the workflows itself but also the scheduling of jobs to compute resources. To enable the best possible scheduling, job schedulers require accurate information about resource consumption of a job before it is even executed. It is the responsibility of the user to provide an accurate resource estimate required for jobs. However, this is quite a challenge for users as they (i) want to ensure their jobs to run correctly, (ii) must manage to deal with heterogeneous compute resources and (iii) face intransparent library dependencies and frequent updates. Users therefore tend to specify resource requests with an ample buffer. This inaccuracy results in inefficient utilisation by either blocking unused resources or exceeding reserved resources. Especially in the context of opportunistic resource provisioning the inaccuracies have an even broader impact that does not even target utilisation of resources but also composition of the most suitable resources. The contribution of this paper is an analysis of production and end-user workflows in HEP with regards to optimizing the various resources types. We further propose a method to improve user estimates.
Construction of a New Data Center at BNL
Authors: Latif Imran , Misawa Shigeki , Zaytsev Alexandr
Published: November 2020

Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory’s scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source II (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS experiment at Europe’s Large Hadron Collider (LHC) and the Belle II experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL.
Managing the CERN Batch System with Kubernetes
Authors: Fernandez Alvarez Luis , Datskova Olga , Jones Ben , McCance Gavin
Published: November 2020

The CERN Batch Service faces many challenges in order to get ready for the computing demands of future LHC runs. These challenges require that we look at all potential resources, assessing how efficiently we use them and that we explore different alternatives to exploit opportunistic resources in our infrastructure as well as outside of the CERN computing centre. Several projects, like BEER, Helix Nebula Science Cloud and the new OCRE project, have proven our ability to run batch workloads on a wide range of non-traditional resources. However, the challenge is not only to obtain the raw compute resources needed but how to define an operational model that is cost and time efficient, scalable and flexible enough to adapt to a heterogeneous infrastructure. In order to tackle both the provisioning and operational challenges it was decided to use Kubernetes. By using Kubernetes we benefit from a de-facto standard in containerised environments, available in nearly all cloud providers and surrounded by a vibrant ecosystem of open-source projects. Leveraging Kubernetes’ built-in functionality, and other open-source tools such as Helm, Terraform and GitLab CI, we have deployed a first cluster prototype which we discuss in detail. The effort has simplified many of the existing operational procedures we currently have, but has also made us rethink established procedures and assumptions that were only valid in a VM-based cloud environment. This contribution presents how we have adopted Kubernetes into the CERN Batch Service, the impact its adoption has in daily operations, a comparison on resource usage efficiency and the experience so far evolving our infrastructure towards this model.
WLCG Dashboards with Unified Monitoring
Authors: Andrade Pedro , Aimar Alberto , Brundu Simone , Garrido Bear Borja , Menendez Borge Gonzalo , Magnoni Luca , Lima Nicolau Diogo , Tsvetkov Nikolay
Published: November 2020

Monitoring of the CERN Data Centres and the WLCG infrastructure is now largely based on the new monitoring infrastructure provided by CERN IT. This is the result of the migration from several old in-house developed monitoring tools into a common monitoring infrastructure based on open source technologies such as Collectd, Flume, Kafka, Spark, InfluxDB, Grafana and others. This new infrastructure relies on CERN IT services (OpenStack, Puppet, Gitlab, DBOD, etc) and covers the full range of monitoring tasks: metrics and logs collection, alarms generation, data validation and transport, data enrichment and aggregation (where applicable), dashboards visualisation, reports generation, etc. This contribution will present the different services offered by the infrastructure today, highlight the main monitoring use cases from the CERN Data Centres and WLCG, and analyse the last years experience of moving from legacy well-established custom monitoring tools into a common open source-based infrastructure.