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  1. 941
    por Nowak, Sebastian
    Publicado 2015
    “…It could be shown that the data provided by the demonstrator can be processed with a fast track reconstruction algorithm on an ARM CPU within the 6 microseconds latency of the first level ATLAS trigger anticipated for the HL-LHC.…”
    Enlace del recurso
    A new Highly Selective First Level ATLAS Muon Trigger With MDT Chamber Data for HL-LHC
  2. 942
    por Kroha, Hubert
    Publicado 2015
    “…It could be shown that the data provided by the demonstrator can be processed with a fast track reconstruction algorithm on an ARM CPU within the 6 microseconds latency of the first level ATLAS trigger anticipated for the HL-LHC.…”
    Enlace del recurso
    Test of a demonstrator of an MDT-based first-level muon Trigger for HL-LHC under realistic operating conditions
  3. 943
    por Hubacek, Zdenek
    Publicado 2016
    “…Many physics and performance studies with the ATLAS detector at the Large Hadron Collider require very large samples of simulated events, and producing these using the full GEANT4 detector simulation is highly CPU intensive. Often, a very detailed detector simulation is not needed, and in these cases fast simulation tools can be used to reduce the calorimeter simulation time. …”
    Enlace del recurso
    Enlace del recurso
    Upgrading the ATLAS fast calorimeter simulation
  4. 944
    por Filipcic, Andrej
    Publicado 2016
    “…These two centers have been the pilots for ATLAS Monte Carlo Simulation in SCEAPI and have been providing CPU power since fall 2015.…”
    Enlace del recurso
    Integration of the Chinese HPC Grid in ATLAS Distributed Computing
  5. 945
    por Novak, Tadej
    Publicado 2018
    “…This leads to significant improvements in CPU time. The contribution will discuss the technical aspects of the implementation in the ATLAS simulation and production infrastructure and compare the performance, both in terms of computing and physics, to the previous approach.…”
    Enlace del recurso
    New Techniques for Pile-up Simulation in ATLAS
  6. 946
    por Hegeman, Jeroen Guido
    Publicado 2018
    “…This paper presents the baseline design of the DAQ and HLT systems for Phase-2, taking into account the projected evolution of high speed network fabrics for event building and distribution, and the anticipated performance of general purpose CPU. In addition, some opportunities offered by reading out and processing parts of the detector data at the full LHC bunch crossing rate (40 MHz) are discussed.…”
    Enlace del recurso
    The CMS Data Acquisition System for the Phase-2 Upgrade
  7. 947
    “…ATLAS is reviewing together with CERN IT experts several typical simulation and data processing workloads for potential performance improvements in terms of memory and CPU usage, disk and network I/O. All ATLAS production and analysis grid jobs are instrumented to collect many performance metrics for detailed statistical studies using modern data analytics tools like ElasticSearch and Kibana. …”
    Enlace del recurso
    Enlace del recurso
    ATLAS grid workflow performance optimization
  8. 948
    por Novak, Tadej
    Publicado 2018
    “…This leads to significant improvements in CPU time. The contribution will discuss the technical aspects of the implementation in the ATLAS simulation and production infrastructure and compare the performance, both in terms of computing and physics, to the previous approach.…”
    Enlace del recurso
    Enlace del recurso
    New techniques for pile-up simulation in ATLAS
  9. 949
    “…It provides a significant improvement to high-momentum tracking without increasing the CPU budget. Furthermore, it allows for the verification of the expected alignment quality using high-statistics collision data. …”
    Enlace del recurso
    Enlace del recurso
    New fitting concept in ATLAS muon tracking for the LHC Run-2
  10. 950
    “…In LHCb, the accurate simulation of Cherenkov detectors takes a sizeable fraction of CPU time. An alternative approach is described here, when one generates high-level reconstructed observables using a generative neural network to bypass low level details. …”
    Enlace del recurso
    Enlace del recurso
    Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
  11. 951
    por Alnasrallah, Eissa A M A M
    Publicado 2019
    “…The validation code compares the GPU code results with the CPU’s. The current results are presented in this report along with the required future work for this process.…”
    Enlace del recurso
    Validation for CMS ECAL - Reconstruction using GPU
  12. 952
    por Kaneda, Michiru
    Publicado 2019
    “…The current system provides 7,680 CPU cores and 10.56 PB disk storage for WLCG. CERN plans the high-luminosity LHC starting from 2026, which increases the peak luminosity to 5 times compared to the present value in LHC. …”
    Enlace del recurso
    External Resources: Clouds and HPCs for the expansion of the ATLAS production system at the Tokyo regional analysis center
  13. 953
    por Krawczyk, Rafal Dominik
    Publicado 2019
    “…In this work, the RoCE high-throughput kernel bypass Ethernet-based protocol is benchmarked as an applicable technology for the event building network. CPU and memory bandwidth utilization for RoCE-based data transmissions is investigated and discussed. …”
    Enlace del recurso
    Feasibility tests of RoCE for the cluster-based event building in LHCb
  14. 954
    “…</p> <p>In order to cope with the higher data rates, both experiments are exploring alternative hardware technologies instead of traditional CPU compute farms.</p> <p>We discuss the framework design, implementation details, performance, and results of LHCb's full-software trigger on GPUs "Allen" and of ALICE's online and offline data compression and reconstruction on GPUs.…”
    Enlace del recurso
    Usage of GPU for online data processing – the experience of ALICE and LHCb
  15. 955
    por Kaneda, Michiru
    Publicado 2020
    “…The current system has 10,752 CPU cores and 16 PB disk storage. CERN plans the high-luminosity LHC starting from 2026, which increases the peak luminosity to 5 times compared to the present value in LHC. …”
    Enlace del recurso
    Enlace del recurso
    External Resources: Clouds and HPCs for the expansion of the ATLAS production system at the Tokyo regional analysis center
  16. 956
    por Svatos, Michal
    Publicado 2020
    “…The grid workflow system PanDA routinely utilizes more than 400 thousand CPU cores of those sites. The data management system Rucio manages about half an exabyte of detector and simulation data distributed among these sites. …”
    Enlace del recurso
    Enlace del recurso
    Providing the computing and data to the physicists: Overview of the ATLAS distributed computing system
  17. 957
    por Ahmed, Hasib
    Publicado 2021
    “…However, a detailed detector simulation with Geant4 is often too slow and requires too many CPU resources. For more than 10 years, ATLAS has developed and utilized tools that replace the slowest component - the calorimeter shower simulation - by faster alternatives. …”
    Enlace del recurso
    AtlFast3: Next Generation of Fast Simulation in ATLAS
  18. 958
    por Negru, Adrian-Eduard
    Publicado 2021
    “…--HTML-->CERN uses the world's largest scientific computing grid, WLCG, for distributed data storage and processing. Monitoring of the CPU and storage resources is an important and essential element to detect operational issues in its systems, for example in the storage elements, and to ensure their proper and efficient function. …”
    Enlace del recurso
    Analysis of data integrity and storage quality of a distributed storage system
  19. 959
    por Mifsud, Xandru
    Publicado 2021
    “…The primary task at hand was then to benchmark the CPU and memory performance of TMVA’s BDT implementation, and see how it fairs. …”
    Enlace del recurso
    BENCHMARKING OF ROOT BOOSTED DECISION TREES AND XGBOOST
  20. 960
    por Dos Santos Fernandes, Nuno
    Publicado 2022
    “…A more GPU parallelizable version of the Topological Clustering, called Topo-Automaton Clustering, was implemented within AthenaMT, the software framework of the ATLAS trigger, and its results were compared to those of the standard CPU algorithm to ensure physical validity is maintained. …”
    Enlace del recurso
    Enlace del recurso
    GPU acceleration of the ATLAS calorimeter clustering algorithm
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