bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Centre Refurbishment with the aim of Energy Saving and Achieving Carbon Net Zero
Authors: Traynor Daniel , Owen R.A. , Hays Jonathan
Published: May 2024

Queen Mary University of London (QMUL) as part of the refurbishment of one of its’s data centres will install water to water heat pumps to use the heat produced by the computing servers to provide heat for the university via a district heating system. This will reduce the use of high carbon intensity natural gas heating boilers, replacing them with electricity which has a lower carbon intensity due to the contribution from wind, solar, hydroelectric, nuclear and biomass sources of power sources.

The QMUL GridPP cluster today provides 15PB of storage and over 20K jobs slots mainly devoted to the ATLAS experiment. The data centre that houses the QMUL GridPP cluster, was originally commissioned in 2004. By 2020 it was in significant need of refurbishment. The original design had a maximum power capacity of 200KW, no hot/cold aisle containment, down flow air conditioning units using refrigerant cooling and no raised floor or ceiling plenum.

The main requirements of the refurbishment are: To significantly improve the energy efficiency and reduce the carbon usage of the University; Improve the availability and reliability of the power and cooling; Increase the capacity of the facility to provide for future expansion; Provide a long term home for the GridPP cluster to support the computing needs of the LHC and other new large science experiments (SKA/LSST) into the next decade.

After taking into account the future requirements and likely funding allocation, floor space in the data centre and the space available to house the cooling equipment the following design was chosen: A total power capacity of 390KW with redundant feeds to each rack; 39 racks with an average of 10KW of power per rack (flexible up to 20KW); An enclosed hot aisle design with in row cooling units using water cooling; water to water heat pumps connected to the universities district heating system

An overview of the project, it’s status and expected benefits in power and carbon saving are presented.
Estimating the environmental impact of a large Tier 2
Authors: Bauer Daniela , Fayer Simon , Whitehouse Dan
Published: May 2024

Recent years have seen an increasing interest in the environmental impact, especially the carbon footprint, generated by the often large scale computing facilities used by the communities represented at CHEP. As this is a fairly new requirement, this information is not always readily available, especially at universities and similar institutions which do not necessarily see large scale computing provision as their core competency. We present the results of a survey of a large WLCG Tier 2 with respect to power usage and carbon footprint leveraging all sources of information currently available to us. We show that it is possible to estimate the environmental impact with respect to power usage without having to invest in dedicated monitoring equipment. Manufacturers however do not yet provide sufficient information to allow for a detailed analysis of the carbon footprint of equipment manufacture, but even with the available information it is clear that this cannot be ignored.
Experiences in deploying in-network data caches
Authors: Sim Alex , Kissel Ezra , Hazen Damian , Guok Chin
Published: May 2024

Data caches of various forms have been widely deployed in the context of commercial and research and education networks, but their common positioning at the Edge limits their utility from a network operator perspective. When deployed outside the network core, providers lack visibility to make decisions or apply traffic engineering based on data access patterns and caching node location. As an alternative, in-network caching provides a different type of content delivery network for scientific data infrastructure, supporting on-demand temporary caching service. We will describe the status of in-network caching nodes deployed within ESnet in support of the US CMS data federation. We will describe the container and networking architecture used to deploy data caches within ESnet, and update on the evolving tooling around service management lifecycle. An analysis of cache usage will also be provided along with an outlook for expanding the in-network cache footprint.
Exploring XRootD Optimisations Using Advanced Monitoring in the UK
Authors: Currie Robert , Yuan Wenlong , Themistokleous Neofytos
Published: May 2024

XRootD servers are commonplace to many parts of HEP data management and are a key component to data access and management strategies in both the WLCG and OSG. Deployments of XRootD instances across the UK have demonstrated the versatility and expandability of this data management software. As we become more reliant on these services, there is a requirement to collect low-level metrics to monitor service performance and behaviour. This presentation will share our recent experiences in the collection, monitoring and analysis of such metrics from servers at UK WLCG-Tier2 sites.

Building on work presented at VCHEP-2021 we have been collecting XRootD service metrics from various UK sites. In addition to this, we have developed novel technologies for recording and verifying metrics from our grid storage systems at the Edinburgh Tier2. Our custom tooling is integrated with our recently deployed monitoring platform. This work complements ongoing WLCG efforts focussing on capturing and analysing XRootD based traffic flow. Through capturing and analysing additional metrics we’re able to better remotely assess service health and provide insights to assist in debugging. The goal of this is to offer a centralised resource that monitors the performance and health of remote storage services across the UK.

Making use of these additional service metrics also allows us a greater insight into the performance of XRootD Proxy File Caches. By applying machine learning techniques to already collected metrics, we aim to determine if gains can potentially be made in optimising the behaviour of these caching systems in production at a UK WLCG-Tier2. The result of this potentially being the building and deployment of custom decision-making libraries for use with XRootDPFC services.
Financial Case Study on the Use of Cloud Resources in HEP Computing
Authors: Misawa Shigeki , Hollowell Christopher , Lauret Jerome , Rao Tejas , Zaytsev Alexandr
Published: May 2024

An all-inclusive analysis of costs for on-premises and public cloudbased solutions to handle the bulk of HEP computing requirements shows that dedicated on-premises deployments of compute and storage resources are still the most cost-effective. Since the advent of public cloud services, the HEP community has engaged in multiple proofs of concept to study the technical viability of using cloud resources; however, the financial viability of using cloud resources for HEP computing and storage is of greater importance. We present the results of a study comparing the cost of providing computing resources in a public cloud and a comprehensive estimate for the cost of an on-premises solution for HEP computing. Like previous studies, the fundamental conclusion is that for the bulk of HEP computing needs, on premises is significantly more cost effective than public clouds.
HEP Benchmark Suite The centralized future of WLCG benchmarking
Authors: Menéndez Borge Gonzalo
Published: May 2024

The HEPiX Benchmarking Working Group has devised a new HEPspecific benchmark, HEPscore23. This benchmark is an instance of the underlying benchmarking tool that is HEPscore, also created by the Working Group during this endeavor. HEPscore can be set up to run different HEP Workloads, autonomous production applications sourced from various HEP experiments. Through study and analysis, a subset of those workloads was elected, the minimal set that best represented HEP applications overall. To streamline the benchmarking process, this benchmarking framework includes the HEP Benchmark Suite, which facilitates the execution of HEPscore and other benchmarks, such as HEP-SPEC06, SPEC CPU 2006, and DB12. This paper elucidates the rationale behind the benchmark, and the framework created to develop it, delineates the key design considerations, diving into the motivations behind it and the flexibility and advantages that is offers as a replacement of HS06 in the WLCG and HEP communities.
Measureing Carbon NetZero and the IRISCAST Project
Authors: Owen R. A. , Hays J.
Published: May 2024

Moving towards NetZero requires robust information to enable good decision making at all levels: covering hardware procurement, workload management and operations, as well as higher level aspects encompassing grant funding processes and policy framework development. The IRISCAST project is a proofof-concept study funded as part of the UKRI DRI Net-Zero Scoping Project.

IRISCAST performed an audit of carbon costs across a multi-site heterogeneous infrastructure by collecting and analysing snapshots of actual usage across different facilities within the IRIS community (https://iris.ac.uk). Combining usage information with an analysis of the embodied carbon costs and careful mapping and consideration of the underlying assumptions resulted in an estimate of the overall carbon cost, an understanding of the key elements that contribute to the carbon cost, and the important metrics needed to measure it.

IRISCAST makes reccomendations to allow high level feedback of carbon costs to funding bodies to inform strategic decisions as well as low level feedback of carbon costs to users and user communities to drive changes in user code bases and behaviors to be more carbon efficient.

IRISCAST carbon modeling shows that estimates of carbon costs can vary by factors of 10, hence there is significant opportunity for the carbon footprint of Digital Research Infrastructures to be reduced.
Migrating the INFN-CNAF datacenter to the Bologna Tecnopolo: A status update
Authors: Cesini Daniele , Cavalli Alessandro , Chierici Andrea , Ciaschini Vincenzo , Costantini Alessandro , Dal Pra Stefano , De Girolamo Donato , dell’Agnello Luca , Donatelli Massimo , Falabella Antonio , Fattibene Enrico , Giacomini Francesco , Martelli Barbara , Michelotto Diego , Morganti Lucia , Pellegrino Carmelo , Prosperini Andrea , Ricci Pier Paolo , Sapunenko Vladimir , Scarponi Luigi , Velardo Antonio , Zani Stefano , Boccali Tommaso , Chiarelli Lorenzo
Published: May 2024

The INFN Tier1 data center is currently located in the premises of the Physics Department of the University of Bologna, where CNAF is also located. During 2023 it will be moved to the “Tecnopolo”, the new facility for research, innovation, and technological development in the same city area; the same location is also hosting Leonardo, the pre-exascale supercomputing machine managed by CINECA, co-financed as part of the EuroHPC Joint Undertaking, 4th ranked in the top500 November 2022 list. The construction of the new CNAF data center consists of two phases, corresponding to the computing requirements of LHC: Phase 1 involves an IT power of 3 MW, and Phase 2, starting from 2025, involves an IT power up to 10 MW. The new data center is designed to cope with the computing requirements of the data taking of the HL-LHC experiments, in the time spanning from 2026 to 2040 and will provide, at the same time, computing services for several other INFN experiments and projects, not only belonging to the HEP domain. The co-location with Leonardo opens wider possibilities to integrate HTC and HPC resources and the new CNAF data center will be tightly coupled with it, allowing access from a single entry point to resources located at CNAF and provided by the supercomputer. Data access from both infrastructures will be transparent to users. In this presentation we describe the new data center design, providing a status update on the migration, and we focus on the Leonardo integration showing the results of the preliminary tests to access it from the CNAF access points.
Optimization of opportunistic utilization of the ATLAS high-level trigger farm for LHC Run 3
Authors: Glushkov Ivan , Lee Chris , di Girolamo Alessandro , Walker Rodney , Gottardo Carlo Alberto
Published: May 2024

The ATLAS Trigger and Data Acquisition (TDAQ) High Level Trigger (HLT) computing farm contains 120 000 CPU cores. These resources are critical for the online selection and collection of collision data in the ATLAS experiment during LHC operation. Since 2013, during a longer period of LHC inactivity, these resources are being used for offline event simulation via the “Simulation at Point One” project (Sim@P1). With the recent start of LHC Run 3 and the flat computing budget expected in the near future, finding ways to maximize resource utilization efficiency is of paramount importance. Recent improvements in the ATLAS software stack can potentially allow the utilization of the Sim@P1 even during LHC operation for the duration of the LHC inter-fill gaps. While previous papers on the Sim@P1 project emphasized the technical implementation details, the current contribution is presenting results of a variety of tests that led to the optimal configuration of the job submission infrastructure which would allow the use of Sim@P1 during LHC Run 3.
Overcoming obstacles to IPv6 on WLCG
Authors: Babik Marian , Bly Martin , Buraglio Nick , Chown Tim , Christidis Dimitrios , Chudoba Jiri , DeMar Phil , Flix Molina José , Grigoras Costin , Hoeft Bruno , Ito Hiro , Kelsey David , Martelli Edoardo , McKee Shawn , Misa Moreira Carmen , Nandakumar Raja , Ohrenberg Kars , Prelz Francesco , Rand Duncan , Sciabà Andrea , Skirvin Tim
Published: May 2024

The transition of the Worldwide Large Hadron Collider Computing Grid (WLCG) storage services to dual-stack IPv6/IPv4 is almost complete; all Tier-1 and 94% of Tier-2 storage are IPv6 enabled. While most data transfers now use IPv6, a significant number of IPv4 transfers still occur even when both endpoints support IPv6. This paper presents the ongoing efforts of the HEPiX IPv6 working group to steer WLCG toward IPv6-only services by investigating and fixing the obstacles to the use of IPv6 and identifying cases where IPv4 is used when IPv6 is available. Removing IPv4 use is essential for the long-term agreed goal of IPv6-only access to resources within WLCG, thus eliminating the complexity and security concerns associated with dual-stack services. We present our achievements and ongoing challenges as we navigate the final stages of the transition from IPv4 to IPv6 within WLCG.
Preparing for the next WLCG Network Data Challenge: Site Network Monitoring
Authors: McKee Shawn , Andreeva Julia , Forti Alessandra
Published: May 2024

During the first WLCG Network Data Challenge in fall of 2021 (DC21) we identified shortcomings in the monitoring that impeded our ability to fully understand the results collected during the data challenge. One of the simplest missing components was site-specific network information, especially information about traffic entering and leaving any of the participating sites. Without this information, it is very difficult to understand which sites are experiencing bottlenecks or might be misconfigured or under-used based on their capacity. The WLCG Monitoring Task Force, formed at the end of 2021, was tasked with three main work areas, one of which was site network monitoring. We will describe the work carried out by the task force to enhance our knowledge of network use for WLCG by enabling site network documentation and use, the status of the deployment, and the implications for the next data challenge.
The Creation and Evolution of the US ATLAS Shared Analysis Facilities
Authors: Rind Ofer , Benjamin Douglas , Bryant Lincoln , Caramarcu Costin , Gardner Robert , Golnaraghi Farnaz , Hollowell Christopher , Hu Fengping , Jordan David , Stephen Judith , Vukotic Ilija , Yang Wei
Published: May 2024

Prior to the start of the LHC Run 3, the US ATLAS Software and Computing operations program established three shared Tier 3 Analysis Facilities (AFs). The newest AF was established at the University of Chicago in the past year, joining the existing AFs at Brookhaven National Lab and SLAC National Accelerator Lab. In this paper, we will describe both the common and unique aspects of these three AFs, and the resulting distributed facility from the user’s perspective, including how we monitor and measure the AFs. The common elements include enabling easy access via Federated ID, file sharing via EOS, provisioning of similar Jupyter environments using common Jupyter kernels and containerization, and efforts to centralize documentation and user support channels. The unique components we will cover are driven in turn by the requirements, expertise and resources at each individual site. Finally, we will highlight how the US AFs are collaborating with other ATLAS and LHC wide (IRIS-HEP and HSF) user analysis support activities, evaluating tools like ServiceX and Coffea-Casa.
A NodeJS application for XENON collaboration member management
Authors: Lee Jason , Liang Shixiao , Martinez Yvette , Tunnell Christopher
Published: May 2024

The Big Science projects common of multi-institute particle-physics collaborations generates unique needs for member management, including paper authorship tracking, shift assignments, subscription to mailing lists and access to 3rd party applications such as Github and Slack. For smaller collaborations under 200 people, often no facility for centralized member management is available and these needs are usually manually handled by long-term members despite the management becoming untenable as collaborations grow. To automate many of these tasks for the expanding XENON collaboration, we developed the XENONnT User Management Website, a web application that stores and updates data related to the collaboration members through the use of Node.js and MongoDB. We found that web frameworks are so mature and approachable such that a student can develop a good system to meet the unique needs of the collaboration. The application allows for the scheduling of shifts for members to coordinate between institutes. User manipulation of 3rd party applications are implemented using REST API integration. The XENONnT User Management Website is open source and is a show case of quick implementation of utility application using the web framework, which demonstrated the utility of web-based approaches for solving specific problems to aid the logistics of running Big Science collaborations.
DUNE Computing Tutorials
Authors: DeMuth David , Schellman Heidi , David Clare
Published: May 2024

Providing computing training to the next generation of physicists is the principal driver for a biannual multi-day training workshop hosted by the DUNE Computing Consortium. Materials are cast in a Software Carpentry’s template, and topics have included storage space, data management, LArSoft, grid job submission and monitoring. Moreover, experts provide extended breakout sessions to demonstrate the fundamentals of the unique software used in HEP analysis. Each session uses live documents for real time correspondence, and are captured on Zoom; afterwards, videos are embedded on the corresponding web-pages for review. As a GitHub repository, shared editing of the learning modules is straightforward, and provides a trusted framework to extend to other training topics in the future. An overview of the tutorials as well as the machinery used, along with survey statistics and lessons learned is presented.
Software Training Outreach In HEP
Authors: Malik Sudhir , Cordero Danelix , Elmer Peter , LaMee Adam , Cecire Ken
Published: May 2024

The NSF-funded IRIS-HEP "Training, Education & Outreach" program and QuarkNet are partnering to enable and expand software training for the high school teachers with a goal to tap, grow and diversify the talent pipeline from K-12 students for future cyberinfrastructure. The Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) is a software institute that aims to develop the state-of-the-art software cyberinfrastructure for the High Luminosity Large Hadron Collider (HL-LHC) at CERN and other planned HEP experiments of the 2020’s. QuarkNet provides professional development to K-12 physics teachers in particle physics content and teaching methods. The two projects have recently built a collaborative relationship where a well-established community of QuarkNet K-12 teachers has access to a wide training on software tools via its Data and Coding Camps supported by IRISHEP. The paper highlights the synergistic efforts and future plans.
The ALICE Service Work system
Authors: de Souza Pereira Jomar Júnior , Telesca Adriana , Günter Simão Mario , Manuel Seixas Jose , Coura Torres Rodrigo
Published: May 2024

The "A Large Ion Collider Experiment" (ALICE), one of the four large experiments at the European Organization for Nuclear Research (CERN), is responsible for studying the physics of strongly interacting matter and the quark-gluon plasma. In order to ensure the full success of ALICE operation and data taking during the Large Hadron Collider Runs 3 and 4, a list of tasks identified as Service Work is established and maintained. This concerns detector maintenance, operation, calibration, quality control, data processing and outreach, as well as coordination and managerial roles in ALICE. The ALICE Glance Service Work system is a tool developed by a cooperation between the ALICE Collaboration and several universities that serves as the link between the user interaction and thousands of database entries. This paper describes the development process of this system and its functionalities, which range from planning the entire year of work for hundreds of tasks to individually assigning these tasks to members of the collaboration.
The Virtual Research Environment: A multi-science analysis platform
Authors: Gazzarrini Elena , Garcia Garcia Enrique , Gosein Domenic , Espinal Xavier
Published: May 2024

One of the objectives of the ESCAPE collaboration, within the context of the implementation of the European Open Science Cloud, is integrating analysis workflows from Cosmology, Astrophysics and High Energy Physics into a common framework, making it easier for researchers to share data, software and analysis code. The development of the ESCAPE Science Projects (Dark Matter and Extreme Universe) relies on the implementation of the Virtual Research Environment, a prototype analysis platform deployed in the respect of FAIR data principles. The Virtual Research Environment’s main components are (1) an Authentication and Authorization layer, (2) a federated distributed storage solution, providing functionalities for data injection and replication through a Data Management and Orchestration software, (3) an interactive notebook interface that hides the complexity of the underlying infrastructure and facilitates browsing data catalogues and (4) computing processing power to run full end-to-end analyses through workflow schedulers and resource managers that also allow for pipelines’ preservation. This contribution will depict the collection of services and technologies that compose the Virtual Research Environment, hosted on a cloud infrastructure and managed by an open source, publicly available repository.
FAIR AI Models in High Energy Physics
Authors: Li Haoyang , Duarte Javier , Roy Avik , Zhu Ruike , Huerta E. A. , Diaz Daniel , Harris Philip , Kansal Raghav , Katz Daniel S. , Kavoori Ishaan H. , Kindratenko Volodymyr V. , Mokhtar Farouk , Neubauer Mark S. , Park Sang Eon , Quinnan Melissa , Rusack Roger , Zhao Zhizhen
Published: May 2024

The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, and improving data sharing to advance scientific endeavors. There is an emerging trend to adapt these principles for machine learning models—algorithms that learn from data without specific coding—and, more generally, AI models, due to AI’s swiftly growing impact on scientific and engineering sectors. In this paper, we propose a practical definition of the FAIR principles for AI models and provide a template program for their adoption. We exemplify this strategy with an implementation from high-energy physics, where a graph neural network is employed to detect Higgs bosons decaying into two bottom quarks.
Fast Inclusive Flavour Tagging at LHCb
Authors: Prouve Claire , Nolte Niklas , Hasse Christoph
Published: May 2024

The task of identifying B meson flavor at the primary interaction point in the LHCb detector is crucial for measurements of mixing and timedependent CP violation. Flavor tagging is usually done with a small number of expert systems that find important tracks to infer the B meson flavor from. Recent advances show that replacing all of those expert systems with one ML algorithm that considers all tracks in an event yields an increase in tagging power. However, training the current classifier takes a long time and it is not suitable for use in real time triggers. In this work we present a new classifier, based on the DeepSet architecture. With the right inductive bias of permutation invariance, we achieve great speedups in training (multiple hours vs 10 minutes), a factor of 4-5 speed-up in inference for use in real time environments like the trigger and less tagging asymmetry. For the first time we investigate and compare performances of these “Inclusive Flavor Taggers” on simulation of the upgraded LHCb detector for the third run of the LHC.
Fast muon simulation in the JUNO experiment with neural networks
Authors: Fang Wenxing , Li Weidong , Lin Tao
Published: May 2024

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is set to begin data taking in 2024 with the aim of determining the neutrino mass ordering (NMO) to a significance of 3 σ after 6 years of data taking. Achieving this goal requires effective background suppression, with the background induced by cosmic-ray muons being one of the most significant sources of interference in the NMO study. Accurately simulating the cosmic-ray muon background is crucial for the success of the experiment, but the sheer number of optical photons produced by the muon makes this detector simulation process extremely time-consuming using traditional methods such as Geant4. This paper presents a fast muon simulation method that employs neural networks to expedite the simulation process. Our approach achieves an order-of-magnitude speed-up in simulation time compared to Geant4, while still producing accurate results.
Machine Learning for New Physics Searches in B → K*0µ+µ− Decays
Authors: Dubey S. , Browder T. E. , Kohani S. , Mandal R. , Sibidanov A. , Sinha R. , Vahsen S.E.
Published: May 2024

We report the status of a neural network regression model trained to extract new physics (NP) parameters in Monte Carlo (MC) simulation data. We utilize a new EvtGen NP MC generator to generate B → K*0µ+µ− events according to the deviation of the Wilson Coefficient C9 from its SM value, δC9. We train a three-dimensional ResNet regression model, using images built from the angular observables and the invariant mass of the di-muon system, to extract values of δ
C9 directly from the MC data samples. This work is intended for future analyses at the Belle II experiment but may also find applicability at other experiments.
Simulating Hadronization with Machine Learning
Authors: Wilkinson Michael K.
Published: May 2024

Hadronization is an important part of physics modeling in Monte Carlo event generators, where quarks and gluons are bound into physically observable hadrons. Today’s generators rely on finely-tuned phenomenological models, such as the Lund string model; while these models have been quite successful overall, there remain phenomenological areas where they do not match data well. A machine-learning-based alternative called MLhad, intended ultimately to be data-trainable, can simulate hadronization by encoding latentspace vectors, trained to be distributed according to a user-defined distribution using the sliced-Wasserstein distance in the loss function, then decoding them. The multiplicities and cumulative kinematic distributions of pions generated with MLhad in this way match those generated using Pythia 8.

While this architecture has been successful, an alternative using normalizing flows is convenient for generating non-pion hadrons and for taking advantage of reweighting techniques to reduce computing time. Combined with new methods for reweighting the output of phenomenological models, this updated architecture should prove convenient for comparing the output of MLhad and of empirical models.
Pion/Kaon Identification at STCF DTOF Based on Classical/Quantum Convolutional Neural Network
Authors: Yao Zhipeng , Li Teng , Huang Xingtao
Published: May 2024

Particle identification (PID) is one of the most fundamental tools in various physics research conducted in collider experiments. In recent years, machine learning methods have gradually become one of the mainstream methods in the PID field of high-energy physics experiments, often providing superior performance. The emergence of quantum machine learning may potential arm a powerful new toolbox for machine learning. In this work, targeting at the π±/K± discrimination problem at the STCF experiment, a convolutional neural network (CNN) in the endcap PID system is developed. By combining the hit position and arrival time of each Cherenkov photon at the sensors, a two-dimensional pixel map is constructed as the CNN input. The preliminary results show that the CNN model has a promising performance. In addition, based on the classical CNN, a quantum convolution neural network (QCNN) is developed as well, exploring possible quantum advantages provided by quantum machine learning methods.
Vertex reconstruction with Graph Neural Network in JSNS2
Authors: Yoo Changhyun , Goh Junghwan
Published: May 2024

The JSNS2(J-PARC Sterile Neutrino Search at the J-PARC Spallation Neutron Source) experiment searches for neutrino oscillations at 24m baseline with the J-PARC’s 3 GeV 1 MW proton beam incident on a mercury target at the Materials and Life science experimental Facility (MLF). The JSNS2detector consists of three cylindrical layers, an innermost neutrino target, an intermediate gamma-catcher, and an outermost veto. The neutrino target is made of 17 tonnes of Gd-loaded LS (Gd-LS) stored in an acrylic vessel, 3.2m(D) 2.5m(H). The detector consists of a total of 120 photomultiplier tubes (PMTs), 96 PMTs for inner and 24 PMTs for veto. In JSNS2, a maximum likelihood method based on the PMT charges is used to reconstruct position and energy of the event. We introduce Static Graph Convolution Neural Network (SGCNN), which is a combined model of PointNet and Graph Neural Network (GNN). The model was trained by Monte Carlo (MC) samples, and the position and charge of 96 inner PMTs was used as the training feature.
INFN and the evolution of distributed scientific computing in Italy
Authors: Salomoni Davide , Alkhansa Ahmad , Antonacci Marica , Belluomo Patrizia , Biasotto Massimo , Carbone Luca Giovanni , Cesini Daniele , Ciangottini Diego , Ciaschini Vincenzo , Costantini Alessandro , Doria Alessandra , Donvito Giacinto , Duma Doina Cristina , Fanzago Federica , Foggetti Nadina , Fornari Federico , Giorgio Emidio Maria , Italiano Alessandro , Malatesta Giada , Martelli Barbara , Michelotto Diego , Morganti Lucia , Gasparetto Jacopo , Peco Gianluca , Pellegrino Carmelo , Rendina Andrea , Sgaravatto Massimo , Sinisi Francesco , Spiga Daniele , Spinoso Vincenzo , Spisso Bernardino , Stalio Stefano , Strizzolo Lucio , Traldi Sergio , Verlato Marco , Vianello Enrico
Published: May 2024

INFN has been running a distributed infrastructure (the Tier-1 at Bologna-CNAF and 9 Tier-2 centres) for more than 20 years which currently offers about 150000 CPU cores and 120 PB of space both in tape and disk storage, serving more than 40 international scientific collaborations. This Grid-based infrastructure was augmented in 2019 with the INFN Cloud: a production quality multi-site federated Cloud infrastructure, composed by a core backbone, and which is able to integrate other INFN sites and public or private Clouds as well. The INFN Cloud provides a customizable and extensible portfolio offering computing and storage services spanning the IaaS, PaaS and SaaS layers, with dedicated solutions to serve special purposes, such as ISO-certified regions for the handling of sensitive data. INFN is now revising and expanding its infrastructure to tackle the challenges expected in the next 10 years of scientific computing adopting a “cloud-first” approach, through which all the INFN data centres will be federated via the INFN Cloud middleware and integrated with key HPC centres, such as the pre-exascale Leonardo machine at CINECA. In such a process, which involves both the infrastructures and the higher level services, initiatives and projects such as the "Italian National Centre on HPC, Big Data and Quantum Computing" (funded in the context of the Italian "National Recovery and Resilience Plan") and the Bologna Technopole are precious opportunities that will be exploited to offer advanced resources and services to universities, research institutions and industry. In this paper we describe how INFN is evolving its computing infrastructure, with the ambition to create and operate a national vendorneutral, open, scalable, and flexible "datalake" able to serve much more than just INFN users and experiments.
A new portable random number generator wrapper library
Authors: Wang Tianle , Atif Mohammad , Dong Zhihua , Leggett Charles , Lin Meifeng
Published: May 2024

Random number generator is an important component of many scientific projects. Many projects are written using programming models (like OpenMP and SYCL) to target different architectures. However, some programming models do not provide a random number generator. In this work, we introduce our random number generator wrapper. It is a header-only library that supports three distributions of random numbers: uniform, normal, and poisson. On the GPU backend, it wraps the cuRAND and rocRAND library, and supports various random number engines. It also wraps random123, a counterbased random number generator, on both CPU and GPU. With this library, we can generate random numbers with a few lines of code and target both GPU and multi-thread CPU with the same code. We also investigate the performance and scalability of this wrapper on different architectures with different engines and the number of cores.
APEIRON: A Framework for High Level Programming of Dataflow Applications on Multi-FPGA Systems
Authors: Ammendola Roberto , Biagioni Andrea , Chiarini Carlotta , Ciardiello Andrea , Cretaro Paolo , Frezza Ottorino , Lo Cicero Francesca , Lonardo Alessandro , Martinelli Michele , Paolucci Pier Stanislao , Pontisso Luca , Simula Francesco , Rossi Cristian , Turisini Matteo , Vicini Piero
Published: May 2024

High Energy Physics (HEP) Trigger and Data Acquisition systems (TDAQs) need ever increasing throughput and real-time data analytics capabilities either to improve particle identification accuracy and further suppress background events in trigger systems or to perform an efficient online data reduction for trigger-less ones.

As for the requirements imposed by HEP TDAQs applications in the class of real-time dataflow processing, FPGA devices are a good fit inasmuch they can not only provide adequate compute, memory and I/O resources but also a smooth programming experience thanks to the availability of High-Level Synthesis (HLS) tools.

The main motivation for the design and development of the APEIRON framework is that the currently available HLS tools do not natively support the deployment of applications over multiple FPGA devices, which severely chokes the scalability of problems that this approach could tackle. To overcome this limitation, we envisioned APEIRON as an extension of the Xilinx Vitis framework able to support a network of FPGA devices interconnected by a lowlatency direct network as the reference execution platform.

Developers can define scalable applications, using a dataflow programming model inspired by Kahn Process Networks, that can be efficiently deployed on a multi-FPGAs system: the APEIRON communication IPs allow low-latency communication between processing tasks deployed on FPGAs, even if they are hosted on different computing nodes. Thanks to the use of HLS tools in the workflow, processing tasks are described in C++ as HLS kernels, while communication between tasks is expressed through a lightweight C++ API based on non-blockingsend()and blockingreceive()operations.
Celeritas: Accelerating Geant4 with GPUs
Authors: Johnson Seth R. , Esseiva Julien , Biondo Elliott , Canal Philippe , Demarteau Marcel , Evans Thomas , Jun Soon Yung , Lima Guilherme , Lund Amanda , Romano Paul , Tognini Stefano C.
Published: May 2024

Celeritas [1] is a new Monte Carlo (MC) detector simulation code designed for computationally intensive applications (specifically, High Lumi- nosity Large Hadron Collider (HL-LHC) simulation) on high-performance heterogeneous architectures. In the past two years Celeritas has advanced from prototyping a GPU-based single physics model in infinite medium to implementing a full set of electromagnetic (EM) physics processes in complex geometries. The current release of Celeritas, version 0.3, has incorporated full device-based navigation, an event loop in the presence of magnetic fields, and detector hit scoring. New functionality incorporates a scheduler to offload electromagnetic physics to the GPU within a Geant4-driven simulation, enabling integration of Celeritas into high energy physics (HEP) experimental frameworks such as CMSSW. On the Summit supercomputer, Celeritas performs EM physics between 6 and 32 faster using the machine’s Nvidia GPUs compared to using only CPUs. When running a multithreaded Geant4 ATLAS test beam application with full hadronic physics, using Celeritas to accelerate the EM physics results in an overall simulation speedup of 1.8–2.3× on GPU and 1.2× on CPU.
KServe inference extension for an FPGA vendor-free ecosystem
Authors: Ciangottini Diego , Bianchini Giulio , Mariotti Mirko , Spiga Daniele , Storchi Loriano , Surace Giacomo
Published: May 2024

Field Programmable Gate Arrays (FPGAs) are playing an increasingly important role in the sampling and data processing industry due to their intrinsically highly parallel architecture, low power consumption, and flexibility to execute custom algorithms. In particular, the use of FPGAs to perform Machine Learning (ML) inference is increasingly growing thanks to the development of High-Level Synthesis (HLS) projects that abstract the complexity of Hardware Description Language (HDL) programming. In this work we will describe our experience extending KServe predictors, an emerging standard for ML model inference as a service on kubernetes. This project will support a custom workflow capable of loading and serving models on-demand on top of FPGAs. A key aspect of the proposed approach is to make the firmware generation, often an obstacle to a widespread FPGA adoption, transparent. We will detail how the proposed system automates both the synthesis of the HDL code and the generation of the firmware, starting from a high-level language and user-friendly machine learning libraries. The ecosystem is then completed with the adoption of a common language for sharing user models and firmwares, that is based on a dedicated Open Container Initiative artifact definition, thus leveraging all the well established practices on managing resources on a container registry.
Madgraph5_aMC@NLO on GPUs and vector CPUs Experience with the first alpha release
Authors: Hageböck Stephan , Childers Taylor , Hopkins Walter , Mattelaer Olivier , Nichols Nathan , Roiser Stefan , Teig Jørgen , Valassi Andrea , Vuosalo Carl , Wettersten Zenny
Published: May 2024

Madgraph5_aMC@NLO is one of the most-frequently used MonteCarlo event generators at the LHC, and an important consumer of compute resources. The software has been reengineered to maintain the overall lookand-feel of the user interface while speeding up event generation on CPUs and GPUs. The most computationally intensive part, the calculation of “matrix elements”, is offloaded to new implementations optimised for GPUs and for CPU vector instructions, using event-level data parallelism. We present the work to support accelerated leading-order QCD processes, and discuss how this work is going to be released to Madgraph5_aMC@NLO’s users.
Porting ATLAS Fast Calorimeter Simulation to GPUs with Performance Portable Programming Models
Authors: Atif Mohammad , Dong Zhihua , Leggett Charles , Lin Meifeng , Tsulaia Vakhtang
Published: May 2024

FastCaloSim is a parameterized simulation of the particle energy response and of the energy distribution in the ATLAS calorimeter. It is a relatively small and self-contained package with massive inherent parallelism and captures the essence of GPU offloading via important operations like data transfer, memory initialization, floating point operations, and reduction. It was identified by the High Energy Physics Center for Computational Excellence project as a good testbed for evaluating the performance and ease of portability of programming models.

In this paper, we will discuss the results of our evaluation of the porting process to Kokkos, SYCL, Alpaka, OpenMP and std::par (nvc++), and compare performance on NVIDIA, AMD and Intel GPUs, as well as multicore CPUs.
Porting LHAASO WFCTA simulation job to ARM computing cluster
Authors: Cheng Yaosong , Bi Yujiang , Shi Jingyan , Guo Chaoqi , Yan Xiaofei
Published: May 2024

With the advancement of many large-scale high-energy physics experiments, the amount of data to be processed and analyzed has significantly increased. For example, since the start of the Large High Altitude Air Shower Observatory (LHAASO) experiment in 2020, their simulation jobs have been running on an Intel X86 cluster, producing only a fraction of the planned data for the first phase due to limited CPU resources. Therefore, it is necessary to explore and expand other computing service devices. We built an application ecosystem based on the ARM architecture to support offline data processing for high-energy physics. The main work includes porting the offline software based on LHAASO experiments to run on ARM machines, formulating data transfer and job scheduling strategies in the ARM cluster, and evaluating performance and power consumption in both Intel X86 and ARM clusters. The results show that the LHAASO simulation jobs can run correctly on the ARM computing cluster. The singlecore performance of Intel X86 CPUs is better than ARM CPUs, but for the entire server with a multicore architecture, ARM servers perform better.
Running Qiskit on ROCm Platform
Authors: Bi Yujiang , Xu Shun , Ma Yunheng
Published: May 2024

Qiskit is one of the common quantum computing frameworks and and the qiskit-aer package can accelerating quantum circuit simulation using NVIDIA GPU with the help of THRUST. AMD ROCm framework similar to CUDA, a heterogeneous computing framework supporting both the NVIDIA and AMD GPUs provides the possibility to porting Qiskit/Qiskit-Aer from CUDA platform to its own. We present the porting progress of Qiskit/QiskitAer and preliminary performance test on both NVIDA and AMD GPUs. Our results show that Qiskit/Qiskit-Aer cand work well on AMD GPUs with the help of ROCm/HIP, and has comparable performance on AMD platform.
Numerical investigation of thermal loading on stainless steel during laser powder bed fusion
Authors: Kortli Houcem Edine , Yahyaoui Houda , Ben Moussa Naoufel , Ghanem Farhat
Published: May 2024

The objective of this study is to establish a simulation approach based on finite element modeling (FEM) of the process for obtaining parts by additive manufacturing (AM). This approach makes it possible to evaluate the influence of different parameters such as inter-track, scanning speed and laser power of the "Laser Powder Bed Fusion L-PBF" process on the thermal characteristics of AISI 316L stainless steel parts. A thermal analysis is carried out taking into account the sequence of material deposition and scanning of the laser beam similar to the actual process. The simulation result, validated experimentally, made it possible to predict the distribution of the temperatures at the inter-layers and inter-tracks and the morphology of the molten pool. These results allowed us to provide physical explanations of material discontinuities, degrading the mechanical behavior and often encountered in additive manufacturing. This allows the identification of a range of volumetric density of energy (VED) leading to better continuity of matter.
Thermal insulation revolution in buildings between past and present: an updated review
Authors: Belghazdis Mohammed , El Khalfaouy Mohammed , Hachem El-Kaber
Published: May 2024

Eco-design, sustainability and energy efficiency are emerging concepts that reflect the need to design new building solutions that are environmentally friendly and reduce material and energy consumption. Indeed, the probability of a massive climate change, capable of threatening the planet’s stability, is high if the current trend continues. The purpose of this study is to elucidate the main thermal insulation materials dominating the current market as well as their origins, strengths, and weaknesses. Additionally, the study identifies the criteria that make an insulator ideal and may be competitive in the near future as well as the direction of research in order to develop advanced materials. The results of the consulted studies reveal that there is no agreement on an ideal insulator that gather all the thermal insulation requirements. Each of the categories has its own weaknesses that should be addressed in the future to overcome. As well, super-insulators remain one of the most promising materials in this market, but they require more research in order to overcome some of the weaknesses that limit their insertion and production on a large scale. Ecological materials produced from natural fibers are currently far from being the candidates that can compete with super-insulators. Thus, considerable efforts are required to make these natural fibers able to dominate the construction market. Similarly, the comparison of the thermal conductivity of the four categories confirms that super-insulators are the most effective in reducing heat transfer through walls and through thinner layers as well. Finally, in addition to the ecological aspect, the thermo-physical and mechanical properties, and so on, the insulating materials must guarantee certain percentages of acoustic insulation.