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CERN Document Server 2,024 записей найдено  1 - 10следующийконец  перейти к записи: Поиск длился 0.65 секунд. 
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Modernizing ATLAS PanDA for a sustainable multi-experiment future / ATLAS Collaboration
In early 2024, ATLAS undertook an architectural review to evaluate the functionalities of its current components within the workflow and workload management ecosystem. Pivotal to the review was the assessment of the Production and Distributed Analysis (PanDA) system, which plays a vital role in the overall infrastructure. [...]
ATL-SOFT-SLIDE-2024-537.- Geneva : CERN, 2024 - 10 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 27th International Conference on Computing in High Energy & Nuclear Physics, Kraków, Pl, 19 - 25 Oct 2024
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A Function-as-a-Task Workflow Management Approach with PanDA and iDDS / ATLAS Collaboration
The growing complexity of high energy physics analysis often involves running various distinct applications. [...]
ATL-SOFT-PROC-2024-001. - 2024. - 7 p.
Original Communication (restricted to ATLAS) - Full text
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Distributed Machine Learning Workflow with PanDA and iDDS in LHC ATLAS / ATLAS Collaboration
Machine Learning (ML) has become one of the important tools for High Energy Physics analysis. [...]
ATL-SOFT-PROC-2023-010. - 2024 - 6.
Original Communication (restricted to ATLAS) - Full text
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Utilizing Distributed Heterogeneous Computing with PanDA in ATLAS / ATLAS Collaboration
In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of the large scientific experiments at LHC. [...]
ATL-SOFT-PROC-2023-022. - 2024 - 8.
Original Communication (restricted to ATLAS) - Full text
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Utilizing Distributed Heterogeneous Computing with PanDA in ATLAS / ATLAS Collaboration
In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of the large scientific experiments at the Large Hadron Collider (LHC). Support for such workflows has allowed users to exploit remote computing resources and service providers distributed worldwide, overcoming limitations on local resources and services. [...]
ATL-SOFT-SLIDE-2023-156.- Geneva : CERN, 2023 - 14 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023
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A Function-as-a-Task Workflow Management Approach with PanDA and iDDS / ATLAS Collaboration
The growing complexity of high energy physics analysis often involves running a large number of different tools. This demands a multi-step data processing approach, with each step requiring different resources and carrying dependencies on preceding steps. [...]
ATL-SOFT-SLIDE-2024-037.- Geneva : CERN, 2024 - 21 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 22nd International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Stony Brook, Us, 11 - 15 Mar 2024
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Distributed Machine Learning with PanDA and iDDS in LHC ATLAS / ATLAS Collaboration
Machine learning has become one of the important tools for High Energy Physics analysis. As the size of the dataset increases at the Large Hadron Collider (LHC), and at the same time the search spaces become bigger and bigger in order to exploit the physics potentials, more and more computing resources are required for processing these machine learning tasks. [...]
ATL-SOFT-SLIDE-2023-128.- Geneva : CERN, 2023 - 12 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
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The ATLAS Data Carousel Project / ATLAS Collaboration
The High Luminosity upgrade to the LHC, which aims for a ten-foldincrease in the luminosity of proton-proton collisions at an energy of 14 TeV,is expected to start operation in 2028/29, and will deliver an unprecedentedvolume of scientific data at the multi-exabyte scale. This amount of data hasto be stored and the corresponding storage system must ensure fast and reli-able data delivery for processing by scientific groups distributed all over theworld. [...]
ATL-SOFT-SLIDE-2021-128.- Geneva : CERN, 2021 - 10 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021
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intelligent Data Delivery Service (iDDS) / Bockelman, Brian Paul (University of Wisconsin Madison (US)) ; Barreiro Megino, Fernando Harald (University of Texas at Arlington (US)) ; Guan, Wen (University of Wisconsin Madison (US)) ; Lin, Fa-Hui (University of Texas at Arlington (US)) ; Maeno, Tadashi (Brookhaven National Laboratory (US)) ; Weber, Christian (Brookhaven National Laboratory (US)) ; Wenaus, Torre (Brookhaven National Laboratory (US)) ; Zhang, Rui (University of Wisconsin Madison (US)) /ATLAS Collaboration
The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. It has been designed to intelligently orchestrate workflow and data management systems, decoupling data pre-processing, delivery, and primary processing in large scale workflows. [...]
ATL-SOFT-SLIDE-2022-249.- Geneva : CERN, 2022 - 15 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 41st International Conference on High Energy Physics (ICHEP 2022), Bologna, Italy, 6 - 13 Jul 2022, pp.ATL-SOFT-SLIDE-2022-249
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An intelligent Data Delivery Service for and beyond the ATLAS Experiment / Guan, Wen (University of Wisconsin Madison (US)) ; Maeno, Tadashi (Brookhaven National Laboratory (US)) ; Bockelman, Brian Paul (University of Wisconsin Madison (US)) ; Wenaus, Torre (Brookhaven National Laboratory (US)) ; Lin, Fa-Hui (University of Texas at Arlington (US)) ; Padolski, Siarhei (Brookhaven National Laboratory (US)) ; Zhang, Rui (University of Wisconsin Madison (US)) ; Alekseev, Aleksandr (Universidad Andres Bello (CL)) ; Barreiro Megino, Fernando Harald (University of Texas at Arlington (US)) /ATLAS Collaboration
The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. iDDS has been designed to intelligently orchestrate workflow and data management systems, decoupling data pre-processing, delivery, and main processing in various workflows. [...]
ATL-SOFT-SLIDE-2021-120.- Geneva : CERN, 2021 - 11 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021
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