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2 results on '"Cardoso, M Alves"'

1. Software Performance of the ATLAS Track Reconstruction for LHC Run 3

Author

Aad, G, Abbott, B, Abeling, K, Abicht, NJ, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Acharya, BS, Bourdarios, C Adam, Adamczyk, L, Adamek, L, Addepalli, SV, Addison, MJ, Adelman, J, Adiguzel, A, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Ahmad, A, Ahmadov, F, Ahmed, WS, Ahuja, S, Ai, X, Aielli, G, Aikot, A, Tamlihat, M Ait, Aitbenchikh, B, Aizenberg, I, Akbiyik, M, Åkesson, TPA, Akimov, AV, Akiyama, D, Akolkar, NN, Khoury, K Al, Alberghi, GL, Albert, J, Albicocco, P, Albouy, GL, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, F, Algren, M, Alhroob, M, Ali, B, Ali, HMJ, Ali, S, Alibocus, SW, Aliev, M, Alimonti, G, Alkakhi, W, Allaire, C, Allbrooke, BMM, Allen, JF, Flores, CA Allendes, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Estevez, M Alvarez, Fernandez, A Alvarez, Cardoso, M Alves, Alviggi, MG, Aly, M, Coutinho, Y Amaral, Ambler, A, Amelung, C, Amerl, M, Ames, CG, Amidei, D, Santos, SP Amor Dos, Amos, KR, Ananiev, V, Anastopoulos, C, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Anulli, F, Aoki, M, Aoki, T, Pozo, JA Aparisi, Aparo, MA, and Bella, L Aperio

Subjects
Information and Computing Sciences, Physical Sciences, Software Engineering, Bioengineering
Abstract

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.

Published
2024

2. Total Cost of Ownership and Evaluation of Google Cloud Resources for the ATLAS Experiment at the LHC

Author

Aad, G, Aakvaag, E, Abbott, B, Abdelhameed, S, Abeling, K, Abicht, NJ, Abidi, SH, Aboelela, M, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Acharya, BS, Ackermann, A, Bourdarios, C Adam, Adamczyk, L, Addepalli, SV, Addison, MJ, Adelman, J, Adiguzel, A, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Ahmadov, F, Ahmed, WS, Ahuja, S, Ai, X, Aielli, G, Aikot, A, Tamlihat, M Ait, Aitbenchikh, B, Akbiyik, M, Åkesson, TPA, Akimov, AV, Akiyama, D, Akolkar, NN, Aktas, S, Al Khoury, K, Alberghi, GL, Albert, J, Albicocco, P, Albouy, GL, Alderweireldt, S, Alegria, ZL, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, F, Algren, M, Alhroob, M, Ali, B, Ali, HMJ, Ali, S, Alibocus, SW, Aliev, M, Alimonti, G, Alkakhi, W, Allaire, C, Allbrooke, BMM, Allen, JF, Flores, CA Allendes, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Alsolami, ZMK, Estevez, M Alvarez, Fernandez, A Alvarez, Cardoso, M Alves, Alviggi, MG, Aly, M, Coutinho, Y Amaral, Ambler, A, Amelung, C, Amerl, M, Ames, CG, Amidei, D, Amini, B, Amirie, KJ, Dos Santos, SP Amor, Amos, KR, Amperiadou, D, An, S, Ananiev, V, Anastopoulos, C, Andeen, T, Anders, JK, Anderson, AC, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, and Antel, C

Subjects
Distributed Computing and Systems Software, Information and Computing Sciences, Generic health relevance
Abstract

Abstract: The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computing infrastructure was achieved at large scale and for an extended period of time, and hence cloud resources are shown to be an effective mechanism to provide additional, flexible computing capacity to ATLAS. For the first time a total cost of ownership analysis has been performed, to identify the dominant cost drivers and explore effective mechanisms for cost control. Network usage significantly impacts the costs of certain ATLAS workflows, underscoring the importance of implementing such mechanisms. Resource bursting has been successfully demonstrated, whilst exposing the true cost of this type of activity. A follow-up to the project is underway to investigate methods for improving the integration of cloud resources in data-intensive distributed computing environments and reducing costs related to network connectivity, which represents the primary expense when extensively utilising cloud resources.

Published
2025

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