000619284 001__ 619284
000619284 005__ 20251124184215.0
000619284 0247_ $$2doi$$a10.1177/10943420241271017
000619284 0247_ $$2ISSN$$a1094-3420
000619284 0247_ $$2ISSN$$a1078-3482
000619284 0247_ $$2ISSN$$a1741-2846
000619284 0247_ $$2WOS$$aWOS:001307506500001
000619284 0247_ $$2altmetric$$aaltmetric:160945348
000619284 0247_ $$2openalex$$aopenalex:W4402073247
000619284 0247_ $$2arXiv$$aarXiv:2403.12179
000619284 037__ $$aPUBDB-2024-07532
000619284 041__ $$aEnglish
000619284 082__ $$a004
000619284 088__ $$2arXiv$$aarXiv:2403.12179
000619284 1001_ $$0P:(DE-HGF)0$$aMyers, Andrew$$b0$$eCorresponding author
000619284 245__ $$aAMReX and pyAMReX: Looking beyond the exascale computing project
000619284 260__ $$aThousand Oaks, Calif.$$bSage Science Press$$c2024
000619284 3367_ $$2DRIVER$$aarticle
000619284 3367_ $$2DataCite$$aOutput Types/Journal article
000619284 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1763977550_1928916
000619284 3367_ $$2BibTeX$$aARTICLE
000619284 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000619284 3367_ $$00$$2EndNote$$aJournal Article
000619284 500__ $$aWaiting for fulltext
000619284 520__ $$aAMReX is a software framework for the development of block-structured mesh applications with adaptive mesh refinement (AMR). AMReX was initially developed and supported by the AMReX Co-Design Center as part of the U.S. DOE Exascale Computing Project (ECP), and is continuing to grow post-ECP. In addition to adding new functionality and performance improvements to the core AMReX framework, we have also developed a Python binding, pyAMReX, that provides a bridge between AMReX-based application codes and the data science ecosystem. pyAMReX provides zero-copy application GPU data access for AI/ML, in situ analysis and application coupling, and enables rapid, massively parallel prototyping. In this paper we review the overall functionality of AMReX and pyAMReX, focusing on new developments, new functionality, and optimizations of key operations. We also summarize capabilities of ECP projects that used AMReX and provide an overview of new, non-ECP applications.
000619284 536__ $$0G:(DE-HGF)POF4-621$$a621 - Accelerator Research and Development (POF4-621)$$cPOF4-621$$fPOF IV$$x0
000619284 542__ $$2Crossref$$i2025-08-29$$uhttp://www.sagepub.com/licence-information-for-chorus
000619284 588__ $$aDataset connected to CrossRef, Journals: bib-pubdb1.desy.de
000619284 693__ $$0EXP:(DE-MLZ)NOSPEC-20140101$$5EXP:(DE-MLZ)NOSPEC-20140101$$eNo specific instrument$$x0
000619284 7001_ $$0P:(DE-HGF)0$$aZhang, Weiqun$$b1
000619284 7001_ $$0P:(DE-HGF)0$$aAlmgren, Ann$$b2
000619284 7001_ $$0P:(DE-HGF)0$$aAntoun, Thierry$$b3
000619284 7001_ $$0P:(DE-HGF)0$$aBell, John$$b4
000619284 7001_ $$0P:(DE-HGF)0$$aHuebl, Axel$$b5
000619284 7001_ $$0P:(DE-H253)PIP1094791$$aSinn, Alexander$$b6
000619284 77318 $$2Crossref$$3journal-article$$a10.1177/10943420241271017$$bSAGE Publications$$d2024-08-29$$n6$$p599-611$$tThe International Journal of High Performance Computing Applications$$v38$$x1094-3420$$y2024
000619284 773__ $$0PERI:(DE-600)2017480-9$$a10.1177/10943420241271017$$gVol. 38, no. 6, p. 599 - 611$$n6$$p599-611$$tThe international journal of high performance computing applications$$v38$$x1094-3420$$y2024
000619284 8564_ $$uhttps://bib-pubdb1.desy.de/record/619284/files/2403.12179v2.pdf$$yRestricted
000619284 8564_ $$uhttps://bib-pubdb1.desy.de/record/619284/files/2403.12179v2.pdf?subformat=pdfa$$xpdfa$$yRestricted
000619284 909CO $$ooai:bib-pubdb1.desy.de:619284$$pVDB
000619284 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1094791$$aDeutsches Elektronen-Synchrotron$$b6$$kDESY
000619284 9131_ $$0G:(DE-HGF)POF4-621$$1G:(DE-HGF)POF4-620$$2G:(DE-HGF)POF4-600$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bForschungsbereich Materie$$lMaterie und Technologie$$vAccelerator Research and Development$$x0
000619284 9141_ $$y2024
000619284 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-08-26
000619284 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-08-26
000619284 915__ $$0StatID:(DE-HGF)0430$$2StatID$$aNational-Konsortium$$d2024-12-27$$wger
000619284 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-27
000619284 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bINT J HIGH PERFORM C : 2022$$d2024-12-27
000619284 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-27
000619284 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2024-12-27
000619284 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2024-12-27
000619284 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-27
000619284 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2024-12-27
000619284 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-27
000619284 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2024-12-27
000619284 9201_ $$0I:(DE-H253)MPA1-20210408$$kMPA1$$lPlasma Theory and Simulations$$x0
000619284 980__ $$ajournal
000619284 980__ $$aVDB
000619284 980__ $$aUNRESTRICTED
000619284 980__ $$aI:(DE-H253)MPA1-20210408
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1006/jcph.1998.5890
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1088/0004-637X/715/2/1221
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1088/0004-637x/765/1/39
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.21105/joss.02513
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.21105/joss.05202
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1109/P3HPC49587.2019.00012$$uBeckingsale DA, Burmark J, Hornung R, et al. (2019) RAJA: portable performance for large-scale scientific applications. In: 2019 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC), Denver, CO, USA, 22-22 November 2019. 71–81.
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/0021-9991(89)90035-1
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/0021-9991(84)90073-1
000619284 999C5 $$2Crossref$$uConsortium for Python Data API Standards (2021) Python array API standard. https://data-apis.org/array-api.
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1177/10943420221121151
000619284 999C5 $$2Crossref$$uDhruv A (2024) AMReX-Bittree-Performance. https://github.com/Lab-Notebooks/AMReX-Bittree-Performance.
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/j.cpc.2022.108421
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/j.jpdc.2014.07.001
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/j.jpdc.2014.07.003
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.21105/joss.05450
000619284 999C5 $$2Crossref$$uExaEpi (2023) ExaEpi documentation. https://exaepi.readthedocs.io.
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1109/SC41404.2022.00008
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1063/1.1893366
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1038/s41586-020-2649-2
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.18429/JACoW-NAPAC2022-TUYE2
000619284 999C5 $$2Crossref$$uHuebl A, Lehe R, Zoni E, et al. (2022b) From compact plasma particle sources to advanced accelerators with modeling at exascale. https://arxiv.org/abs/2303.12873.
000619284 999C5 $$2Crossref$$uJakob W (2022) nanobind: tiny and efficient C++/Python bindings. https://github.com/wjakob/nanobind.
000619284 999C5 $$2Crossref$$uJakob W, Rhinelander J, Moldovan D (2017) pybind11 – seamless operability between C++11 and Python. https://github.com/pybind/pybind11.
000619284 999C5 $$1Kluyver T$$2Crossref$$oKluyver T Positioning and Power in Academic Publishing: Players, Agents and Agendas 2016$$tPositioning and Power in Academic Publishing: Players, Agents and Agendas$$y2016
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1145/2833157.2833162
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1177/10943420211009293
000619284 999C5 $$2Crossref$$uOkuta R, Unno Y, Nishino D, et al. (2017) CuPy: a NumPy-compatible library for NVIDIA GPU calculations Proceedings of Workshop on Machine Learning Systems (LearningSys) in the Thirty-First Annual Conference on Neural Information Processing Systems (NIPS), December 8, 2017, Long Beach, California.
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1038/s41598-023-39150-1
000619284 999C5 $$1Paszke A$$2Crossref$$oPaszke A Advances in Neural Information Processing Systems 2019$$tAdvances in Neural Information Processing Systems$$y2019
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/j.ces.2023.118614
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1006/jcph.2000.6570
000619284 999C5 $$2Crossref$$uREMORA (2023) REMORA documentation. https://remora.readthedocs.io.
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1145/3659914.3659937
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1002/we.2886
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/j.nima.2018.01.035
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1109/IPDPSW.2016.50
000619284 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1177/10943420211022811