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<!DOCTYPE HTML>
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<h2>Publications</h2>
<p>The foundation of PanDA's architecture, design, concepts, implementation, and operational insights is supported by an array of peer-reviewed publications, encompassing both journal articles and conference proceedings.</p>
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<h2>Recommended for citation</h2>
<p>Tadashi Maeno, Aleksandr Alekseev, Fernando Harald Barreiro Megino, Kaushik De, Wen Guan, Edward Karavakis, Alexei Klimentov, Tatiana Korchuganova, FaHui Lin, Paul Nilsson, Torre Wenaus, Zhaoyu Yang and Xin Zhao<br><em>PanDA: Production and Distributed Analysis System</em><br><br><em><b>Maeno, T. et al., PanDA: Production and Distributed Analysis System, Comput Softw Big Sci 8, 4 (2024)</b><br><br><a href="https://doi.org/10.1007/s41781-024-00114-3">https://doi.org/10.1007/s41781-024-00114-3</a></em></p>
<section class="bibtex">
<pre>@article{panda2023,
author = {{Maeno}, Tadashi and {Alekseev}, Aleksandr and
{Barreiro Megino}, Fernando Harald and {De}, Kaushik and
{Guan}, Wen and {Karavakis}, Edward and
{Klimentov}, Alexei and {Korchuganova}, Tatiana and
{Lin}, FaHui and {Nilsson}, Paul and {Wenaus}, Torre and
{Yang}, Zhaoyu and {Zhao}, Xin},
title = "PanDA: Production and Distributed Analysis System",
journal = "Computing and Software for Big Science",
volume = "8",
year = "2024",
month = "01",
day = "23",
issn = "2510-2044",
doi = "10.1007/s41781-024-00114-3",
url = "https://doi.org/10.1007/s41781-024-00114-3",
publisher = "Springer International Publishing"
}</pre>
</section>
</section>
<section>
<h2>Selected Conference Proceedings</h2>
<ul style="font-size:small;text-align:left;">
<li>T. Maeno et al, <em>Utilizing Distributed Heterogeneous Computing with PanDA in ATLAS</em>, to appear in EPJ Web of Conferences, 2023</li>
<li>E. Karavakis et al, <em>Integrating the PanDA Workload Management System with the Vera C. Rubin Observatory</em>, to appear in EPJ Web of Conferences, 2023</li>
<li>W. Guan et al, <em>Distributed Machine Learning with PanDA and iDDS in LHC ATLAS</em>, to appear in EPJ Web of Conferences, 2023</li>
<li>F H Barreiro Megino et al, <em>Managing the ATLAS Grid through Harvester</em>, EPJ Web of Conferences 245, 03010, 2020</li>
<li>T. Maeno et al, <em>Harvester : an edge service harvesting heterogeneous resources for ATLAS</em>, EPJ Web Conf., 214 03030, 2019</li>
<li>F H Barreiro Megino et al, <em>The Future of Distributed Computing Systems in ATLAS: Boldly Venturing Beyond Grids</em>, EPJ Web Conf., 214, 03047, 2019</li>
<li>P. Svirin et al, <em>BigPanDA: PanDA Workload Management System and its Applications beyond ATLAS</em>, EPJ Web Conf., 214, 03050, 2019</li>
<li>A. Anisenkov et al, <em>Global heterogeneous resource harvesting: the next-generation PanDA Pilot for ATLAS</em>, J. Phys.: Conf. Ser. 1085 032031, 2018</li>
<li>A Beche et al, <em>Supercomputers, Clouds and Grids powered by BigPanDA for Brain studies</em>, J. Phys.: Conf. Ser. 1085 032003, 2018</li>
<li>A A Alekseev et al, <em>ATLAS BigPanDA monitoring</em>, J. Phys.: Conf. Ser. 1085 032043, 2018</li>
<li>A A Alekseev et al, <em>Global heterogeneous resource harvesting: the next-generation PanDA Pilot for ATLAS</em>, J. Phys.: Conf. Ser. 1085 032031, 2018</li>
<li>A A Alekseev et al, <em>Building analytical platform with Big Data solutions for log files of PanDA infrastructure</em>, J. Phys.: Conf. Ser. 1015 032003, 2018</li>
<li>F H Barreiro Megino et al, <em>PanDA for ATLAS distributed computing in the next decade</em>, J. Phys.: Conf. Ser. 898 052002, 2017</li>
<li>F H Barreiro Megino et al, <em>ATLAS WORLD-cloud and networking in PanDA</em>, J. Phys.: Conf. Ser. 898 052011, 2017</li>
<li>F H Barreiro Megino et al, <em>PanDA: Exascale Federation of Resources for the ATLAS Experiment at the LHC</em>, EPJ Web of Conferences, 108, 01001, 2016</li>
<li>A Klimentov et al, <em>Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science</em>, J. Phys.: Conf. Ser. 762 012021, 2016</li>
<li>E Karavakis et al, <em>gLExec Integration with the ATLAS PanDA Workload Management System</em>, J. Phys.: Conf. Ser. 664 062022, 2015</li>
<li>K De et al, <em>Integration of PanDA workload management system with Titan supercomputer at OLCF</em>, J. Phys.: Conf. Ser. 664 092020, 2015</li>
<li>K De et al, <em>The future of PanDA in ATLAS distributed computing</em>, J. Phys.: Conf. Ser. 664 062035, 2015</li>
<li>A Klimentov et al, <em>Next Generation Workload Management System For Big Data on Heterogeneous Distributed Computing</em>, J. Phys.: Conf. Ser. 608 012040, 2015</li>
<li>P Nilsson et al, <em>Next Generation PanDA Pilot for ATLAS and Other Experiments</em>, J. Phys.: Conf. Ser. 513 032071, 2014</li>
<li>T Maeno et al, <em>Evolution of the ATLAS PanDA workload management system for exascale computational science</em>, J. Phys.: Conf. Ser. 513 032062, 2014</li>
<li>H Ito et al, <em>Development of noSQL data storage for the ATLAS PanDA Monitoring System</em>, J. Phys.: Conf. Ser. 396 0520411, 2012</li>
<li>T Maeno et al, <em>Evolution of the ATLAS PanDA Production and Distributed Analysis System</em>, J. Phys.: Conf. Ser. 396 032071, 2012</li>
<li>T Maeno et al, <em>PD2P: PanDA Dynamic Data Placement for ATLAS</em>, J. Phys.: Conf. Ser. 396 032070, 2012</li>
<li>M Potekhin, <em>Development of noSQL data storage for the ATLAS PanDA Monitoring System</em>, J. Phys.: Conf. Ser. 368 012006, 2012</li>
<li>P Nilsson et al, <em>The ATLAS PanDA Pilot in Operation</em>, J. Phys.: Conf. Ser. 331 062040, 2011</li>
<li>A Klimentov et al, <em>The ATLAS PanDA Monitoring System and its Evolution</em>, J. Phys.: Conf. Ser. 331 072058, 2011</li>
<li>T Maeno et al, <em>Overview of ATLAS PanDA Workload Management</em>, J. Phys.: Conf. Ser. 331 072024, 2011</li>
<li>P Nilsson, <em>Experience from a pilot based system for ATLAS</em>, J. Phys.: Conf. Ser. 119 062038, 2008</li>
<li>T Maeno, <em>PanDA: distributed production and distributed analysis system for ATLAS</em>, J. Phys.: Conf. Ser. 119 062036, 2008</li>
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