Publications

List of publications of the chair.

  1. 2022

    1. E. Agullo et al., “Resiliency in numerical algorithm design for extreme scale simulations,” The International Journal of High Performance ComputingApplications, vol. 36, no. 2, Art. no. 2, 2022, doi: 10.1177/10943420211055188.
    2. T. Boege et al., “Research-Data Management Planning in the German Mathematical Community.” arXiv, 2022. doi: 10.48550/ARXIV.2211.12071.
    3. M. T. Horsch and B. Schembera, “Documentation of epistemic metadata by a mid-level ontology of cognitive processes,” 2022.
    4. K. Jung, B. Schembera, and M. Gärtner, “Best of Both Worlds? Mapping Process Metadata in Digital Humanities and Computational Engineering,” Metadata and Semantic Research, pp. 199--205, 2022, doi: 10.1007/978-3-030-98876-0_17.
    5. M. Zinßer et al., “Irradiation-dependent topology optimization of metallization grid patterns and variation of contact layer thickness used for latitude-based yield gain of thin-film solar modules,” MRS Advances, vol. 7, no. 3, Art. no. 3, 2022, doi: 10.1557/s43580-022-00321-3.

  2. 2021

    1. M. Altenbernd, N.-A. Dreier, C. Engwer, and D. Göddeke, “Towards Local-Failure Local-Recovery in PDE Frameworks: The Case of Linear Solvers,” in High Performance Computing in Science and Engineering -- HPCSE 2019, 2021, vol. 12456, pp. 17--38. doi: 10.1007/978-3-030-67077-1_2.
    2. T. Benacchio et al., “Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction,” The International Journal of High Performance Computing Applications, vol. 35, no. 4, Art. no. 4, 2021, doi: 10.1177/1094342021990433.
    3. A. Krämer et al., “Multi-physics multi-scale HPC simulations of skeletal muscles,” High Performance Computing in Science and Engineering ’20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020, 2021, doi: 10.1007/978-3-030-80602-6_13.
    4. J. Kühnert, D. Göddeke, and M. Herschel, “Provenance-integrated parameter selection and optimization in numerical simulations,” 2021. [Online]. Available: https://www.usenix.org/conference/tapp2021/presentation/kühnert
    5. M. Osorno, M. Schirwon, N. Kijanski, R. Sivanesapillai, H. Steeb, and D. Göddeke, “A cross-platform, high-performance SPH toolkit for image-based flow simulations on the pore scale of porous media,” Computer Physics Communications, vol. 267, no. 108059, Art. no. 108059, 2021, doi: 10.1016/j.cpc.2021.108059.
    6. A. Rörich, T. A. Werthmann, D. Göddeke, and L. Grasedyck, “Bayesian inversion for electromyography using low-rank tensor formats,” Inverse Problems, vol. 37, no. 5, Art. no. 5, 2021, doi: 10.1088/1361-6420/abd85a.
    7. J. Schmalfuss, C. Riethmüller, M. Altenbernd, K. Weishaupt, and D. Göddeke, “Partitioned coupling vs. monolithic block-preconditioning approaches for solving Stokes-Darcy systems,” 2021. doi: 10.23967/coupled.2021.043.

  3. 2020

    1. P. Bastian et al., “Exa-Dune - Flexible PDE Solvers, Numerical Methods and Applications,” in Software for Exascale Computing -- SPPEXA 2016--2019, H.-J. Bungartz, S. Reiz, B. Uekermann, P. Neumann, and W. E. Nagel, Eds. Springer, 2020, pp. 225--269. doi: 10.1007/978-3-030-47956-5_9.
    2. M. Brehler, M. Schirwon, P. M. Krummrich, and D. Göddeke, “Simulation of Nonlinear Signal Propagation in Multimode Fibers on Multi-GPU Systems,” Communications in Nonlinear Science and Numerical Simulation, vol. 84, p. 105150, 2020, doi: 10.1016/j.cnsns.2019.105150.
    3. L. Giraud, U. Rüde, and L. Stals, “Resiliency in Numerical Algorithm Design for Extreme Scale Simulations (Dagstuhl Seminar 20101),” Dagstuhl Reports, vol. 10, no. 3, Art. no. 3, 2020, doi: 10.4230/DagRep.10.3.1.
    4. D. Göddeke, M. Schirwon, and N. Borg, “Smartphone-Apps im Mathematikstudium,” 2020, doi: 10.18419/darus-1147.
    5. R. Tielen, M. Möller, D. Göddeke, and C. Vuik, “p-multigrid methods and their comparison to h-multigrid methods in Isogeometric Analysis,” Computer Methods in Applied Mechanics and Engineering, vol. 372, p. 113347, 2020, doi: 10.1016/j.cma.2020.113347.

  4. 2018

    1. M. Altenbernd and D. Göddeke, “Soft fault detection and correction for multigrid,” The International Journal of High Performance Computing Applications, vol. 32, no. 6, Art. no. 6, 2018, doi: 10.1177/1094342016684006.
    2. C. P. Bradley et al., “Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems,” Frontiers in Physiology, vol. 9, no. 816, Art. no. 816, 2018, doi: 10.3389/fphys.2018.00816.
    3. M. Brehler, M. Schirwon, D. Göddeke, and P. Krummrich, “Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber  Transmission Systems on GPUs,” 2018.
    4. C. Engwer, M. Altenbernd, N.-A. Dreier, and D. Göddeke, “A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application,” 2018.

  5. 2017

    1. M. Brehler, M. Schirwon, D. Göddeke, and P. M. Krummrich, “A GPU-Accelerated Fourth-Order Runge-Kutta in the Interaction Picture Method for the Simulation of Nonlinear Signal Propagation in Multimode Fibers,” Journal of Lightwave Technology, vol. 35, no. 17, Art. no. 17, 2017, doi: 10.1109/JLT.2017.2715358.

  6. 2016

    1. P. Bastian et al., “Advances Concerning Multiscale Methods and Uncertainty Quantification  in EXA-DUNE,” in Software for Exascale Computing -- SPPEXA 2013--2015, H.-J. Bungartz, P. Neumann, and W. E. Nagel, Eds. Springer, 2016, pp. 25--43. doi: 10.1007/978-3-319-40528-5_2.
    2. P. Bastian et al., “Hardware-Based Efficiency Advances in the EXA-DUNE Project,” in Software for Exascale Computing -- SPPEXA 2013--2015, H.-J. Bungartz, P. Neumann, and W. E. Nagel, Eds. Springer, 2016, pp. 3--23. doi: 10.1007/978-3-319-40528-5_1.
    3. M. Geveler, B. Reuter, V. Aizinger, D. Göddeke, and S. Turek, “Energy efficiency of the simulation of three-dimensional coastal  ocean circulation on modern commodity and mobile processors -- A  case study based on the Haswell and Cortex-A15 microarchitectures,” Computer Science -- Research and Development, vol. 31, no. 4, Art. no. 4, 2016, doi: 10.1007/s00450-016-0324-5.

  7. 2015

    1. D. Göddeke, M. Altenbernd, and D. Ribbrock, “Fault-tolerant finite-element multigrid algorithms with hierarchically  compressed asynchronous checkpointing,” Parallel Computing, vol. 49, pp. 117–135, 2015, doi: 10.1016/j.parco.2015.07.003.
    2. S. Müthing, D. Ribbrock, and D. Göddeke, “Integrating multi-threading and accelerators into DUNE-ISTL,” in Numerical Mathematics and Advanced Applications -- ENUMATH 2013, vol. 103, A. Abdulle, S. Deparis, D. Kressner, F. Nobile, and M. Picasso, Eds. Springer, 2015, pp. 601--609. doi: 10.1007/978-3-319-10705-9_59.

  8. 2014

    1. P. Bastian et al., “EXA-DUNE: Flexible PDE Solvers, Numerical Methods and Applications,” in Euro-Par 2014: Parallel Processing Workshops, vol. 8806, L. Lopes, J. Zilinskas, A. Costan, RobertoG. Cascella, G. Kecskemeti, E. Jeannot, M. Cannataro, L. Ricci, S. Benkner, S. Petit, V. Scarano, J. Gracia, S. Hunold, StephenL. Scott, S. Lankes, C. Lengauer, J. Carretero, J. Breitbart, and M. Alexander, Eds. Springer, 2014, pp. 530--541. doi: 10.1007/978-3-319-14313-2_45.
    2. D. Göddeke, D. Komatitsch, and M. Möller, “Finite and Spectral Element Methods on Unstructured Grids for Flow  and Wave Propagation Methods,” in Numerical Computations with GPUs, V. Kindratenko, Ed. Springer, 2014, pp. 183--206. doi: 10.1007/978-3-319-06548-9_9.
    3. S. Müthing, P. Bastian, D. Göddeke, and D. Ribbrock, “Node-level performance engineering for an advanced density driven  porous media flow solver,” in 3rd Workshop on Computational Engineering 2014, Stuttgart, Germany, 2014, pp. 109--113.

  9. 2013

    1. M. Geveler, D. Ribbrock, D. Göddeke, P. Zajac, and S. Turek, “Towards a complete FEM-based simulation toolkit on GPUs: Unstructured  Grid Finite Element Geometric Multigrid solvers with strong smoothers  based on Sparse Approximate Inverses,” Computers & Fluids, vol. 80, pp. 327--332, 2013, doi: 10.1016/j.compfluid.2012.01.025.
    2. D. Göddeke et al., “Energy efficiency vs. performance of the numerical solution of PDEs:  an application study on a low-power ARM-based cluster,” Journal of Computational Physics, vol. 237, pp. 132--150, 2013, doi: 10.1016/j.jcp.2012.11.031.
    3. S. Turek and D. Göddeke, “Hardware-oriented Numerics for PDE,” in Encyclopedia of Applied and Computational Mathematics, B. Engquist, T. Chan, W. J. Cook, E. Hairer, J. Hastad, A. Iserles, H. P. Langtangen, C. Le Bris, P. L. Lions, C. Lubich, A. J. Majda, J. R. McLaughlin, R. M. Nieminen, J. T. Oden, P. Souganidis, and A. Tveito, Eds. Springer, 2013.

  10. 2011

    1. M. Geveler, D. Ribbrock, D. Göddeke, P. Zajac, and S. Turek, “Towards a complete FEM-based simulation toolkit on GPUs: Geometric  multigrid solvers,” 2011.
    2. M. Geveler, D. Ribbrock, S. Mallach, D. Göddeke, and S. Turek, “A Simulation Suite for Lattice-Boltzmann based Real-Time CFD  Applications Exploiting Multi-Level Parallelism on modern Multi-  and Many-Core Architectures,” Journal of Computational Science, vol. 2, pp. 113--123, 2011, doi: 10.1016/j.jocs.2011.01.008.
    3. M. Geveler, D. Ribbrock, D. Göddeke, P. Zajac, and S. Turek, “Efficient Finite Element Geometric Multigrid Solvers for Unstructured  Grids on GPUs,” in Second International Conference on Parallel, Distributed, Grid and  Cloud Computing for Engineering, 2011. doi: 10.4203/ccp.95.22.
    4. D. Göddeke and R. Strzodka, “Cyclic Reduction Tridiagonal Solvers on GPUs Applied to Mixed Precision  Multigrid,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 1, Art. no. 1, 2011, doi: 10.1109/TPDS.2010.61.

  11. 2010

    1. M. Geveler, D. Ribbrock, D. Göddeke, and S. Turek, “Lattice-Boltzmann Simulation of the Shallow-Water Equations with  Fluid-Structure Interaction on Multi- and Manycore Processors,” in Facing the Multicore Challenge, vol. 6310, R. Keller, D. Kramer, and J.-P. Weiß, Eds. Springer, 2010, pp. 92--104. doi: 10.1007/978-3-642-16233-6_11.
    2. D. Göddeke and R. Strzodka, “Mixed Precision GPU-Multigrid Solvers with Strong Smoothers,” in Scientific Computing with Multicore and Accelerators, J. Kurzak, D. A. Bader, and J. J. Dongarra, Eds. CRC Press, 2010, pp. 131--147. doi: 10.1201/b10376-11.
    3. D. Göddeke, “Fast and Accurate Finite-Element Multigrid Solvers for PDE Simulations  on GPU Clusters,” Technische Universität Dortmund, Fakultät für Mathematik, 2010. [Online]. Available: http://hdl.handle.net/2003/27243
    4. D. Komatitsch, Michéa, G. Erlebacher, and D. Göddeke, “Running 3D finite-difference or spectral-element wave propagation  codes 25x to 50x faster using a GPU cluster,” in 72nd European Association of Geoscientists and Engineers Conference  and Exhibition (EAGE’2010), 2010, vol. 4, pp. 2920--2924.
    5. D. Komatitsch, G. Erlebacher, D. Göddeke, and D. Michéa, “High-order finite-element seismic wave propagation modeling with  MPI on a large GPU cluster,” Journal of Computational Physics, vol. 229, pp. 7692--7714, 2010, doi: 10.1016/j.jcp.2010.06.024.
    6. D. Komatitsch, D. Göddeke, G. Erlebacher, and D. Michéa, “Modeling the propagation of elastic waves using spectral elements  on a cluster of 192 GPUs,” Computer Science -- Research and Development, vol. 25, no. 1--2, Art. no. 1--2, 2010, doi: 10.1007/s00450-010-0109-1.
    7. D. Ribbrock, M. Geveler, D. Göddeke, and S. Turek, “Performance and Accuracy of Lattice-Boltzmann Kernels on Multi-  and Manycore Architectures,” in International Conference on Computational Science (ICCS’10), 2010, vol. 1, pp. 239--247. doi: 10.1016/j.procs.2010.04.027.
    8. S. Turek, D. Göddeke, S. H. M. Buijssen, and H. Wobker, “Hardware-Oriented Multigrid Finite Element Solvers on GPU-Accelerated  Clusters,” in Scientific Computing with Multicore and Accelerators, J. Kurzak, D. A. Bader, and J. J. Dongarra, Eds. CRC Press, 2010, pp. 113--130. doi: 10.1201/b10376-10.
    9. S. Turek, D. Göddeke, C. Becker, S. H. M. Buijssen, and H. Wobker, “FEAST -- Realisation of hardware-oriented Numerics for HPC  simulations with Finite Elements,” Concurrency and Computation: Practice and Experience, vol. 22, no. 6, Art. no. 6, 2010, doi: 10.1002/cpe.1584.

  12. 2009

    1. D. Göddeke, S. H. M. Buijssen, H. Wobker, and S. Turek, “GPU Acceleration of an Unmodified Parallel Finite Element Navier-Stokes  Solver,” in High Performance Computing & Simulation 2009, 2009, pp. 12--21. doi: 10.1109/HPCSIM.2009.5191718.
    2. D. Göddeke, H. Wobker, R. Strzodka, J. Mohd-Yusof, P. S. McCormick, and S. Turek, “Co-Processor Acceleration of an Unmodified Parallel Solid Mechanics  Code with FEASTGPU,” International Journal of Computational Science and Engineering, vol. 4, no. 4, Art. no. 4, 2009, doi: 10.1504/IJCSE.2009.029162.
    3. D. van Dyk, M. Geveler, S. Mallach, D. Ribbrock, D. Göddeke, and C. Gutwenger, “HONEI: A collection of libraries for numerical computations targeting  multiple processor architectures,” Computer Physics Communications, vol. 180, no. 12, Art. no. 12, 2009, doi: 10.1016/j.cpc.2009.04.018.

  13. 2008

    1. S. H. M. Buijssen, H. Wobker, D. Göddeke, and S. Turek, “FEASTSolid and FEASTFlow: FEM Applications Exploiting FEAST’s  HPC Technologies,” in High Performance Computing in Science and Engineering ’08, vol. 2008, W. Nagel, D. Kröner, and M. Resch, Eds. Springer, 2008, pp. 425--440. doi: 10.1007/978-3-540-88303-6_30.
    2. D. Göddeke et al., “Using GPUs to Improve Multigrid Solver Performance on a Cluster,” International Journal of Computational Science and Engineering, vol. 4, no. 1, Art. no. 1, 2008, doi: 10.1504/IJCSE.2008.021111.
    3. D. Göddeke and R. Strzodka, “Performance and accuracy of hardware-oriented native, emulated-  and mixed-precision solvers in FEM simulations (Part 2: Double  Precision GPUs),” Fakultät für Mathematik, Technische Universität  Dortmund, 2008.
    4. M. Köster, D. Göddeke, H. Wobker, and S. Turek, “How to gain speedups of 1000 on single processors with fast FEM  solvers ---- Benchmarking numerical and computational efficiency,” Fakultät für Mathematik, TU Dortmund, 2008.
    5. S. Turek, D. Göddeke, C. Becker, S. H. M. Buijssen, and H. Wobker, “UCHPC -- Unconventional High-Performance Computing for Finite  Element Simulations,” 2008.

  14. 2007

    1. D. Göddeke et al., “Exploring weak scalability for FEM calculations on a GPU-enhanced  cluster,” Parallel Computing, vol. 33, no. 10--11, Art. no. 10--11, 2007, doi: 10.1016/j.parco.2007.09.002.
    2. D. Göddeke, H. Wobker, R. Strzodka, J. Mohd-Yusof, P. S. McCormick, and S. Turek, “Co-processor acceleration of an unmodified parallel structural mechanics  code with FEAST-GPU.” 2007.
    3. D. Göddeke, R. Strzodka, and S. Turek, “Performance and accuracy of hardware-oriented native-, emulated-  and mixed-precision solvers in FEM simulations,” International Journal of Parallel, Emergent and Distributed Systems, vol. 22, no. 4, Art. no. 4, 2007, doi: 10.1080/17445760601122076.

  15. 2006

    1. D. Göddeke, C. Becker, and S. Turek, “Integrating GPUs as fast co-processors into the parallel FE package  FEAST,” in 19th Symposium Simulationstechnique (ASIM’06), 2006, pp. 277--282.
    2. R. Strzodka and D. Göddeke, “Pipelined Mixed Precision Algorithms on FPGAs for Fast and Accurate  PDE Solvers from Low Precision Components,” in Proceedings of the 14th Annual IEEE Symposium on Field-Programmable  Custom Computing Machines (FCCM’06), 2006, pp. 259--270. doi: 10.1109/FCCM.2006.57.
    3. R. Strzodka and D. Göddeke, “Mixed Precision Methods for Convergent Iterative Schemes,” in Proceedings of the Workshop on Edge Computing Using New Commodity  Architectures, 2006, p. D-59--60.

  16. 2005

    1. D. Göddeke, R. Strzodka, and S. Turek, “Accelerating Double Precision FEM Simulations with GPUs,” in 18th Symposium Simulationstechnique (ASIM’05), 2005, pp. 139--144.
    2. D. Göddeke, “GPGPU--Basic Math Tutorial,” Fachbereich Mathematik, Universität Dortmund, 2005.
This image shows Dominik Göddeke

Dominik Göddeke

Prof. Dr. rer. nat.

Head of Institute and Head of Group

This image shows Britta Lenz

Britta Lenz

 

Secretary's Office

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