bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing Spatial Release From Masking Using Traditional Methods and Portable Automated Rapid Testing iPad App
Authors: Srinivasan Nirmal Kumar , Holtz Allison , Gallun Frederick J.
Published: November 2020

Purpose

The purpose of this study was to compare speech identification abilities of individuals of various ages and hearing abilities using traditional methods and Portable Automated Rapid Testing (PART) iPad app.

Method

Speech identification data were collected using three techniques: over headphones using a virtual speaker array, using PART iPad app (UCR Brain Game Center, 2018), and using loudspeaker presentation in a sound-attenuated room. For all three techniques, Coordinate Response Measure sentences were used as the stimuli and “Charlie” was used as the call sign. A progressive tracking procedure was used to estimate the speech identification thresholds for listeners with varying hearing thresholds. The target sentence was always presented at 0° azimuth angle, whereas the maskers were colocated (0°) with the target or symmetrically spatially separated by ±15°, ±30°, or ±45°.

Results

Data analysis revealed similar speech identification thresholds for the iPad and headphone conditions and slightly poorer thresholds for the loudspeaker array condition across participant groups. This was true for all spatial separations between the target and the maskers.

Conclusion

Strong correlation between the headphone and iPad data presented in this study indicated that the spatial release from masking module in the PART iPad app can be used as a clinical tool to assess spatial processing ability prior to audiologic evaluation in the clinic and can also be used to make recommendations for and to track progress with aural rehabilitation programs over time.
INTEGRAL discovery of a high-energy tail in the microquasar Cygnus X-3
Authors: Cangemi F. , Rodriguez J. , Grinberg V. , Belmont R. , Laurent P. , Wilms J.
Published: November 2020

Context. The X-ray spectra of X-ray binaries are dominated by emission of either soft or hard X-rays which defines their soft and hard spectral states. While the generic picture is relatively well understood, little is known about the interplay of the various media at work, or about the reasons why some sources do not follow common behavior. Cygnus X-3 is amongst the list of X-ray binaries that show quite complex behavior, with various distinct spectral states not seen in other sources. These states have been characterized in many studies. Because of its softness and intrinsic low flux above typically 50 keV, very little is known about the hard X/soft gamma-ray (100–1000 keV) emission in Cygnus X-3.

Aims. Using the whole INTEGRAL data base, we aim to explore the 3–1000 keV spectra of Cygnus X-3. This allows to probe this region with the highest sensitivity ever, and search for the potential signature of a high-energy non-thermal component as sometimes seen in other sources.

Methods. Our work is based on state classification carried out in previous studies with data from the Rossi X-Ray Timing Explorer. We extend this classification to the whole INTEGRAL data set in order to perform a long-term state-resolved spectral analysis. Six stacked spectra were obtained using 16 years of data from JEM-X (3–25 keV), ISGRI (25–300 keV), and SPI (20–400 keV).

Results. We extract stacked images in three different energy bands, and detect the source up to 200 keV. In the hardest states, our purely phenomenological approach clearly reveals the presence of an additonnal component > 50 keV in addition to the component usually interpreted as thermal Comptonization. We apply a more physical model of hybrid thermal/nonthermal corona (EQPAIR) to characterize this nonthermal component and compare our results with those of previous studies and analyses. Our modeling indicates a more efficient acceleration of electrons in states where major ejections are observed. We also evaluate and find a dependence of the photon index of the power law as a function of the strong orbital modulation of the source in the Flaring InterMediate state. This dependence could be due to a higher absorption when Cygnus X-3 is behind its companion. However, the uncertainties on the density column prevent us from drawing any firm conclusions.
A novel centralized slow control and board management solution for ATCA blades based on the Zynq Ultrascale+ System-on-Chip
Authors: Ardila-Perez Luis , Cascadan André , Calligaris Luigi , Tcherniakhovski Denis , Balzer Matthias , Weber Marc , Sander Oliver
Published: November 2020

Data acquisition systems (DAQ) for high energy physics experiments utilize complex FPGAs to handle unprecedented high data rates, especially in the first stages of the data processing chain. The complexity of developing and commissioning these systems increases as additional local processing intelligence is placed closer to the detector directly on the ATCA blades. On the other hand, sophisticated slow control is as well desired. In this contribution, we introduce a novel solution for ATCA based systems, which combines the IPMI, a Linux based slow-control software, and an FPGA for custom slow-control tasks in one single Zynq Ultrascale+ (ZUS+) System-on-Chip (SoC) module.
Mass storage interface LTSM for FAIR Phase 0 data acquisition
Authors: Adamczewski-Musch Jörn , Stibor Thomas
Published: November 2020

Since 2018 several FAIR Phase 0 beamtimes have been operated at GSI, Darmstadt. Here the new challenging technologies for the upcoming FAIR facility shall be tested while various physics experiments are performed with the existing GSI accelerators. One of these challenges concerns the performance, reliability, and scalability of the experiment data storage. Raw data as collected by event building software of large scale detector data acquisition has to be safely written to a mass storage system like a magnetic tape library. Besides this long term archive, it is often required to process this data as soon as possible on a high performance compute farm.

The C library LTSM (“Lightweight Tivoli Storage Management”) has been developed at the GSI IT department based on the IBM TSM software. It provides a file API that allows for writing raw listmode data files via TCP/IP sockets directly to an IBM TSM storage server. Moreover, the LTSM library offers Lustre HSM (“Hierarchical Storage Management”) capabilities for seamlessly archiving and retrieving data stored on Lustre file system and TSM server.

In spring 2019 LTSM has been employed at the FAIR Phase 0 beamtimes at GSI. For the HADES experiment LTSM was implemented into the DABC (“Data Acquisition Backbone Core”) event building software. During the 4 weeks of Ag+Ag@1.58 AGeV beam, the HADES event builders have transferred about 400 TB of data via 8 parallel 10 GbE sockets, both to the TSM archive and to the “GSI green cube” HPC farm.

For other FAIR Phase 0 experiments using the vintage MBS (“Multi Branch System”) event builders, an LTSM gateway application has been developed to connect the legacy RFIO (“Remote File I/O”) protocol of these DAQ systems with the new storage interface.
FELIX: the new detector interface for ATLAS
Authors: Panduro Vazquez William
Published: November 2020

After the current LHC shutdown (2019-2021), the ATLAS experiment will be required to operate in an increasingly harsh collision environment. To maintain physics performance, the ATLAS experiment will undergo a series of upgrades during the shutdown. A key goal of this upgrade is to improve the capacity and flexibility of the detector readout system. To this end, the FrontEnd Link eXchange (FELIX) system has been developed. FELIX acts as the interface between the data acquisition; detector control and TTC (Timing, Trigger and Control) systems; and new or updated trigger and detector front-end electronics. The system functions as a router between custom serial links from front end ASICs and FPGAs to data collection and processing components via a commodity switched network. The serial links may aggregate many slower links or be a single high bandwidth link. FELIX also forwards the LHC bunchcrossing clock, fixed latency trigger accepts and resets received from the TTC system to front-end electronics. FELIX uses commodity server technology in combination with FPGA-based PCIe I/O cards. FELIX servers run a software routing platform serving data to network clients. Commodity servers, connected to FELIX systems via the same network, run the new multi-threaded Software Readout Driver (SW ROD) infrastructure for event fragment building, buffering and detector-specific processing to facilitate online selection. This presentation will cover the design of FELIX and the results of the installation and commissioning activities for the full system.
Multi-threaded simulation for ATLAS: challenges and validation strategy
Authors: Bandieramonte Marilena , Chapman John Derek , Chiu Justin , Gray Heather , Muskinja Miha
Published: November 2020

Estimations of the CPU resources that will be needed to produce simulated data for the future runs of the ATLAS experiment at the LHC, indicate a compelling need to speed-up the process to reduce the computational time required. While different fast simulation projects are ongoing, full Geant4 based simulation will still be heavily used and is expected to consume the biggest portion of the total estimated processing time. In order to run effectively on modern architectures and profit from multi-core designs a migration of the Athena framework to a multi-threading processing model was performed. A multi-threaded simulation based on AthenaMT and Geant4MT, enables substantial decreases in the memory footprint of jobs, largely from shared geometry and cross-section tables. This approach scales better with respect to the multi-processing approach (AthenaMP) especially on the architectures that are foreseen to be used in the next LHC runs. In these proceedings we report about the status of the multi-threaded simulation in ATLAS, focusing on the different challenges of its validation process. We demonstrate the different tools and strategies that have been used for debugging multi-threaded runs versus the corresponding sequential ones, in order to have a fully reproducible and consistent simulation result.
A deep neural network method for analyzing the CMS High Granularity Calorimeter (HGCAL) events
Authors: Grasseau Gilles , Kumar Abhinav , Sartirana Andrea , Lobanov Artur , Beaudette Florian
Published: November 2020

For the High Luminosity LHC, the CMS collaboration made the ambitious choice of a high granularity design to replace the existing endcap calorimeters. Thousands of particles coming from the multiple interactions create showers in the calorimeters, depositing energy simultaneously in adjacent cells. The data are similar to 3D gray-scale image that should be properly reconstructed. In this paper, we investigate how to localize and identify the thousands of showers in such events with a Deep Neural Network model. This problem is well-known in the “Vision” domain, it belongs to the challenging class: “Object Detection”. Our project shares a lot of similarities with the ones treated in Industry but faces several technological challenges like the 3D treatment. We present the Mask R-CNN model which has already proven its efficiency in Industry (for 2D images). We also present the first results and our plans to extend it to tackle 3D HGCAL data.
Using multiple engines in the Virtual Monte Carlo package
Authors: Volkel Benedikt , Morsch Andreas , Hřivnáčová Ivana , Grosse-Oetringhaus Jan Fiete , Wenzel Sandro
Published: November 2020

The Virtual Monte Carlo (VMC) package provides a unified interface to different detector simulation transport engines such as GEANT3 and GEANT4. It has been in production use in various experiments but so far the simulation of one event was restricted to the usage of a single chosen engine. We introduce here the possibility to mix multiple engines within the simulation of a single event. Depending on user conditions the simulation is partitioned among the chosen engines, for instance to profit from each of their advantages or specific capabilities. Such conditions can depend on phase space, geometry, particle type or an arbitrary combination.

As a main achievement, this development allows for the implementation of fast simulation kernels at the VMC level which can be used stand-alone or together with full simulation engines. This capability is crucial to cope with largely increasing data expected in future LHC runs.
Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures
Authors: Berkman Sophie , Cerati Giuseppe , Gravelle Brian , Norris Boyana , Reinsvold Hall Allison , Wang Michael
Published: November 2020

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming more difficult as the detectors increase in size to reach their physics goals. In liquid argon time projection chambers (TPCs) the charged particles from neutrino interactions produce ionization electrons which drift in an electric field towards a series of collection wires, and the signal on the wires is used to reconstruct the interaction. The MicroBooNE detector currently collecting data at Fermilab has 8000 wires, and planned future experiments like DUNE will have 100 times more, which means that the time required to reconstruct an event will scale accordingly. Modernization of liquid argon TPC reconstruction code, including vectorization, parallelization and code portability to GPUs, will help to mitigate these challenges. The liquid argon TPC hit finding algorithm within the LArSoft framework used across multiple experiments has been vectorized and parallelized. This increases the speed of the algorithm on the order of ten times within a standalone version on Intel architectures. This new version has been incorporated back into LArSoft so that it can be generally used. These methods will also be applied to other low-level reconstruction algorithms of the wire signals such as the deconvolution. The applications and performance of this modernized liquid argon TPC wire reconstruction will be presented.
GRAAL: A novel package to reconstruct data of triple-GEM detectors
Authors: Farinelli Riccardo
Published: November 2020

Micro-Pattern Gas Detectors (MPGDs) are the new frontier among the gas tracking systems. Among them, the triple Gas Electron Multiplier (triple-GEM) detectors are widely used. In particular, cylindrical triple-GEM (CGEM) detectors can be used as inner tracking devices in high energy physics experiments. In this contribution, a new offline software called GRAAL (Gem Reconstruction And Analysis Library) is presented: digitization, reconstruction, alignment algorithms and analysis of the data collected with APV-25 and TIGER ASICs are reported. An innovative cluster reconstruction method based on charge centroid, micro-TPC and their merge is discussed, and the detector performance evaluated experimentally for both planar triple-GEM and CGEM prototypes.
Selective background Monte Carlo simulation at Belle II
Authors: Kahn James , Dorigatti Emilio , Lieret Kilian , Lindner Andreas , Kuhr Thomas
Published: November 2020

The large volume of data expected to be produced by the Belle II experiment presents the opportunity for studies of rare, previously inaccessible processes. Investigating such rare processes in a high data volume environment necessitates a correspondingly high volume of Monte Carlo simulations to prepare analyses and gain a deep understanding of the contributing physics processes to each individual study. This resulting challenge, in terms of computing resource requirements, calls for more intelligent methods of simulation, in particular for processes with very high background rejection rates. This work presents a method of predicting in the early stages of the simulation process the likelihood of relevancy of an individual event to the target study using graph neural networks. The results show a robust training that is integrated natively into the existing Belle II analysis software framework.
CMS Experience with Adoption of the Community supported DD4hep Toolkit
Authors: Vuosalo Carl , Banerjee Sunanda , Frank Markus , Ivanchenko Vladimir , Lo Meo Sergio , Osborne Ianna , Vargas Hernandez Andres
Published: November 2020

DD4hep is an open-source software toolkit that provides comprehensive and complete generic detector descriptions for high energy physics (HEP) detectors. The Compact Muon Solenoid collaboration (CMS) has recently evaluated and adopted DD4hep to replace its custom detector description software. CMS has demanding software requirements as a very large, longrunning experiment that must support legacy geometries and study many possible upgraded detector designs of a constantly evolving detector that will be taking data for many years to come. CMS has chosen DD4hep since it is a high-quality, community-supported solution that will benefit from continuing modernization and maintenance. This presentation will discuss the issues of DD4hep adoption, the advantages and disadvantages of the various design choices, performance results, and the integration of the plugin systems from CMS and Gaudi, another open-source software framework. Recommendations about DD4hep based upon the CMS use cases will also be presented.
Geant4 performance optimization in the ATLAS experiment
Authors: Muškinja Miha , Chapman John Derek , Gray Heather
Published: November 2020

Software improvements in the ATLAS Geant4-based simulation are critical to keep up with evolving hardware and increasing luminosity. Geant4 simulation currently accounts for about 50% of CPU consumption in ATLAS and it is expected to remain the leading CPU load during Run 4 (HL-LHC upgrade) with an approximately 25% share in the most optimistic computing model. The ATLAS experiment recently developed two algorithms for optimizing Geant4 performance: Neutron Russian Roulette (NRR) and range cuts for electromagnetic processes. The NRR randomly terminates a fraction of low energy neutrons in the simulation and weights energy deposits of the remaining neutrons to maintain physics performance. Low energy neutrons typically undergo many interactions with the detector material and their path becomes uncorrelated with the point of origin. Therefore, the response of neutrons can be efficiently estimated only with a subset of neutrons. Range cuts for electromagnetic processes exploit a built-in feature of Geant4 and terminate low energy electrons that originate from physics processes including conversions, the photoelectric effect, and Compton scattering. Both algorithms were tuned to maintain physics performance in ATLAS and together they bring about a 20% speed-up of the ATLAS Geant4 simulation. Additional ideas for improvements, currently under investigation, will also be discussed in this paper. Lastly, this paper presents how the ATLAS experiment utilizes software packages such as Intel’s VTune to identify and resolve hot-spots in simulation.
Lightweight Grid Computing for Small Group Use Cases
Authors: Zhang Aiqiang , Deng Xu , Zhou Qimin , Xu Benda
Published: November 2020

Grid computing provides important data analysis infrastructure for many physics experiments. Small groups may only have several hosts that may exist in diﬀerent private isolated networks. The existing solutions are complex or heavy to run for small groups. Our project focuses on small-scale grid computing, using several oﬃcial Debian packages, to construct the whole system. The system is designed to be lightweight and scalable.
Distributed Computing Software and Data Access Patterns in OSG Midscale Collaborations
Authors: Paschos Pascal , Riedel Benedikt , Rynge Mats , Bryant Lincoln , Stephen Judith , Gardner Robert , Fajardo Edgar , Hicks John , Wuerthwein Frank , Clark James
Published: November 2020

In this paper we showcase the support in Open Science Grid (OSG) of Midscale collaborations, the region of computing and storage scale where multi-institutional researchers collaborate to execute their science workflows on the grid without having dedicated technical support teams of their own. Collaboration Services enables such collaborations to take advantage of the distributed resources of the Open Science Grid by facilitating access to submission hosts, the deployment of their applications and supporting their data management requirements. Distributed computing software adopted from large scale collaborations, such as CVMFS, Rucio, xCache lower the barrier of intermediate scale research to integrate with existing infrastructure.
Evolution of the CMS Global Submission Infrastructure for the HL-LHC Era
Authors: Pérez-Calero Yzquierdo Antonio , Acosta Flechas Maria , Davila Foyo Diego , Haleem Saqib , Hurtado Anampa Kenyi , Ivanov Todor Trendafilov , Khan Farrukh Aftab , Kizinevič Edita , Larson Krista , Letts James , Mascheroni Marco , Mason David
Published: November 2020

Efforts in distributed computing of the CMS experiment at the LHC at CERN are now focusing on the functionality required to fulfill the projected needs for the HL-LHC era. Cloud and HPC resources are expected to be dominant relative to resources provided by traditional Grid sites, being also much more diverse and heterogeneous. Handling their special capabilities or limitations and maintaining global flexibility and efficiency, while also operating at scales much higher than the current capacity, are the major challenges being addressed by the CMS Submission Infrastructure team. These proceedings discuss the risks to the stability and scalability of the CMS HTCondor infrastructure extrapolated to such a scenario, thought to be derived mostly from its growing complexity, with multiple Negotiators and schedulers flocking work to multiple federated pools. New mechanisms for enhanced customization and control over resource allocation and usage, mandatory in this future scenario, are also described.
Building an IRIS Trust Framework
Authors: Crooks David , Neilson Ian , Kelsey David P. , Collier Ian
Published: November 2020

IRIS is the co-ordinating body of a UK science eInfrastructure and is a collaboration between UKRI-STFC, its resource providers and representatives from the science activities themselves. We describe an ongoing project to build a security policy trust framework suitable for use across the IRIS community using the AARC Policy Development Kit. Derived from existing practice, the AARC PDK aims to assist in efficiently bootstrapping a Research Infrastructure in the operation of an authentication and authorisation infrastructure in line with the AARC Blueprint Architecture, making resources accessible to researchers in an easy and secure fashion.

We document the experience gained to date by the IRIS community in adopting component policies of the AARC PDK to build a trust framework to form a foundation for resource sharing, access and trust for a national infrastructure collaboration.
DIRACOS: a cross platform solution for grid tools
Authors: Petrič Marko , Haen Christophe , Couturier Benjamin
Published: November 2020

DIRACOS is a project aimed to provide a stable base layer of dependencies on top of which the DIRAC middleware is running. The goal was to produce a coherent environment for grid interaction and streamline the operational overhead. Historically the DIRAC dependencies were grouped in two bundles; Externals containing Python and standard binary libraries, and the LCGBundle which contained all grid-related libraries (gfal, arc, etc). Such a setup proved difficult to test and hindered agile development. DIRACOS solves the binary incompatibility that was caused by using a python version newer than the native system one (SLC6). It is spawned from a single list of required packages from where we pull all dependencies down to the level of glibc using SRPMs. With such an approach we can provide a uniform set of packages for our clients, servers, and several platforms. It is an extendible setup with a DevOps development cycle in mind. The core build functionality of DIRACOS is based on Fedora Mock. DIRACOS introduces its own grammar to handle specific cases, and it also allows patching (some SRPMs require tweaking, which the user can do by providing a diff) as well as routines for pre/post/instead actions of compilation. With this approach DIRAC was able to provide a single bundle for clients and servers that is reliable, flexible, easy to test and relatively small (250 MB). It allows for a smooth transition from SLC6 to CC7 and provides a clear roadmap for possible extensions of DIRAC to a wide variety of platforms.
Distributed Computing for the Project 8 Experiment
Authors: Schram Malachi , Thomas Mathew , Fox Kevin , LaRoque Benjamin , VanDevender Brent , Oblath Noah , Cowley David
Published: November 2020

The Project 8 collaboration aims to measure the absolute neutrino mass or improve on the current limit by measuring the tritium beta decay electron spectrum. We present the current distributed computing model for the Project 8 experiment. Project 8 is in its second phase of data taking with a near continuous data rate of 1Gbps. The current computing model uses DIRAC (Distributed Infrastructure with Remote Agent Control) for its workflow and data management. A detailed meta-data assignment using the DIRAC File Catalog is used to automate raw data transfers and subsequent stages of data processing. The DIRAC system is deployed on containers managed using a Kubernetes cluster to provide a scalable infrastructure. A modified DIRAC Site Director provides the ability to submit jobs using Singularity on opportunistic High-Performance Computing (HPC) sites.
Implementation of ATLAS Distributed Computing monitoring dashboards using InfluxDB and Grafana
Authors: Beermann Thomas , Alekseev Aleksandr , Baberis Dario , Crépé-Renaudin Sabine , Elmsheuser Johannes , Glushkov Ivan , Svatos Michal , Vartapetian Armen , Vokac Petr , Wolters Helmut
Published: November 2020

For the last 10 years, the ATLAS Distributed Computing project has based its monitoring infrastructure on a set of custom designed dashboards provided by CERN. This system functioned very well for LHC Runs 1 and 2, but its maintenance has progressively become more difficult and the conditions for Run 3, starting in 2021, will be even more demanding; hence a more standard code base and more automatic operations are needed. A new infrastructure has been provided by CERN, based on InfluxDB as the data store and Grafana as the display environment. ATLAS has adapted and further developed its monitoring tools to use this infrastructure for data and workflow management monitoring and accounting dashboards, expanding the range of previous possibilities with the aim to achieve a single, simpler, environment for all monitoring applications. This document describes these tools and the data flows for monitoring and accounting.
GOCDB - new communities, new requirements, new architecture
Authors: Corbett Greg , Ryall George , Collier Ian
Published: November 2020

GOCDB is the official repository for storing and presenting EGI and WLCG topology and resource information. It is a definitive information source, with the emphasis on user communities to maintain their own data. It is intentionally designed to have no dependencies on other operational tools for information.

In recent years, funding sources and user communities have evolved and GOCDB is developing to meet the resulting new requirements, such as allowing more programmatic updates to the data within GOCDB. We will explain the roadmap for developing GOCDB and the motivation for the changes on that Roadmap.

As GOCDB, and the team supporting it, has evolved, we have re-examined the underpinning architecture for GOCDB. We will set out the changes we have made as a result.
Distributed resources of Czech WLCG Tier-2 center
Authors: Adam Martin , Adamová Dagmar , Chudoba Jiří , Mikula Alexandr , Svatoš Michal , Uhlířová Jana , Vokáč Petr
Published: November 2020

The Computing Center of the Institute of Physics (CC IoP) of the Czech Academy of Sciences provides compute and storage capacity to several physics experiments. Most resources are used by two LHC experiments, ALICE and ATLAS. In the WLCG, which coordinates computing activities for the LHC experiments, the computing center is Tier-2. The rest of computing resources is used by astroparticle experiments like the Pierre Auger Observatory (PAO) and the Cherenkov Telescope Array (CTA) or particle experiments like NOvA and DUNE. Storage capacity is distributed to several locations. DPM servers used by the ATLAS and the PAO are all in the same server room. ALICE uses several xrootd servers located at the Nuclear Physics Institute in Rez, about 10 km away. The storage capacity for the ATLAS and the PAO is extended by resources of the CESNET (the Czech National Grid Initiative representative) located in Ostrava, more than 100 km away from the CC IoP. Storage is managed by dCache instance, which is published in the CC IoP BDII. ATLAS users can use these resources using the standard ATLAS tools in the same way as the local storage without noticing this geographical distribution. The computing center provides about 8k CPU cores which are used by the experiments based on fair-share. The CPUs are distributed amongst server rooms in the Institute of Physics, in the Faculty of Mathematics and Physics of the Charles University, and in CESNET. For the ATLAS experiment, the resources are extended by opportunistic usage of the Salomon HPC provided by the Czech national HPC center IT4Innovations in Ostrava. The HPC provides 24-core nodes. The maximum number of allowed single-node jobs in the batch system is 200. The contribution of the HPC to the CPU consumption by the ATLAS experiment is about 15% on average.
China-EU scientific cooperation on JUNO distributed computing
Authors: Andronico Giuseppe
Published: November 2020

The Jiangmen Underground Neutrino Observatory (JUNO) is an underground 20 kton liquid scintillator detector being built in the south of China. Targeting an unprecedented relative energy resolution of 3% at 1 MeV, JUNO will be able to study neutrino oscillation phenomena and determine neutrino mass ordering with a statistical significance of 3-4 sigma within six years running time.

These physics challenges are addressed by a large Collaboration localized in three continents. In this context, key to the success of JUNO will be the realization of a distributed computing infrastructure to fulfill foreseen computing needs.

Computing infrastructure development is performed jointly by the Institute for High Energy Physics (IHEP) (part of Chinese Academy of Sciences (CAS)), and a number of Italian, French and Russian data centers, already part of WLCG (Worldwide LHC Computing Grid).

Upon its establishment, JUNO is expected to deliver not less than 2 PB of data per year, to be stored in the data centers throughout China and Europe. Data analysis activities will be also carried out in cooperation.

This contribution is meant to report on China-EU cooperation to design and build together the JUNO computing infrastructure and to describe its main characteristics and requirements.
Heat Prediction of High Energy Physical Data Based on LSTM Recurrent Neural Network
Authors: Cheng Zhenjing , Wang Lu , Cheng Yaodong , Chen Gang
Published: November 2020

High-energy physics computing is a typical data-intensive calculation. Each year, petabytes of data needs to be analyzed, and data access performance is increasingly demanding. The tiered storage system scheme for building a unified namespace has been widely adopted. Generally, data is stored on storage devices with different performances and different prices according to different access frequency. When the heat of the data changes, the data is then migrated to the appropriate storage tier. At present, heuristic algorithms based on artificial experience are widely used in data heat prediction. Due to the differences in computing models of different users, the accuracy of prediction is low. A method for predicting future access popularity based on file access characteristics with the help of LSTM deep learning algorithm is proposed as the basis for data migration in hierarchical storage. This paper uses the real data of high-energy physics experiment LHAASO as an example for comparative testing. The results show that under the same test conditions, the model has higher prediction accuracy and stronger applicability than existing prediction models.
The Dynafed data federator as a grid site storage element
Authors: Ebert Marcus , Berghaus Frank , Casteels Kevin , Driemel Colson , Keeble Oliver , Leavett-Brown Colin , Fernandez Galindo Fernando , Furano Fabrizio , Paterson Michael , Seuster Rolf , Sobie Randall , Tafirout Reda
Published: November 2020

The Dynafed data federator is designed to present a dynamic and unified view of a distributed file repository. We describe our use of Dynafed to construct a production-ready WLCG storage element (SE) using existing grid storage endpoints as well as object storage. Dynafed is used as the primary SE for the Canadian distributed computing cloud systems for the Belle-II experiment, where we use it in production for reading input files. We have run up to 6,000 Belle-II jobs simultaneously on distributed cloud resources, requiring the transfer of approximately 60 TB of input data per day. Similarly, we have been using a Dynafed-based SE in pre-production testing for the ATLAS experiment. We will describe the configuration of Dynafed to make it suitable as a WLCG SE. In particular, we will highlight the improvements within Dynafed that make it possible to do checksum based file verification and 3rd-party file copy via WebDAV. We will also report on a new monitoring system and an automated system that collects storage information from all endpoints.
EOS architectural evolution and strategic development directions
Authors: Bitzes Georgios , Luchetti Fabio , Manzi Andrea , Patrascoiu Mihai , Peters Andreas Joachim , Simon Michal Kamil , Sindrilaru Elvin Alin
Published: November 2020

EOS [1] is the main storage system at CERN providing hundreds of PB of capacity to both physics experiments and also regular users of the CERN infrastructure. Since its first deployment in 2010, EOS has evolved and adapted to the challenges posed by ever-increasing requirements for storage capacity, user-friendly POSIX-like interactive experience and new paradigms like collaborative applications along with sync and share capabilities.

Overcoming these challenges at various levels of the software stack meant coming up with a new architecture for the namespace subsystem, completely redesigning the EOS FUSE module and adapting the rest of the components like draining, LRU engine, file system consistency check and others, to ensure a stable and predictable performance. In this paper we detail the issues that triggered all these changes along with the software design choices that we made.

In the last part of the paper, we move our focus to the areas that need immediate improvements in order to ensure a seamless experience for the end-user along with increased over-all availability of the service. Some of these changes have far-reaching effects and are aimed at simplifying both the deployment model but more importantly the operational load when dealing with (non/)transient errors in a system managing thousands of disks.
The BNLBox Cloud Storage Service
Authors: Rind Ofer , Ito Hironori , Che Guangwei , Chou Tim , Hancock Robert , Karasawa Mizuki , Liu Zhenping , Novakov Ognian , Rao Tejas , Wu Yingzi , Zaytsev Alexandr
Published: November 2020

Large scientific data centers have recently begun providing a number of different types of data storage in order to satisfy the various needs of their users. Users with interactive accounts, for example, might want a POSIX interface for easy access to the data from their interactive machines. Grid computing sites, on the other hand, likely need to provide an X509-based storage protocol, like SRM and GridFTP, since the data management system is built upon them. Meanwhile, an experiment producing large amounts of data typically demands a service that provides archival storage for the safe keeping of their unique data. To access these various types of data, users must use specific sets of commands tailored to their respective storage, making access to their data complex and difficult. BNLBox is an attempt to provide a unified and easy to use storage service for all BNL users, to store their important documents, code and data. It is a cloud storage system with an intuitive web interface for novice users. It provides an automated synchronization feature that enables users to upload data to their cloud storage without manual intervention, freeing them to focus on analysis rather than data management software. It provides a POSIX interface for local interactive users, which simplifies data access from batch jobs as well. At the same time, it also provides users with a straightforward mechanism for archiving large data sets for later processing. The storage space can be used for both code and data within the compute job environment. This paper will describe various aspects of the BNLBox storage service.
Towards an Intelligent Data Delivery Service
Authors: Guan Wen , Maeno Tadashi , Dimitrov Gancho , Bockelman Brian Paul , Wenaus Torre , Tsulaia Vakhtang , Magini Nicolo
Published: November 2020

The ATLAS Event Streaming Service (ESS) at the LHC is an approach to preprocess and deliver data for Event Service (ES) that has implemented a fine-grained approach for ATLAS event processing. The ESS allows one to asynchronously deliver only the input events required by ES processing, with the aim to decrease data traffic over WAN and improve overall data processing throughput. A prototype of ESS was developed to deliver streaming events to fine-grained ES jobs. Based on it, an intelligent Data Delivery Service (iDDS) is under development to decouple the “cold format” and the processing format of the data, which also opens the opportunity to include the production systems of other HEP experiments. Here we will at first present the ESS model view and its motivations for iDDS system. Then we will also present the iDDS schema, architecture and the applications of iDDS.
Testing the limits of HTTPS single point third party copy transfer over the WAN
Authors: Fajardo Edgar , Bockelman Brian , Wuerthwein Frank
Published: November 2020

LHC data is constantly being moved between computing and storage sites to support analysis, processing, and simulation; this is done at a scale that is currently unique within the science community. For example, the CMS experiment on the LHC manages approximately 200PB of storage across 100 sites and, on a daily basis, moves 1PB between sites via GridFTP as primary protocol. This paper describes the performance results we have achieved by exploring alternatives to the GridFTP protocol for these data movements. In particular the HTTPS third party copy over Xrootd data servers as a possible replacement of GridFTP for LHC big data movements.
Evaluation of the ATLAS model for remote access to database resident information for LHC Run 3
Authors: Gallas Elizabeth J , Dimitrov Gancho
Published: November 2020

The ATLAS model for remote access to database resident information relies upon a limited set of dedicated and distributed Oracle database repositories complemented with the deployment of Frontier system infrastructure on the WLCG (Worldwide LHC Computing Grid). ATLAS clients with network access can get the database information they need dynamically by submitting requests to a Squid proxy cache server in the Frontier network which provides results from its cache or passes new requests along the network to launchpads co-located at one of the Oracle sites (the master Oracle database at CERN or one of the Tier 1 Oracle database replicas). Since the beginning of LHC Run 1, the system has evolved in terms of client, Squid, and launchpad optimizations but the distribution model has remained fundamentally unchanged. On the whole, the system has been broadly successful in providing data to clients with relatively few disruptions even while site databases were down due to overall redundancy. At the same time, its quantitative performance characteristics, such as the global throughput of the system, the load distribution between sites, and the constituent interactions that make up the whole, were largely unknown. But more recently, information has been collected from launchpad and Squid logs into an Elasticsearch repository which has enabled a wide variety of studies of various aspects of the system. This contribution*** will describe dedicated studies of the data collected in Elasticsearch over the previous year to evaluate the efficacy of the distribution model. Specifically, we will quantify any advantages that the redundancy of the system offers as well as related aspects such as the geographical dependence of wait times seen by clients in getting a response to its requests. These studies are essential so that during LS2 (the long shutdown between LHC Run 2 and Run 3), we can adapt the system in preparation for the expected increase in the system load in the ramp up to Run 3 operations.
Third-party transfers in WLCG using HTTP
Authors: Bockelman Brian , Ceccanti Andrea , Furano Fabrizio , Millar Paul , Litvintsev Dmitry , Forti Alessandra
Published: November 2020

Since its earliest days, the Worldwide LHC Computational Grid (WLCG) has relied on GridFTP to transfer data between sites. The announcement that Globus is dropping support of its open source Globus Toolkit (GT), which forms the basis for several FTP client and servers, has created an opportunity to reevaluate the use of FTP. HTTP-TPC, an extension to HTTP compatible with WebDAV, has arisen as a strong contender for an alternative approach.

In this paper, we describe the HTTP-TPC protocol itself, along with the current status of its support in different implementations, and the interoperability testing done within the WLCG DOMA working group’s TPC activity. This protocol also provides the first real use-case for token-based authorisation for this community. We will demonstrate the benefits of such authorisation by showing how it allows HTTP-TPC to support new technologies (such as OAuth, OpenID Connect, Macaroons and SciTokens) without changing the protocol. We will also discuss the next steps for HTTP-TPC and the plans to use the protocol for WLCG transfers.
ATLAS Data Carousel
Authors: Barisits Martin , Borodin Mikhail , Di Girolamo Alessandro , Elmsheuser Johannes , Golubkov Dmitry , Klimentov Alexei , Lassnig Mario , Maeno Tadashi , Walker Rodney , Zhao Xin
Published: November 2020

The ATLAS experiment at CERN’s LHC stores detector and simulation data in raw and derived data formats across more than 150 Grid sites world-wide, currently in total about 200PB on disk and 250PB on tape. Data have different access characteristics due to various computational workflows, and can be accessed from different media, such as remote I/O, disk cache on hard disk drives or SSDs. Also, larger data centers provide the majority of offline storage capability via tape systems. For the HighLuminosity LHC (HL-LHC), the estimated data storage requirements are several factors bigger than the present forecast of available resources, based on a flat budget assumption. On the computing side, ATLAS Distributed Computing was very successful in the last years with high performance and high throughput computing integration and in using opportunistic computing resources for the Monte Carlo simulation. On the other hand, equivalent opportunistic storage does not exist. ATLAS started the Data Carousel project to increase the usage of less expensive storage, i.e. tapes or even commercial storage, so it is not limited to tape technologies exclusively. Data Carousel orchestrates data processing between workload management, data management, and storage services with the bulk data resident on offline storage. The processing is executed by staging and promptly processing a sliding window of inputs onto faster buffer storage, such that only a small percentage of input data are available at any one time. With this project, we aim to demonstrate that this is the natural way to dramatically reduce our storage cost. The first phase of the project was started in the fall of 2018 and was related to I/O tests of the sites archiving systems. Phase II now requires a tight integration of the workload and data management systems. Additionally, the Data Carousel studies the feasibility to run multiple computing workflows from tape. The project is progressing very well and the results presented in this document will be used before the LHC Run 3.
dCache - Keeping up With the Evolution of Science
Authors: Mkrtchyan Tigran , Adeyemi Olufemi , Garonne Vincent , Litvintsev Dmitry , Millar Paul , Morschel Lea , Rossi Albert , Sahakyan Marina , Starek Jürgen , Yasar Sibel
Published: November 2020

The dCache project provides open-source software deployed internationally to satisfy ever more demanding storage requirements of various scientific communities. Its multifaceted approach provides an integrated way of supporting different use-cases with the same storage, from high throughput data ingest, through wide access and easy integration with existing systems, including event driven workflow management. With this presentation, we will show some of the recent developments that optimize data management and access to maximise the gain from stored data.
Moving the California distributed CMS XCache from bare metal into containers using Kubernetes
Authors: Fajardo Edgar , Tadel Matevz , Balcas Justas , Tadel Alja , Würthwein Frank , Davila Diego , Guiang Jonathan , Sfiligoi Igor
Published: November 2020

The University of California system maintains excellent networking between its campuses and a number of other Universities in California, including Caltech, most of them being connected at 100 Gbps. UCSD and Caltech Tier2 centers have joined their disk systems into a single logical caching system, with worker nodes from both sites accessing data from disks at either site. This successful setup has been in place for the last two years. However, coherently managing nodes at multiple physical locations is not trivial and requires an update on the operations model used. The Pacific Research Platform (PRP) provides Kubernetes resource pool spanning resources in the science demilitarized zones (DMZs) in several campuses in California and worldwide. We show how we migrated the XCache services from bare-metal deployments into containers using the PRP cluster. This paper presents the reasoning behind our hardware decisions and the experience in migrating to and operating in a mixed environment.
Configuration and scheduling of the LHCb trigger application
Authors: Matev Rosen , Nolte Niklas , Pearce Alex
Published: November 2020

For Run 3 of the Large Hadron Collider, the final stage of the LHCb experiment’s high-level trigger must process 100 GB/s of input data. This corresponds to an input rate of 1 MHz, and is an order of magnitude larger compared to Run 2. The trigger is responsible for selecting all physics signals that form part of the experiment’s broad research programme, and as such defines thousands of analysis-specific selections that together comprise tens of thousands of algorithm instances. The configuration of such a system needs to be extremely flexible to be able to handle the large number of different studies it must accommodate. However, it must also be robust and easy to understand, allowing analysts to implement and understand their own selections without the possibility of error. A Python-based system for configuring the data and control flow of the Gaudi-based trigger application is presented. It is designed to be user-friendly by using functions for modularity and removing indirection layers employed previously in Run 2. Robustness is achieved by reducing global state and instead building the data flow graph in a functional manner, whilst keeping configurability of the full call stack.