001014307 001__ 1014307
001014307 005__ 20240313095004.0
001014307 037__ $$aFZJ-2023-03229
001014307 041__ $$aEnglish
001014307 1001_ $$0P:(DE-Juel1)191583$$aVillamar, Jose$$b0$$eCorresponding author$$ufzj
001014307 1112_ $$aHelmholtz Metadata Collaboration conference$$cVirtual$$d2022-10-05 - 2022-10-06$$wGermany
001014307 245__ $$aTracking large-scale simulations through unified metadata handling
001014307 260__ $$c2022
001014307 3367_ $$033$$2EndNote$$aConference Paper
001014307 3367_ $$2BibTeX$$aINPROCEEDINGS
001014307 3367_ $$2DRIVER$$aconferenceObject
001014307 3367_ $$2ORCID$$aCONFERENCE_POSTER
001014307 3367_ $$2DataCite$$aOutput Types/Conference Poster
001014307 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1695110044_7536$$xAfter Call
001014307 520__ $$aSimulation is an essential pillar of knowledge generation in science. The numerical models used to describe, predict, and understand real-world systems are typically complex. Consequently,  applying these models by means of simulation often poses high demands on computational resources, and requires high-performance computing (HPC) or other dedicated hardware architectures. Metadata describing the details of a numerical experiment arise at all stages of the simulation process: the conceptual description of the model, the model implementation, and the tools and machines used to run the simulation. Capturing these metadata and provenance information along the processing chain is a vital requirement for several purposes, e.g. reproducibility, benchmarking and validation, assessment of the reliability of the simulations, and data exploration [1,2]. The ability to search, share, and evaluate metadata and provenance traces from heterogeneous simulations and environments is a major challenge in provenance-driven analysis. The availability of a common metadata framework, which can be adopted by scientists from different scientific domains, would foster the meta-analysis of HPC simulation workflows [3]. Here, we develop a metadata management framework for generic HPC-based simulation research comprising concepts and tools for efficiently generating, organizing, and exploring metadata along a given simulation workflow.  The derived solutions cope with the modularity and flexibility demands of rapidly progressing science and are applicable to diverse research fields. As a proof of concept, we will apply these solutions to use cases from environmental research and computational neuroscience.[1] Guilyardi, E., et. al. (2013) doi: 10.1175/BAMS-D-11-00035.1[2] Manninen, T., et. al. (2018) doi: 10.3389/fninf.2018.00020[3] Ivie, P., & Thain, D. (2018). doi: 10.1145/3186266
001014307 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001014307 536__ $$0G:(DE-Juel-1)ZT-I-PF-3-026$$aMetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026)$$cZT-I-PF-3-026$$x1
001014307 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001014307 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x3
001014307 536__ $$0G:(DE-Juel1)jinb33_20220812$$aBrain-Scale Simulations (jinb33_20220812)$$cjinb33_20220812$$fBrain-Scale Simulations$$x4
001014307 7001_ $$0P:(DE-HGF)0$$aKelbling, Matthias$$b1
001014307 7001_ $$0P:(DE-Juel1)169778$$aTerhorst, Dennis$$b2$$ufzj
001014307 7001_ $$0P:(DE-Juel1)190225$$aMore, Heather$$b3$$ufzj
001014307 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b4$$ufzj
001014307 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b5$$ufzj
001014307 7001_ $$0P:(DE-HGF)0$$aThober, Stephan$$b6
001014307 909CO $$ooai:juser.fz-juelich.de:1014307$$pec_fundedresources$$pVDB$$popenaire
001014307 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191583$$aForschungszentrum Jülich$$b0$$kFZJ
001014307 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169778$$aForschungszentrum Jülich$$b2$$kFZJ
001014307 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190225$$aForschungszentrum Jülich$$b3$$kFZJ
001014307 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145211$$aForschungszentrum Jülich$$b4$$kFZJ
001014307 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162130$$aForschungszentrum Jülich$$b5$$kFZJ
001014307 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001014307 9141_ $$y2023
001014307 920__ $$lyes
001014307 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
001014307 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
001014307 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
001014307 9201_ $$0I:(DE-Juel1)IAS-9-20201008$$kIAS-9$$lMaterials Data Science and Informatics$$x3
001014307 980__ $$aposter
001014307 980__ $$aVDB
001014307 980__ $$aI:(DE-Juel1)INM-6-20090406
001014307 980__ $$aI:(DE-Juel1)IAS-6-20130828
001014307 980__ $$aI:(DE-Juel1)INM-10-20170113
001014307 980__ $$aI:(DE-Juel1)IAS-9-20201008
001014307 980__ $$aUNRESTRICTED
001014307 981__ $$aI:(DE-Juel1)IAS-6-20130828