tag:joss.theoj.org,2005:/papers/in/Cuda?page=2Journal of Open Source Software2020-10-10T17:18:47ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/18482020-10-10T17:18:47Z2021-02-15T11:30:17ZGENRE (GPU Elastic-Net REgression): A CUDA-Accelerated Package for Massively Parallel Linear Regression with Elastic-Net Regularizationacceptedv1.02020-07-10 23:13:10 UTC542020-10-10 17:18:47 UTC520202644ChristopherKhanVanderbilt University0000-0003-3201-3423BrettByramVanderbilt University0000-0003-3693-145910.21105/joss.02644https://doi.org/10.5281/zenodo.4076520Matlab, Cudahttps://joss.theoj.org/papers/10.21105/joss.02644.pdfCUDA, GPU computing, Cyclic coordinate descent, Elastic-net regularization, Linear regressiontag:joss.theoj.org,2005:Paper/16162020-08-31T06:29:30Z2021-02-15T11:30:46ZGinkgo: A high performance numerical linear algebra libraryacceptedv1.1.12020-05-20 16:10:14 UTC522020-08-31 06:29:30 UTC520202260HartwigAnztKarlsruhe Institute of Technology, Innovative Computing Laboratory, University of Tennessee, Knoxville0000-0003-2177-952XTerryCojeanKarlsruhe Institute of Technology0000-0002-1560-921XYen-ChenChenThe University of TokyoGoranFlegarUniversity of Jaume I0000-0002-4154-0420FritzGöbelKarlsruhe Institute of TechnologyThomasGrützmacherKarlsruhe Institute of Technology0000-0001-9346-2981PratikNayakKarlsruhe Institute of Technology0000-0002-7961-1159TobiasRibizelKarlsruhe Institute of Technology0000-0003-3023-1849Yu-HsiangTsaiKarlsruhe Institute of Technology0000-0001-5229-373910.21105/joss.02260https://doi.org/10.5281/zenodo.4003613C++, SourcePawn, Cudahttps://joss.theoj.org/papers/10.21105/joss.02260.pdflinear-algebra, hpc, cuda, modern-c++, hip, spmvtag:joss.theoj.org,2005:Paper/16942020-07-28T18:24:07Z2021-02-15T11:30:37ZHOOMD-TF: GPU-Accelerated, Online Machine Learning in the HOOMD-blue Molecular Dynamics Engineacceptedv0.62020-05-27 21:16:20 UTC512020-07-28 18:24:07 UTC520202367RainierBarrettUniversity of Rochester Chemical Engineering Department, Rochester, New York, United States of America0000-0002-5728-9074MaghesreeChakrabortyUniversity of Rochester Chemical Engineering Department, Rochester, New York, United States of America0000-0001-5706-3027DilnozaB.AmirkulovaUniversity of Rochester Chemical Engineering Department, Rochester, New York, United States of America0000-0001-6961-3081HetaA.GandhiUniversity of Rochester Chemical Engineering Department, Rochester, New York, United States of America0000-0002-9465-3840GeemiP.WellawatteUniversity of Rochester Chemistry Department, Rochester, New York, United States of America0000-0002-3772-6927AndrewD.WhiteUniversity of Rochester Chemical Engineering Department, Rochester, New York, United States of America0000-0002-6647-396510.21105/joss.02367https://doi.org/10.5281/zenodo.3962305C++, Cuda, Pythonhttps://joss.theoj.org/papers/10.21105/joss.02367.pdfmolecular dynamics, machine learningtag:joss.theoj.org,2005:Paper/9132019-11-27T02:24:07Z2021-02-15T11:32:27ZACHR.cu: GPU-accelerated sampling of metabolic networksacceptedv0.12019-03-05 20:27:58 UTC372019-11-27 02:24:07 UTC420191363MarouenBenGuebilaLuxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.0000-0001-5934-966X10.21105/joss.01363https://doi.org/10.5281/zenodo.3233085Cuda, Matlab, Chttps://joss.theoj.org/papers/10.21105/joss.01363.pdfcuda, metabolism, constraint-based modeling, GPUtag:joss.theoj.org,2005:Paper/7212019-02-19T02:29:11Z2021-02-15T11:32:49ZIDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networksacceptedv.0.4.02018-11-11 16:41:12 UTC342019-02-19 02:29:11 UTC420191081PatriciaWollstadtMEG Unit, Brain Imaging Center, Goethe-University Frankfurt, Fankfurt am Main, Germany0000-0002-7105-5207JosephT.LizierCentre for Complex Systems, Faculty of Engineering and IT, The University of Sydney, Sydney, Australia0000-0002-9910-8972RaulVicenteComputational Neuroscience Lab, Institute of Computer Science, Tartu, Estonia0000-0002-7894-6213ConorFinnCentre for Complex Systems, Faculty of Engineering and IT, The University of Sydney, Sydney, Australia, Data61, CSIRO, Epping, Australia0000-0001-8542-4205MarioMartinez-ZarzuelaCommunications and Signal Theory and Telematics Engineering, University of Valladolid, Valladolid, Spain0000-0002-6866-3316PedroMedianoComputational Neurodynamics Group, Department of Computing, Imperial College London, London, United Kingdom0000-0003-1789-5894LeonardoNovelliCentre for Complex Systems, Faculty of Engineering and IT, The University of Sydney, Sydney, Australia0000-0002-6081-3367MichaelWibralMEG Unit, Brain Imaging Center, Goethe-University Frankfurt, Fankfurt am Main, Germany, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany, Campus Institute for Dynamics of Biological Networks, Georg-August Universität, Göttingen, Germany0000-0001-8010-586210.21105/joss.01081https://doi.org/10.5281/zenodo.2554339Python, C, Matlab, Cudahttps://joss.theoj.org/papers/10.21105/joss.01081.pdfinformation theory, network inference, multivariate transfer entropy, mutual information, active information storage, partial information decompositiontag:joss.theoj.org,2005:Paper/2162017-07-21T00:00:00Z2021-02-15T11:34:04ZcuIBM: a GPU-based immersed boundary method codeacceptedv0.12017-06-09 22:43:18 UTC152017-07-21 00:00:00 UTC22017301AnushKrishnannuTonomy Inc. (previously at Boston University)OlivierMesnardThe George Washington University0000-0001-5335-7853LorenaA.BarbaThe George Washington University0000-0001-5812-271110.21105/joss.00301https://doi.org/10.5281/zenodo.832338Cuda, C++, Chttps://joss.theoj.org/papers/10.21105/joss.00301.pdfGPU, Computational Fluid Dynamics, Immersed-Boundary Method, CUSP library