tag:joss.theoj.org,2005:/papers/tagged/High-performance?page=1Journal of Open Source Software2023-12-18T18:08:54ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/45742023-12-18T18:08:54Z2023-12-19T00:00:30Zparafields: A generator for distributed, stationary Gaussian processesacceptedv0.3.02023-07-03 12:18:52 UTC922023-12-18 18:08:54 UTC820235735DominicKempfScientific Software Center, Heidelberg University, Heidelberg, Germany, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany0000-0002-6140-2332OleKleinIndependent Researcher, Heidelberg, Germany0000-0002-3295-7347RobertKutriInterdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany, Institute for Mathematics, Heidelberg University, Heidelberg, Germany0009-0004-8123-4673RobertScheichlInterdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany, Institute for Mathematics, Heidelberg University, Heidelberg, Germany0000-0001-8493-4393PeterBastianInterdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany10.21105/joss.05735https://doi.org/10.5281/zenodo.10355636Python, C++, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05735.pdfMPI, scientific computing, high performance computing, uncertainty quantification, random field generation, circulant embeddingtag:joss.theoj.org,2005:Paper/44662023-07-19T18:21:11Z2023-07-20T00:01:39Zshmem4py: OpenSHMEM for Pythonacceptedv0.12023-05-02 19:53:20 UTC872023-07-19 18:21:11 UTC820235444MarcinRogowskiKing Abdullah University of Science and Technology, Saudi Arabia0000-0002-5662-2082LisandroDalcinKing Abdullah University of Science and Technology, Saudi Arabia0000-0001-8086-0155JeffR.HammondNVIDIA Helsinki Oy, Finland0000-0003-3181-8190DavidE.KeyesKing Abdullah University of Science and Technology, Saudi Arabia0000-0002-4052-722410.21105/joss.05444https://doi.org/10.5281/zenodo.8143862Python, Chttps://joss.theoj.org/papers/10.21105/joss.05444.pdfPGAS, OpenSHMEM, shared memory, High Performance Computingtag:joss.theoj.org,2005:Paper/39792023-03-21T11:34:30Z2023-03-22T00:03:16ZHigh-performance neural population dynamics modeling enabled by scalable computational infrastructureacceptedv1.0.02022-10-26 06:03:04 UTC832023-03-21 11:34:30 UTC820235023AashishN.PatelDepartment of Electrical and Computer Engineering, University of California San Diego, United States of America, Institute for Neural Computation, University of California San Diego, United States of AmericaAndrewR.SedlerCenter for Machine Learning, Georgia Institute of Technology, United States of America, Department of Biomedical Engineering, Georgia Institute of Technology, United States of America0000-0001-9480-0698JingyaHuangDepartment of Electrical and Computer Engineering, University of California San Diego, United States of AmericaChethanPandarinathCenter for Machine Learning, Georgia Institute of Technology, United States of America, Department of Biomedical Engineering, Georgia Institute of Technology, United States of America, Department of Neurosurgery, Emory University, United States of America, These authors contributed equallyVikashGiljaDepartment of Electrical and Computer Engineering, University of California San Diego, United States of America, These authors contributed equally10.21105/joss.05023https://doi.org/10.5281/zenodo.7719505Python, Smartyhttps://joss.theoj.org/papers/10.21105/joss.05023.pdfautolfads, kubeflow, ray, neurosciencetag:joss.theoj.org,2005:Paper/40332023-03-09T12:49:00Z2023-03-10T00:04:40ZPyccel: a Python-to-X transpiler for scientific high-performance computingacceptedv1.7.02022-12-01 12:59:51 UTC832023-03-09 12:49:00 UTC820234991EmilyBourneCEA, IRFM, F-13108 Saint-Paul-lez-Durance, France0000-0002-3469-2338YamanGüçlüNMPP division, Max-Planck-Institut für Plasmaphysik, Garching bei München, Germany0000-0003-2619-5152SaidHadjoutNMPP division, Max-Planck-Institut für Plasmaphysik, Garching bei München, Germany, Dept. of Mathematics, Technische Universität München, Garching bei München, Germany0000-0003-3878-3146AhmedRatnaniLab. MSDA, Mohammed VI Polytechnic University, Benguerir, Morocco0000-0001-9035-123110.21105/joss.04991https://doi.org/10.5281/zenodo.7711108Python, C, Fortranhttps://joss.theoj.org/papers/10.21105/joss.04991.pdftranspiler, C language, HPC, scientific computingtag:joss.theoj.org,2005:Paper/40182023-03-07T14:18:54Z2023-03-08T00:04:15ZDistributedSparseGrids.jl: A Julia library implementing an Adaptive Sparse Grid collocation methodaccepted0.1.32022-11-22 10:27:43 UTC832023-03-07 14:18:54 UTC820235003MaximilianBittensFederal Institute for Geosciences and Natural Resources (BGR), Germany0000-0001-9954-294XRobertL.GatesIndependent Researcher, Germany10.21105/joss.05003https://doi.org/10.5281/zenodo.7673697Juliahttps://joss.theoj.org/papers/10.21105/joss.05003.pdfstochastics, sparse grids, high-performance computingtag:joss.theoj.org,2005:Paper/37592023-02-15T13:28:09Z2023-02-16T00:00:48ZPoUnce: A framework for automatized uncertainty quantification simulations on high-performance clustersacceptedv1.0.02022-08-02 14:41:27 UTC822023-02-15 13:28:09 UTC820234683JakobDuerrwaechterInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, Germany0000-0001-8961-5340ThomasKuhnInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, GermanyFabianMeyerInstitute of Applied Analysis and Numerical Simulation, University of Stuttgart, GermanyAndreaBeckInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, Germany, The Laboratory of Fluid Dynamics and Technical Flows, Otto von Guericke University Magdeburg, GermanyClaus-DieterMunzInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, Germany10.21105/joss.04683https://doi.org/10.5281/zenodo.7600634Pythonhttps://joss.theoj.org/papers/10.21105/joss.04683.pdfUncertainty quantification, High performance computing, Mulitlevel Monte Carlo, Multifidelity Monte Carlotag:joss.theoj.org,2005:Paper/31372022-11-01T12:44:58Z2022-11-02T00:00:27Zrustworkx: A High-Performance Graph Library for Pythonacceptedv0.10.22021-10-28 15:12:06 UTC792022-11-01 12:44:58 UTC720223968MatthewTreinishIBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, USA \newline0000-0001-9713-2875IvanCarvalhoUniversity of British Columbia, Kelowna, Canada \newline0000-0002-8257-2103GeorgiosTsilimigkounakisNational Technical University of Athens, Athens, Greece \newline0000-0001-6174-0801NahumSáCentro Brasileiro de Pesquisas Físicas, Rio de Janeiro, Brazil0000-0002-3234-815410.21105/joss.03968https://doi.org/10.5281/zenodo.7158473Python, Rusthttps://joss.theoj.org/papers/10.21105/joss.03968.pdfgraph theorytag:joss.theoj.org,2005:Paper/34442022-06-25T17:36:18Z2022-06-26T00:01:16ZpyABC: Efficient and robust easy-to-use approximate Bayesian computationaccepted0.12.22022-03-26 22:08:05 UTC742022-06-25 17:36:18 UTC720224304YannikSchälteFaculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany, Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany, Center for Mathematics, Technical University Munich, Garching, Germany0000-0003-1293-820XEmmanuelKlingerInstitute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany, Center for Mathematics, Technical University Munich, Garching, Germany, Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, GermanyEmadAlamoudiFaculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany0000-0002-9129-4635JanHasenauerFaculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany, Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany, Center for Mathematics, Technical University Munich, Garching, Germany0000-0002-4935-331210.21105/joss.04304https://doi.org/10.5281/zenodo.6677826Python, Makohttps://joss.theoj.org/papers/10.21105/joss.04304.pdfapproximate Bayesian computation, ABC, likelihood-free inference, high-performance computing, parallel, sequential Monte Carlotag:joss.theoj.org,2005:Paper/32642022-06-25T13:58:33Z2022-06-26T00:01:19ZInterface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functionsacceptedv1.0.02021-12-24 03:42:31 UTC742022-06-25 13:58:33 UTC720224040EvgenyMoermanThe NOMAD Laboratory at the Fritz Haber Institute of the Max Plank Society, Berlin, GermanyFelixHummelInstitute for Theoretical Physics, TU Wien, Vienna, AustriaAndreasGrüneisInstitute for Theoretical Physics, TU Wien, Vienna, AustriaAndreasIrmlerInstitute for Theoretical Physics, TU Wien, Vienna, AustriaMatthiasSchefflerThe NOMAD Laboratory at the Fritz Haber Institute of the Max Plank Society, Berlin, Germany10.21105/joss.04040https://doi.org/10.5281/zenodo.6658117Fortranhttps://joss.theoj.org/papers/10.21105/joss.04040.pdfMaterials science, Quantum chemistry, High-performance, Periodic Coupled Clustertag:joss.theoj.org,2005:Paper/32942022-06-09T14:33:25Z2022-06-10T00:01:17ZGridapDistributed: a massively parallel finite element toolbox in Juliaacceptedv0.2.42022-01-18 08:34:54 UTC742022-06-09 14:33:25 UTC720224157SantiagoBadiaSchool of Mathematics, Monash University, Clayton, Victoria, 3800, Australia., Centre Internacional de Mètodes Numèrics en Enginyeria, Esteve Terrades 5, E-08860 Castelldefels, Spain.0000-0003-2391-4086AlbertoF.MartínSchool of Mathematics, Monash University, Clayton, Victoria, 3800, Australia.0000-0001-5751-4561FrancescVerdugoCentre Internacional de Mètodes Numèrics en Enginyeria, Esteve Terrades 5, E-08860 Castelldefels, Spain.0000-0003-3667-443X10.21105/joss.04157https://doi.org/10.5281/zenodo.6622081Juliahttps://joss.theoj.org/papers/10.21105/joss.04157.pdfPartial Differential Equations, Finite Elements, Distributed memory parallelization, High Performance Computing