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Publication search results
found 15 matches
- 2024
- Nathan S. Nichols, J. Taylor Childers, Tyler James Burch, Laurence Field:
Improving Performance Portability of the Procedurally Generated High Energy Physics Event Generator MadGraph Using SYCL. IWOCL 2024: 8:1-8:8 - Daniel Lersch, Malachi Schram, Zhenyu Dai, Kishansingh Rajput, Xingfu Wu, N. Sato, J. Taylor Childers:
SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver. CoRR abs/2407.00051 (2024) - 2023
- Mohammad Atif, Meghna Battacharya, Paolo Calafiura, Taylor Childers, Mark Dewing, Zhihua Dong, Oliver Gutsche, Salman Habib, Kyle Knoepfel, Matti Kortelainen, Ka Hei Martin Kwok, Charles Leggett, Meifeng Lin, Vincent R. Pascuzzi, Alexei Strelchenko, Vakhtang Tsulaia, Brett Viren, Tianle Wang, Beomki Yeo, Haiwang Yu:
Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics. CoRR abs/2306.15869 (2023) - 2022
- Yuping Fan, Boyang Li, Dustin Favorite, Naunidh Singh, John Taylor Childers, Paul Rich, William E. Allcock, Michael E. Papka, Zhiling Lan:
DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing. IEEE Trans. Parallel Distributed Syst. 33(12): 4903-4917 (2022) - Andrea Valassi, J. Taylor Childers, Laurence Field, Stefan Hageböck, Walter Hopkins, Olivier Mattelaer, Nathan S. Nichols, Stefan Roiser, David Smith:
Developments in Performance and Portability for MadGraph5_aMC@NLO. CoRR abs/2210.11122 (2022) - 2021
- Yuping Fan, Zhiling Lan, J. Taylor Childers, Paul Rich, William E. Allcock, Michael E. Papka:
Deep Reinforcement Agent for Scheduling in HPC. IPDPS 2021: 807-816 - Yuping Fan, Zhiling Lan, J. Taylor Childers, Paul Rich, William E. Allcock, Michael E. Papka:
Deep Reinforcement Agent for Scheduling in HPC. CoRR abs/2102.06243 (2021) - Michael Salim, Thomas D. Uram, J. Taylor Childers, Venkat Vishwanath, Michael E. Papka:
Toward Real-time Analysis of Experimental Science Workloads on Geographically Distributed Supercomputers. CoRR abs/2105.06571 (2021) - 2019
- In Kwon Choi, Taylor Childers, Nirmal Kumar Raveendranath, Swati Mishra, Kyle Harris, Khairi Reda:
Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations. CHI 2019: 68 - Michael A. Salim, Thomas D. Uram, J. Taylor Childers, Venkatram Vishwanath, Michael E. Papka:
Balsam: Near Real-Time Experimental Data Analysis on Supercomputers. XLOOP@SC 2019: 26-31 - Michael A. Salim, Thomas D. Uram, J. Taylor Childers, Prasanna Balaprakash, Venkatram Vishwanath, Michael E. Papka:
Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows. CoRR abs/1909.08704 (2019) - 2018
- Doug Benjamin, Paolo Calafiura, Taylor Childers, Kaushik De, Alessandro Di Girolamo, Esteban Fullana, Wen Guan, Tadashi Maeno, Nicolò Magini, Paul Nilsson, Danila Oleynik, Shaojun Sun, Vakho Tsulaia, Peter van Gemmeren, Torre J. Wenaus, Wei Yang:
Fine-Grained Processing Towards HL-LHC Computing in ATLAS. eScience 2018: 338 - Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier M. Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir V. Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemyslaw Karpinski, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark S. Neubauer, Harvey B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Chatham Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata:
Machine Learning in High Energy Physics Community White Paper. CoRR abs/1807.02876 (2018) - 2017
- John T. Childers, Thomas D. Uram, Thomas J. LeCompte, Michael E. Papka, D. P. Benjamin:
Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads. Comput. Phys. Commun. 210: 54-59 (2017) - 2015
- John T. Childers, Thomas D. Uram, Thomas J. LeCompte, Michael E. Papka, D. P. Benjamin:
Adapting the serial Alpgen event generator to simulate LHC collisions on millions of parallel threads. CoRR abs/1511.07312 (2015)
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