bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Popularity for Cache Eviction Algorithms using Random Forests
Authors: Chuchuk Olga , Schulz Markus
Published: May 2024

In the HEP community the prediction of Data Popularity is a topic that has been approached for many years. Nonetheless, while facing increasing data storage challenges, especially in the upcoming HL-LHC era, there is still the need for better predictive models to answer the questions of whether particular data should be kept, replicated, or deleted.

Caches have proven to be a convenient technique for partially automating storage management, potentially eliminating some of these questions. On the one hand, one can benefit even from simple cache eviction policies like LRU, on the other hand, we show that incorporation of knowledge about future access patterns has the potential to greatly improve cache performance.

In this paper, we study data popularity on the file level, where the special relation between files belonging to the same dataset could be used in addition to the standard attributes. We turn to Machine Learning algorithms, such as Random Forest, which is well suited to work with Big Data: it can be parallelized, is more lightweight and easier to interpret than Deep Neural Networks. Finally, we compare the results with standard cache eviction algorithms and the theoretical optimum.
Efficient interface to the GridKa tape storage system
Authors: Musheghyan Haykuhi , Petzold Andreas , Ressmann Doris , Konstantinov Preslav , Lobontu Dorin-Daniel , Gottmann Artur , Ambroj Pérez Samuel , Mol Xavier
Published: May 2024

Providing high performance and reliable tape storage system is GridKa’s top priority. The GridKa tape storage system was recently migrated from IBM SP to High Performance Storage System (HPSS) for LHC and non-LHC HEP experiments. These are two different tape backends and each has its own design and specifics that need to be studied thoroughly. Taking into account the features and characteristics of HPSS, a new approach has been developed for flushing and staging files to and from tape storage system. This new approach allows better optimized and efficient flush and stage operations and leads to a substantial improvement in the overall performance of the GridKa tape storage system. The efficient interface that was developed to use IBM SP is now adapted to the HPSS use-case to connect the access point from experiments to the tape storage system.

This contribution provides details on these changes and the results of the Tape Challenge 2022 within the new HPSS tape storage configuration.
Enabling Storage Business Continuity and Disaster Recovery with Ceph distributed storage
Authors: Bocchi Enrico , Lekshmanan Abhishek , Valverde Roberto , Goggin Zachary
Published: May 2024

The Storage Group in the CERN IT Department operates several Ceph storage clusters with an overall capacity exceeding 100 PB. Ceph is a crucial component of the infrastructure delivering IT services to all the users of the Organization as it provides: i) Block storage for OpenStack, ii) CephFS, used as persistent storage by containers (OpenShift and Kubernetes) and as shared filesystems by HPC clusters and iii) S3 object storage for cloud-native applications, monitoring and software distribution across the WLCG.

The Ceph infrastructure at CERN is being rationalized and restructured to allow for the implementation of a Business Continuity/Disaster Recovery plan. In this paper, we give an overview of how we transitioned from a single cluster providing block storage to multiple ones, enabling Storage Availability zones, and how block storage backups can be achieved. We also illustrate future plans for file systems backups through cback,a restic-based scalable orchestrator, and how S3 implements data immutability and provides a highly available, Multi-Data Centre object storage service.
Exploring Future Storage Options for ATLAS at the BNL/SDCC facility
Authors: Huang Qiulan , Garonne Vincent , Hancock Robert , Gamboa Carlos , Misawa Shigeki , Liu Zhenping
Published: May 2024

The ATLAS experiment is expected to deliver an unprecedented amount of scientific data in the High Luminosity(HL-LHC) era. As the demand for disk storage capacity in ATLAS continues to rise steadily, the BNL Scientific Data and Computing Center (SDCC) faces challenges in terms of cost implications for maintaining multiple disk copies and adapting to the coming ATLAS storage requirements. To address these challenges, the SDCC Storage team has undertaken a thorough analysis of the ATLAS experiment’s requirements, matching them to suitable storage options and strategies, and has explored alternatives to enhance or replace the current storage solution.

This paper aims to present the main challenges encountered while supporting big data experiments such as ATLAS. We describe the experiment’s specific requirements and priorities, particularly focusing on the critical storage system characteristics of the high-luminosity run and how the key storage components provided by the Storage team work together: the dCache disk storage system; its archival back-end, HPSS; and its OS-level backend Storage. Specifically, we investigate a novel approach to integrate Lustre and XRootD. In this setup, Lustre serves as backend storage and XRootD acts as an access layer frontend, supporting various grid access protocols. Additionally, we also describe the validation and commissioning tests, including the performance comparison between dCache and XRootd. Furthermore, we provide a performance and cost analysis comparing OpenZFS and LINUX MD RAID, evaluate different storage software stacks, and showcase stress tests conducted to validate Third Party Copy (TPC) functionality.
Scale tests of the new DUNE data pipeline
Authors: Timm Steven
Published: May 2024

In preparation for the second runs of the ProtoDUNE detectors at CERN (NP02 and NP04)[1], DUNE has established a new data pipeline for bringing the data from the EHN-1 experimental hall at CERN to primary tape storage at Fermilab and CERN, and then spreading it out to a distributed disk data store at many locations around the world. This system includes a new Ingest Daemon and a new Declaration Daemon. The Rucio[2] replica catalog, and FTS3 transport are used to transport all files. All file metadata is declared to the new MetaCat[3] metadata service. All of these new components have been successfully tested at a scale equal to the expected output of the detector data acquisition system (~2-4 GB/s), and the expected network bandwidth out of the experimental hall. We present the procedure that was used to test and the results of the test.
Quota management in dCache or making a perfectly normal file system normal
Authors: Litvintsev Dmitry , Krishnaveni Chitrapu , Meyer Svenja , Millar Paul , Mkrtchyan Tigran , Morschel Lea , Rossi Albert , Sahakyan Marina
Published: May 2024

dCache (https://dcache.org) is a highly scalable storage system providing location-independent access to data. The data are stored across multiple data servers as complete files presented to the end-user via a single-rooted namespace. From its inception, dCache has been designed as a caching disk buffer to a tertiary tape storage system with the assumption that the latter has virtually unlimited capacity. dCache can also be configured as a disk-only storage system with no tape backend. Owing to the idea that a tape resource is infinite, or purely physically limited by budget considerations, the system has never provided for any restrictions on how much data can be stored on tape. Likewise, in the disk-only configuration, the capacity of the system is only limited by the aggregate disk capacity of the data servers. In a multi-user environment, however, this has become problematic. This presentation will describe the design and implementation of a user-and group-based quota system, that allows to manage tape and disk space allocations, as part of dCache namespace.
Updates to the ATLAS Data Carousel Project
Authors: Borodin Mikhail , Cameron David , Klimentov Alexei , Korchuganova Tatiana , Lassnig Mario , Maeno Tadashi , Musheghyan Haykuhi , South David , Zhao Xin
Published: May 2024

The High Luminosity upgrade to the LHC (HL-LHC) is expected to deliver scientific data at the multi-exabyte scale. In order to address this unprecedented data storage challenge, the ATLAS experiment launched the Data Carousel project in 2018. Data Carousel is a tape-driven workflow whereby bulk production campaigns with input data resident on tape are executed by staging and promptly processing a sliding window to disk buffer such that only a small fraction of inputs are pinned on disk at any one time. Data Carousel is now in production for ATLAS in Run3. In this paper, we provide updates on recent Data Carousel R&D projects, including data-on-demand and tape smart writing. Data-on-demand removes from disk data that has not been accessed for a predefined period, when users request them, they will be either staged from tape or recreated by following the original production steps. Tape smart writing employs intelligent algorithms for file placement on tape in order to retrieve data back more efficiently, which is our long term strategy to achieve optimal tape usage in Data Carousel.
Benchmarking Data Acquisition event building network performance for the ATLAS HL-LHC upgrade
Authors: Pozo Astigarraga Mikel Eukeni , Bonaventura Matias , Maple James , Pecker Marcosig Ezequiel , Levrini Giacomo , Castro Rodrigo
Published: May 2024

The ATLAS experiment’s data acquisition (DAQ) system will be extensively updated to take full advantage of the High-Luminosity LHC (HL-LHC) upgrade, allowing it to record data at unprecedented rates. The detector will be read out at 1 MHz, generating over 5 TB/s of data. This design poses significant challenges for the Ethernet-based network, which will have to transport 20 times more data than during Run 3. The increased data rate, data sizes and number of servers will exacerbate the TCP Incast effect observed in the past, making it impossible to fully exploit the capabilities of the network and limiting the performance of the processing farm. We present exhaustive and systematic experiments to define buffering requirements in network equipment to minimise the effects of TCP Incast and reduce the impact on processing applications. Both deep and shallow buffer switches were stress-tested using DAQ traffic patterns in a test environment at approximately 10% of the expected HL-LHC DAQ system size. As the desired HL-LHC system hardware is not currently available and the laboratory size is significantly smaller, the tests aim to extrapolate buffer requirements to the expected operating point. A novel analytical formula and new simulation models have been developed to cross-validate the results. The results of these evaluations will contribute to the decision-making process for the acquisitions of network hardware for the HL-LHC DAQ.
Cluster reconstruction in the HGCAL at the Level 1 trigger
Authors: Alves Bruno
Published: May 2024

The CMS collaboration has chosen a novel High Granularity Calorimeter for the endcap regions as part of its planned upgrade for the High Luminosity LHC. The calorimeter will have fine segmentation in both the transverse and longitudinal directions, and its data will be part of the Level 1 trigger of the CMS experiment. The trigger has tight constraints on latency and rate, and will need to be implemented in hardware. The high granularity results in around six million readout channels in total, reduced to one million that are used at 40 MHz as part of the Level 1 trigger, presenting a significant challenge in terms of data manipulation and processing; the trigger data volumes will be an order of magnitude above those currently handled at CMS. In addition, the high luminosity will result in an average of 140 (or more) interactions per bunch crossing. This leads to a huge rate by background processes which must be efficiently rejected by the trigger algorithms. Furthermore, reconstruction of the particle clusters to be used for particle flow in events with high hit rates is also a complex computational problem for the trigger. The status of the cluster reconstruction algorithms developed to tackle these major challenges, as well as the associated trigger architecture, is presented. Methods developed to mitigate the known issue of cluster splitting are described, incuding an iterative algorithm which has no impact on firmware resources.
Development of the ATLAS Liquid Argon Calorimeter Readout Electronics for the HL-LHC
Authors: Ventura Gonzalez Salvador
Published: May 2024

The ATLAS Liquid Argon Calorimeter readout electronics will be upgraded to withstand higher radiation doses and harsher conditions during the HL-LHC. In this context, the upgraded readout chain will include front-end, calibration, processing, and timing control boards. The front-end boards will amplify and digitize the ionization signal with high precision and low noise. Custom made chips for amplifying, shaping and digitize the calorimeter signals have been developed, demonstrating stability under irradiation tests. Calibration boards will ensure precise calibration across all channels by using custom ASICs which will be also stable under irradiation. The digitized signal will be processed for energy estimation and time stamping purposes by using boards featuring FPGAs connected via high-speed links. To enhance their functionality, we are exploring using artificial neural networks within the FPGAs. Lastly, the timing and control system will ensure the synchronization across the different boards. The current status of the prototype boards and testing efforts are presented.
Impact of the high-level trigger for detecting long-lived particles at LHCb
Authors: Henry Louis , Jashal Brij , Kholoimov Valerii , Mendoza Diego , Oyanguren Arantza , Svintozelskyi Volodymyr , Zhuo Jiahui
Published: May 2024

Long-lived particles (LLPs) are very challenging to search for with current detectors and computing requirements due to their very displaced vertices. This study evaluates the ability of the trigger algorithms used in the Large Hadron Collider beauty (LHCb) experiment to detect long-lived particles and attempts to adapt them to enhance the sensitivity of this experiment to undiscovered long-lived particles. One of the challenges in the track reconstruction is to deal with the large amount of combinatorics of hits. A dedicated algorithm has been developed to cope with the large data output. When fully implemented, this algorithm would greatly increase the efficiency for any long-lived particle reconstruction in the forward region, for the Standard Model of particle physics and beyond.
Operational experience with the new ATLAS HLT framework for LHC Run 3
Authors: Poreba Aleksandra
Published: May 2024

Athena is the software framework used in the ATLAS experiment throughout the data processing path, from the software trigger system through offline event reconstruction to physics analysis. For Run 3 data taking (which started in 2022) the framework has been reimplemented into a multi-threaded framework. The ATLAS High Level Trigger (HLT) system has also been updated to rely to a greater extent than in Run 2 (data taking between 2015-2018) on common solutions between online and offline software. We present the now operational new HLT system, report on how the system was tested, commissioned and optimised. In addition, we show developments that have been made in tools that are used to monitor and configure the HLT, some of which are designed from scratch for Run 3.
Preliminary Performance Study of an Alternative Calorimeter Clustering Solution for Allen in LHCb
Authors: Valls Canudas Núria , Vilasis Cardona Xavier , Calvo Gómez Míriam , Golobardes Ribé Elisabet
Published: May 2024

The LHCb experiment has recently started a new period of data taking after a major upgrade in both software and hardware. One of the biggest challenges has been the migration of the first part of the trigger system into a parallel GPU architecture framework called Allen, which performs a partial reconstruction of most of the LHCb sub-detectors. In Allen, the reconstruction of the Electromagnetic Calorimeter (ECAL) sub-detector is used in many selection algorithms, but its efficiency is currently 10% lower than the full reconstruction performed in the second stage of the trigger. In this work, we present a preliminary performance study of an alternative ECAL reconstruction algorithm implemented in Allen that complements the current algorithm to maximise the reconstruction efficiency and also minimise the impact on the throughput rate.
AliECS: A New Experiment Control System for the ALICE Experiment
Authors: Mrnjavac Teo , Alexopoulos Konstantinos , Chibante Barroso Vasco , Guyot Claire , Konopka Piotr , Raduta George
Published: May 2024

The ALICE Experiment at CERN’s Large Hadron Collider (LHC) has undergone a major upgrade during LHC Long Shutdown 2 in 2019-2021, which includes a new computing system called O2 (Online-Offline). To ensure the efficient operation of the upgraded experiment and of its newly designed computing system, a reliable, high performance, full-featured experiment control system has also been developed and deployed at LHC Point 2. The ALICE Experiment Control System (AliECS) is a microservices-oriented system based on state-of-the-art cluster management technologies that emerged recently in the distributed and high-performance computing ecosystem. It is designed, developed and maintained as a comprehensive solution and single entry point for control of experiment data acquisition (up to 3.5 TB/s) and processing. This communication describes the AliECS architecture by providing an in-depth overview of the system’s components, interfaces, features, and design elements, as well as its performance. It also reports on the experience with AliECS during the first year of ALICE Run 3 data taking with LHC beam, including integration and operational challenges, and lessons learned from real-world use.
CMSSW Scaling Limits on Many-Core Machines
Authors: Jones Christopher , Gartung Patrick
Published: May 2024

Today the LHC offline computing relies heavily on CPU resources, despite the interest in compute accelerators, such as GPUs, for the longer term future. The number of cores per CPU socket has continued to increase steadily, reaching the levels of 64 cores (128 threads) with recent AMD EPYC processors, and 128 cores on Ampere Altra Max ARM processors. Over the course of the past decade, the CMS data processing framework, CMSSW, has been transformed from a single-threaded framework into a highly concurrent one. The first multithreaded version was brought into production by the start of the LHC Run 2 in 2015. Since then, the framework’s threading efficiency has gradually been improved by adding more levels of concurrency and reducing the amount of serial code paths. The latest addition was support for concurrent Runs. In this work we review the concurrency model of the CMSSW, and measure its scalability with real CMS applications, such as simulation and reconstruction, on modern many-core machines. We show metrics such as event processing throughput and application memory usage with and without the contribution of I/O, as I/O has been the major scaling limitation for the CMS applications.
EIC Software Overview
Authors: Lawrence David
Published: May 2024

Development of the EIC project detector "ePIC" is now well underway and this includes the "single software stack" used for simulation and reconstruction. The stack combines several non-experiment-specific packages including ACTS, DD4hep, JANA2, and PODIO. The software stack aims to be forward looking in the era of AI/ML and heterogeneous hardware. A formal decision making process was implemented to choose the components that involved everyone in the collaboration that was interested. This talk will present an overview of the software stack currently used for development of the ePIC detector and on which we expect to execute the experiment.
Faster simulated track reconstruction in the ATLAS Fast Chain
Authors: Leight William , Javurkova Martina , Tsai Fang-Ying , Sengupta Debajyoti , Golling Tobias , Chapman John
Published: May 2024

The production of simulated datasets for use by physics analyses consumes a large fraction of ATLAS computing resources, a problem that will only get worse as increases in the instantaneous luminosity provided by the LHC lead to more collisions per bunch crossing (pile-up). One of the more resource-intensive steps in the Monte Carlo production is reconstructing the tracks in the ATLAS Inner Detector, which takes up about 60% of the total detector reconstruction time. This talk discusses a novel technique called track overlay, which substantially speeds up the Inner Detector reconstruction. In track overlay the pile-up Inner Detector tracks are reconstructed ahead of time and overlaid onto the Inner Detector tracks from the simulated hard-scatter event. We present our implementation of this track overlay approach as part of the ATLAS Fast Chain simulation, as well as a method for deciding in which cases it is possible to use track overlay in the reconstruction of simulated data without performance degradation, and in which it is preferable to use the current approach.
Flexible, robust and minimal-overhead Event Data Model for track reconstruction in ACTS
Authors: Gessinger Paul
Published: May 2024

ACTS (A Common Tracking Software) is an experiment independent toolkit for track reconstruction, which is designed from the ground up for thread-safety and high performance. It is built to accommodate different experiment deployment scenarios, and also serves as community platform for research and development of new approaches and algorithms.

The Event Data Model (EDM) is a critical piece of the tracking library that is visible to clients. Until this point, ACTS was mostly focused on an internal EDM, targeting data interchange various components in the toolkit. This contribution reports on a new and improved client EDM for ACTS. For an experiment-agnostic toolkit like ACTS, this requires strong abstractions of potentially experiment-specific details, including event context data like sensor alignments, and tracking inputs like measurements. By applying similar abstraction strategies, the presented EDM can be an expressive, low-overhead abstraction over experiment-specific backends, and seamlessly integrates into an experiment framework and IO model.

The presented EDM includes the ACTS track class, the main data type which tracking clients interact with. It is designed to be interfaced with different IO backends, and also flexible enough to support dynamic information required by various track fitters. At the same time, careful design ensures it can seamlessly serve as a key data object in experiment reconstruction data flows.

In this contribution, the interaction of this centerpiece of the example workflows in ACTS with the standalone ROOT IO, as well as the integration with the EDM4hep package will be shown, and key performance characteristics discussed.
Full Simulation of CMS for Run-3 and Phase-2
Authors: Srimanobhas Norraphat , Banerjee Sunanda , Hahnfeld Jonas , Ivantchenko Vladimir , Krammer Natascha , Muzaffar Malik Shahzad , Pedro Kevin , Piparo Danilo
Published: May 2024

In this contribution we report the status of the CMS Geant4 simulation and the prospects for Run-3 and Phase-2. Firstly, we report about our experience during the start of Run-3 with Geant4 10.7.2, the common software package DD4hep for geometry description, and VecGeom runtime geometry library. In addition, FTFP_BERT_EMM Physics List and CMS configuration for tracking in magnetic field have been utilized. For the first time, for the Grid mass production of Monte-Carlo, this combination of components is used. Further simulation improvements are under development targeting Run-3 such as the switch to the new Geant4 11.1 in production, that provides several features important for the optimization of simulation, for example the new transportation process with built-in multiple scattering, neutron general process, custom tracking manager, G4HepEm sub-library, and others. We will present evaluation of various options, validation results, and the final choice of simulation configuration for 2023 production and beyond. The performance of the CMS full simulation for Run-2 and Run-3 will also be discussed. CMS development plan for the Phase-2 Geant4 based simulation is very ambitious, and it includes a new geometry description, physics, and simulation configurations. The progress on new detector descriptions and full simulation will be presented as well as the R&D in progress to reduce compute capacity needs.
Generalizing mkFit and its Application to HL-LHC
Authors: Cerati Giuseppe , Elmer Peter , Gartung Patrick , Giannini Leonardo , Kortelainen Matti , Krutelyov Vyacheslav , Lantz Steven , Masciovecchio Mario , Reid Tres , Reinsvold Hall Allison , Riley Daniel , Tadel Matevž , Vourliotis Emmanouil , Wittich Peter , Yagil Avi
Published: May 2024

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both threadand data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain. Prospects for an mkFit implementation of the track fit are also discussed.
Investigating mixed-precision for AGATA pulse-shape analysis
Authors: Molina Roméo , Lafage Vincent , Chamont David , Jézéquel Fabienne
Published: May 2024

The AGATA project aims at building a 4π gamma-ray spectrometer consisting of 180 germanium crystals, each crystal being divided into 36 segments. Each gamma ray produces an electrical signal within several neighbouring segments, which is compared with a data base of reference signals, enabling to locate the interaction. This step is called Pulse-Shape Analysis (PSA). In the execution chain leading to the PSA, we observe successive data conversions: the original 14-bit integers given by the electronics are finally converted to 32-bit floats. This made us wonder about the real numerical accuracy of the results, and investigate the use of shorter floats, with the hope to speedup the computation, and also reduce a major cache-miss problem. In this article, we first describe the numerical validation of the PSA code, thanks to the CADNA library. After the code being properly instrumented, CADNA performs each computation three times with a random rounding mode. This allows, for each operation, to evaluate the number of exact significant digits using a Student test with 95% confidence threshold. In a second step, we report our successes and challenges while refactoring the code so to mix different numerical formats, using high precision only when necessary, and taking benefit of hardware speedup elsewhere.
Ranking-based neural network for ambiguity resolution in ACTS
Authors: Allaire Corentin , Bouvet Françoise , Grasland Hadrien , Rousseau David
Published: May 2024

The reconstruction of particle trajectories is a key challenge of particle physics experiments, as it directly impacts particle identification and physics performances while also representing one of the main CPU consumers of many high-energy physics experiments. As the luminosity of particle colliders increases, this reconstruction will become more challenging and resourceintensive. New algorithms are thus needed to address these challenges efficiently. One potential step of track reconstruction is ambiguity resolution. In this step, performed at the end of the tracking chain, we select which tracks candidates should be kept and which must be discarded. The speed of this algorithm is directly driven by the number of track candidates, which can be reduced at the cost of some physics performance. Since this problem is fundamentally an issue of comparison and classification, we propose to use a machine learning-based approach to the Ambiguity Resolution. Using a shared-hits-based clustering algorithm, we can efficiently determine which candidates belong to the same truth particle. Afterwards, we can apply a Neural Network (NN) to compare those tracks and decide which ones are duplicates and which ones should be kept. This approach is implemented within A Common Tracking Software (ACTS) framework and tested on the Open Data Detector (ODD), a realistic virtual detector similar to a future ATLAS one. This new approach was shown to be 15 times faster than the default ACTS algorithm while removing 32 times more duplicates down to less than one duplicated track per event.
Performance of Track Reconstruction at STCF Using ACTS
Authors: Ai Xiaocong , Huang Xingtao , Li Teng , Liu Yi
Published: May 2024

The STCF physics program will provide an unique platform for studies of hadron physics, strong interactions and searches for new physics beyond the Standard Model in the τ-charm region. To deliver those physics programs, the charged particles at STCF are required to be reconstructed with high efficiency and excellent momentum resolution. In particular, charged particles with transverse momentum down to 50 MeV are required to be reconstructed. The tracking performance at STCF is studied using A Common Tracking Software (ACTS) based on the information of the STCF tracking system, a µRWELL-based inner tracker and a drift chamber. We demonstrated the first application of ACTS for a drift chamber. The implementation and tracking performance are presented.
Potentiality of automatic parameter tuning suite available in ACTS track reconstruction software framework
Authors: Garg Rocky Bala , Allaire Corentin , Salzburger Andreas , Grasland Hadrien , Tompkins Lauren , Hofgard Elyssa
Published: May 2024

Particle tracking is among the most sophisticated and complex part of the full event reconstruction chain. A number of reconstruction algorithms work in a sequence to build these trajectories from detector hits. Each of these algorithms use many configuration parameters that need to be fine-tuned to properly account for the detector/experimental setup, the available CPU budget and the desired physics performance. Few examples of such parameters include the cut values limiting the search space of the algorithm, the approximations accounting for complex phenomena or the parameters controlling algorithm performance. The most popular method to tune these parameters is hand-tuning using brute-force techniques. These techniques can be inefficient and raise issues for the long-term maintainability of such algorithms. The opensource track reconstruction software framework known as “A Common Tracking Framework (ACTS)” offers an alternative solution to these parameter tuning techniques through the use of automatic parameter optimization algorithms. ACTS come equipped with an auto-tuning suite that provides necessary setup for performing optimization of input parameters belonging to track reconstruction algorithms. The user can choose the tunable parameters in a flexible way and define a cost/benefit function for optimizing the full reconstruction chain. The fast execution speed of ACTS allows the user to run several iterations of optimization within a reasonable time bracket. The performance of these optimizers has been demonstrated on different track reconstruction algorithms such as trajectory seed reconstruction and selection, particle vertex reconstruction and generation of simplified material map, and on different detector geometries such as Generic Detector and Open Data Detector (ODD). We aim to bring this approach to all aspects of trajectory reconstruction by having a more flexible integration of tunable parameters within ACTS.
Reconstruction of Λ hyperons in an inhomogeneous magnetic field with a Kalman Filter based tracking algorithm
Authors: Parschau Mirco
Published: May 2024

The high interaction rate, fixed target experiment HADES at GSI, located in Darmstadt, Germany, investigates collisions of heavy-ion, proton and secondary pion beams with a target material. Hyperons are one of the key observables for both heavy-ion and elementary collisions [1] [2]. The challenge is to detect displaced vertices with good accuracy without having a dedicated vertex detector, by employing state-of-the-art techniques. In this contribution we discuss a newly developed tracking algorithm that uses both a Kalman Filter (KF) and the high performance KF Particle package to further boost the reconstruction performance for hyperon decays with displaced vertices [3]. With the use of the covariance matrices, which take into account effects from multiple scattering and energy loss of the particles in the material, the reconstruction performance of the tracking algorithm can be significantly improved. The KF Particle utilises these covariance matrices together with its own internal KF to reconstruct primary and secondary decay vertices.
Surface-based GPU-friendly geometry modeling for detector simulation
Authors: Apostolakis John , Cvijetic Dusan , Cosmo Gabriele , Gheata Andrei , Hahnfeld Jonas , Stan Eduard-George
Published: May 2024

In a context where the high-energy physics community strives to enhance software to handle increased data throughput, detector simulation is evolving to take advantage of new performance opportunities. Given the intricacy of particle transport simulation, recent advancements, such as adapting to accelerator hardware, require a significant research and development effort.

The feasibility of porting complex particle transport codes to GPUs has been already demonstrated by parallelizing the processing of tracks undergoing electromagnetic interactions. However, the GPU workflow presents distinct performance bottlenecks compared to the CPU workflow, owing to differences in parallelism models and hardware capabilities. Notably, the geometry component in this workflow has emerged as the primary bottleneck that needs addressing to enhance GPU-based simulations. The current CUDA-aware geometry is a 3D-solid modeler featuring substantial warp divergence, primarily due to the varying complexity of the supported 3D primitive solid shapes.

We present the outcomes of a one-year endeavor to create a bounded surface model that can seamlessly map the navigation capabilities of the existing solidbased geometry, with a focus on improving GPU efficiency. Our implementation aims to simplify low-level algorithms to reduce warp divergence, while simultaneously eliminating other sources of GPU inefficiency, such as recursive and virtual function calls, and ensuring code portability.
Accounting and monitoring tools enhancement for Run 3 in the ATLAS distributed computing
Authors: Alekseev Aleksandr , Barberis Dario , Svatos Michal
Published: May 2024

The ATLAS experiment at the LHC utilizes complex multicomponent distributed systems for processing (PanDA WMS) and managing (Rucio) data. The complexity of the relationships between components, the amount of data being processed and the continuous development of new functionalities of the critical systems are the main challenges to consider when creating monitoring and accounting tools able to adapt to this dynamic environment in a short time. To overcome these challenges, ATLAS uses the unified monitoring infrastructure (UMA) provided by CERN-IT since 2018, which accumulates information from distributed data sources and then makes it available for different ATLAS distributed computing user groups. The information is displayed using Grafana dashboards. Based on the information provided, they can be grouped as “data transfers”, “site accounting”, “jobs accounting” and so on. These monitoring tools are used daily by ATLAS members to spot and fix issues. In addition, LHC Run 3 required the implementation of significant changes in the monitoring and accounting infrastructure to collect and process data collected by ATLAS during the LHC run. This paper describes the recent enhancements to the UMA-based monitoring and accounting dashboards.
Backfilling a cloud with grid jobs
Authors: Fayer Simon , Whitehouse Dan
Published: May 2024

Imperial College London hosts a large Tier-2 WLCG grid site based around a HTCondor batch system; additionally it provides cloud computing facilities using OpenStack to non-WLCG activities. These cloud resources are open to opportunistic usage provided the impact on the primary cloud users remains low.

In common with most Tier-2 sites we see constant job pressure from the WLCG VOs, while the usage of our cloud is much more intermittent. To allocate grid jobs to the available opportunistic cloud resources we implemented a lightweight backfill system based on the OpenStack Python API.
Bringing the ATLAS HammerCloud setup to the next level with containerization
Authors: Rottler Benjamin , Böhler Michael , Duckeck Günter , Lory Alexander , Mitterer Christoph Anton , Schovancova Jaroslava
Published: May 2024

HammerCloud (HC) is a testing service and framework for continuous functional tests, on-demand large-scale stress tests, and performance benchmarks. It checks the computing resources and various components of distributed systems with realistic full-chain experiment workflows.

The HammerCloud software was initially developed in Python 2. After support for Python 2 was discontinued in 2020, migration to Python 3 became vital in order to fulfill the latest security standards and to use the new CERN Single Sign-On, which requires Python 3.

The current deployment setup based on RPMs allowed a stable deployment and secure maintenance over several years of operations for the ATLAS and CMS experiments. However, the current model is not flexible enough to support an agile and rapid development process. Therefore, we have decided to use a containerization solution, and switched to industry-standard technologies and processes. Having an “easy to spawn” instance of HC enables a more agile development cycle and easier deployment. With the help of such a containerized setup, CI/CD pipelines can be integrated into the automation process as an extra layer of verification.

A quick onboarding process for new team members and communities is essential, as there is a lot of personnel rotation and a general lack of personpower. This is achieved with the container-based setup, as developers can now work locally with a quick turnaround without needing to set up a production-like environment first. These developments empower the whole community to test and prototype new ideas and deliver new types of resources or workflows to our community.
Gravitational wave alert generation infrastructure on your laptop
Authors: Vallero Sara , De Pietri Roberto , Poulton Rhys , Chanial Pierre , Fiori Alessio , Monteleone Daniele
Published: May 2024

Multi-messenger astrophysics provides valuable insights into the properties of the physical Universe. These insights arise from the complementary information carried by photons, gravitational waves, neutrinos and cosmic rays about individual cosmic sources and source populations. When a gravitational wave (GW) candidate is identified by the Ligo, Virgo and Kagra (LVK) observatory network, an alert is sent to astronomers in order to search for electromagnetic or neutrino counterparts. The current LVK framework for alert generation consists of the Gravitational-Wave Candidate Event Database (GraceDB), which provides a centralized location for aggregating and retrieving information about candidate GW events, the SCiMMA Hopskotch server (a publishsubscribe messaging system) and GWCelery (a package for annotating and orchestrating alerts). The first two services are deployed in the Cloud (Amazon Web Services), while the latter runs on dedicated physical resources. In this work, we propose a deployment strategy for the alert generation framework as a whole, based on Kubernetes. We present a set of tools (in the form of Helm charts, Python packages and scripts) which conveniently allows running a parallel deployment of the complete infrastructure in a private Cloud for scientific computing (the Cloud at CNAF, INFN Tier-1 Computing Centre), which is currently used for integration tests. As an outcome of this work, we deliver to the community a specific configuration option for a sandboxed deployment on Minikube, which can be used to test the integration of other components (i.e. the lowlatency pipelines for the detection of the GW candidate) with the alert generation infrastructure in an isolated local environment.
Job CPU Performance comparison based on MINIAOD reading options: Local versus remote
Authors: Balcas Justas , Newman Harvey , Bhat Preeti P. , Uppalapati Sravya , Moya Andres , Iordache Catalin , Sirvinskas Raimondas
Published: May 2024

A critical challenge of performing data transfers or remote reads is to be as fast and efficient as possible while, at the same time, keeping the usage of system resources as low as possible. Ideally, the software that manages these data transfers should be able to organize them so that one can have them run up to the hardware limits. Significant portions of LHC analysis use the same datasets, running over each file or dataset multiple times. By utilizing "ondemand" based regional caches, we can improve CPU Efficiency and reduce the wide area network usage. Speeding up user analysis and reducing network usage (and hiding latency from jobs by caching most essential files on demand) are significant challenges for HL-LHC, where the data volume increases to an exabyte level. In this paper, we will describe our journey and tests with the CMS XCache project (SoCal Cache), which will compare job performance and CPU efficiency using different storage solutions (Hadoop, Ceph, Local Disk, Named Data Networking). It will also provide insights into our tests over a wide area network and possible storage and network usage savings.
Managing remote cloud resources for multiple HEP VOs with cloudscheduler
Authors: Driemel Colson , Ebert Marcus , Sobie Randall , Sullivan Tristan
Published: May 2024

Cloudscheduler is a system to manage resources of local and remote compute clouds and makes those resources available to HTCondor pools. It examines the resource needs of idle jobs, then starts virtual machines (VMs), sized accordingly, on allowed clouds with available resources. Using yaml files, cloudscheduler then provisions the VMs during the boot process with all necessary tools needed to register with HTCondor and run the experiment’s jobs. Although we have run cloudscheduler in its first version for ATLAS and Belle-II workloads successfully for more than 10 years, we developed cloudscheduler version 2 (CSV2), a complete overhaul and modernization of cloudscheduler. The new system is used successfully in production for Belle-II, ATLAS, DUNE, and BABAR. In addition to using cloudscheduler version 2 as a WLCG site, we also run it as a service for other WLCG sites, and the Canadian Advanced Network for Astronomical Research (CANFAR) group uses its own instance of CSV2 for their astronomy workloads. In this paper, we report on our experience in operating CSV2 for different experiment’s jobs, running on up to 10,000 cores across all experiments and clouds in North America, Australia, and Europe. We will also report on how to correctly account for the resource usage in the WLCG APEL system, how the monitoring works, as well as on the integration of different clouds and how to use resources opportunistically.
Scientific Community Transfer Protocols, Tools, and Their Performance Based on Network Capabilities
Authors: Sirvinskas Raimondas , Bhat Preeti , Newman Harvey , Iordache Caătălin , Balcas Justas
Published: May 2024

The efficiency of high energy physics workflows relies on the ability to rapidly transfer data among the sites where the data is processed and analyzed. The best data transfer tools should provide a simple and reliable solution for local, regional, national and in some cases intercontinental data transfers. This work outlines the results of data transfer tool tests using internal and external (simulated latency and packet loss) in 100 Gbps testbeds and compares the results among the existing solutions, while also treating the issue of tuning parameters and methods to help optimize the rates of transfers. Many tools have been developed to facilitate data transfers over wide area networks. However, few studies have shown the tools’ requirements, use cases, and reliability through comparative measurements. Here, we were evaluating a variety of high-performance data transfer tools used today in the LHC and other scientific communities, such as FDT, WDT, and NDN in different environments. Furthermore, this test was made to reproduce real-world data transfer examples to analyse each tool’s strengths and weaknesses, including the fault tolerance of the tools when we have packet loss. By comparing the tools in a controlled environment, we can shed light on the tool’s relative reliability and usability for academia and industry. Also, this work highlights the best tuning parameters for WAN and LAN transfers for maximum performance, in several cases.
The integration of heterogeneous resources in the CMS Submission Infrastructure for the LHC Run 3 and beyond
Authors: Pérez-Calero Yzquierdo Antonio , Mascheroni Marco , Kizinevic Edita , Khan Farrukh Aftab , Kim Hyunwoo , Acosta Flechas Maria , Tsipinakis Nikos , Haleem Saqib
Published: May 2024

While the computing landscape supporting LHC experiments is currently dominated by x86 processors at WLCG sites, this configuration will evolve in the coming years. LHC collaborations will be increasingly employing HPC and Cloud facilities to process the vast amounts of data expected during the LHC Run 3 and the future HL-LHC phase. These facilities often feature diverse compute resources, including alternative CPU architectures like ARM and IBM Power, as well as a variety of GPU specifications. Using these heterogeneous resources efficiently is thus essential for the LHC collaborations reaching their future scientific goals. The Submission Infrastructure (SI) is a central element in CMS Computing, enabling resource acquisition and exploitation by CMS data processing, simulation and analysis tasks. The SI must therefore be adapted to ensure access and optimal utilization of this heterogeneous compute capacity. Some steps in this evolution have been already taken, as CMS is currently using opportunistically a small pool of GPU slots provided mainly at the CMS WLCG sites. Additionally, Power9 processors have been validated for CMS production at the Marconi-100 cluster at CINECA. This note will describe the updated capabilities of the SI to continue ensuring the efficient allocation and use of computing resources by CMS, despite their increasing diversity. The next steps towards a full integration and support of heterogeneous resources according to CMS needs will also be reported.