This image shows Dominik Göddeke

Dominik Göddeke

Prof. Dr. rer. nat.

Head of Institute and Head of Group
Institute of Applied Analysis and Numerical Simulation
Chair Computational Mathematics for Complex Simulation in Science and Engineering

Contact

+49 711 685 62022
+4971168552022

Allmandring 5b
70569 Stuttgart
Deutschland
Room: 0.40

Office Hours

by appointment

Subject

Main interests: numerics of partial differential equations, high performance computing, computational science and engineering, hardware-oriented numerics

Especially: finite-element multigrid domain decomposition methods, parallelisation, GPU computing, cluster computing and big machines, efficient data structures and implementation techniques, continuum mechanics

Current focus: fault tolerance, communication-avoiding and asynchrony, software frameworks, green computing, computational geophysics, imaging

  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. 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, Jan. 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, Feb. 2021, doi: 10.1177/1094342021990433.
    3. A. Krämer et al., “Multi-physics multi-scale HPC simulations of skeletal muscles,” in High Performance Computing in Science and Engineering ’20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020, W. E. Nagel, D. H. Kröner, and M. M. Resch, Eds. 2021. doi: 10.1007/978-3-030-80602-6_13.
    4. A. Krämer et al., High Performance Computing in Science and Engineering 20. Springer, 2021. doi: 10.1007/978-3-030-80602-6_13.
    5. J. Kühnert, D. Göddeke, and M. Herschel, “Provenance-integrated parameter selection and optimization in numerical simulations,” Jul. 2021. [Online]. Available: https://www.usenix.org/conference/tapp2021/presentation/kühnert
    6. 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, Oct. 2021, doi: 10.1016/j.cpc.2021.108059.
    7. 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, Mar. 2021, doi: 10.1088/1361-6420/abd85a.
  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, May 2020, doi: 10.1016/j.cnsns.2019.105150.
    3. 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, Dec. 2020, doi: 10.1016/j.cma.2020.113347.
  4. 2019

    1. P. Bastian et al., “Exa-Dune -- Flexible PDE Solvers, Numerical Methods and Applications.” 2019.
    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, Dec. 2019, doi: 10.1016/j.cnsns.2019.105150.
  5. 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, Nov. 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, Jul. 2018, doi: 10.3389/fphys.2018.00816.
    3. 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, Jul. 2018, doi: 10.3389/fphys.2018.00816.
    4. M. Brehler, M. Schirwon, D. Göddeke, and P. Krummrich, “Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber  Transmission Systems on GPUs,” Jul. 2018.
    5. M. Brehler, M. Schirwon, D. Göddeke, and P. Krummrich, “Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber Transmission Systems on GPUs,” presented at the Signal Processing in Photonic Communications, Advanced Photonics Congress, Zürich, 2018.
    6. 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,” Mar. 2018.
  6. 2017

    1. M. Altenbernd and D. Göddeke, “Soft fault detection and correction for multigrid,” Feb. 2017, doi: 10.1177/1094342016684006.
    2. 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, Sep. 2017, doi: 10.1109/JLT.2017.2715358.
  7. 2016

    1. P. Bastian et al., “Hardware-Based Efficiency Advances in the EXA-DUNE Project,” in Software for Exascale Computing - SPPEXA 2013-2015, Cham, 2016, no. 113, pp. 3–23. doi: 10.1007/978-3-319-40528-5_1.
    2. P. Bastian et al., “Advances Concerning Multiscale Methods and Uncertainty Quantification in EXA-DUNE,” in Software for Exascale Computing - SPPEXA 2013-2015, Cham, 2016, no. 113, pp. 25–43. doi: 10.1007/978-3-319-40528-5_2.
    3. 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.
    4. 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.
    5. 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.
    6. 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, Aug. 2016, doi: 10.1007/s00450-016-0324-5.
  8. 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, Nov. 2015, doi: 10.1016/j.parco.2015.07.003.
    2. 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.
    3. S. Müthing, D. Ribbrock, and D. Göddeke, “Integrating multi-threading and accelerators into DUNE-ISTL,” in Numerical Mathematics and Advanced Applications - ENUMATH 2013, Lausanne, 2015, no. 103, pp. 601–609. doi: 10.1007/978-3-319-10705-9_59.
    4. 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.
    5. S. Turek and D. Göddeke, “Hardware-Oriented Numerics for PDE,” in Encyclopedia of Applied and Computational Mathematics, B. Engquist, Ed. Berlin: Springer, 2015, pp. 627–630. doi: 10.1007/978-3-540-70529-1_312.
  9. 2014

    1. P. Bastian et al., “EXA-DUNE: Flexible PDE solvers, numerical methods and applications,” in Lecture notes in computer science, Porto, 2014, vol. 2, no. 8806, pp. 530–541. doi: 10.1007/978-3-319-14313-2_45.
    2. 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.
    3. 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.
    4. D. Göddeke, D. Komatitsch, and M. Möller, “Finite and spectral element methods on unstructured grids for flow and wave propagation problems,” in Numerical computations with GPUs, V. Kindratenko, Ed. Cham: Springer, 2014, pp. 183–206. doi: 10.1007/978-3-319-06548-9_9.
    5. 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, Oct. 2014, pp. 109--113.
    6. 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, 2014, pp. 109–113.
  10. 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, Jul. 2013, doi: 10.1016/j.compfluid.2012.01.025.
    2. 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,” vol. 80, pp. 327–332, 2013, doi: 10.1016/j.compfluid.2012.01.025.
    3. 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, Mar. 2013, doi: 10.1016/j.jcp.2012.11.031.
    4. D. Göddeke et al., “Energy efficiency vs. performance of the numerical solution of PDEs : an application study on a low-power ARM-based cluster,” vol. 237, pp. 132–150, 2013, doi: 10.1016/j.jcp.2012.11.031.
    5. 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.
  11. 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,” May 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, Jan. 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, Apr. 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, Jan. 2011, doi: 10.1109/TPDS.2010.61.
    5. 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. Dongarra, Eds. Boca Raton, Fla.: CRC Press, 2011, pp. 113–130. doi: 10.1201/b10376-10.
  12. 2010

    1. M. Geveler, D. Ribbrock, D. Goeddeke, and S. Turek, “Lattice-Boltzmann Simulation of the Shallow-Water Equations with Fluid-Structure Interaction on Multi- and Manycore Processors,” in Lecture Notes in Computer Science, vol. 1, no. 6310, R. Keller, D. Kramer, and J.-P. Weiss, Eds. Berlin: Springer, 2010, pp. 92–104. doi: 10.1007/978-3-642-16233-6_11.
    2. 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.
    3. 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.
    4. D. Göddeke, “Fast and accurate finite-element multigrid solvers for PDE simulations on GPU clusters,” Dissertation, Stuttgart, 2010.
    5. 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
    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,” Hamburg, 2010, vol. 25, no. 1/2, pp. 75–82. doi: 10.1007/s00450-010-0109-1.
    7. D. Komatitsch, D. Michéa, G. Erlebacher, and D. Göddeke, “Running 3D finite-difference or spectral-element wave propagation codes 25x to 50x faster using a GPU cluster,” Barcelona, Spain, 2010, vol. 4, pp. 2920–2924. doi: 10.3997/2214-4609.201400930.
    8. 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), Jun. 2010, vol. 4, pp. 2920--2924.
    9. 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, Oct. 2010, doi: 10.1016/j.jcp.2010.06.024.
    10. D. Komatitsch, G. Erlebacher, D. Göddeke, and D. Michéa, “High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster,” vol. 229, pp. 7692–7714, 2010, doi: 10.1016/j.jcp.2010.06.024.
    11. 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, May 2010, doi: 10.1007/s00450-010-0109-1.
    12. D. Ribbrock, M. Geveler, D. Göddeke, and S. Turek, “Performance and Accuracy of Lattice-Boltzmann Kernels on Multi- and Manycore Architectures,” in Procedia computer science, Amsterdam, 2010, vol. 1, no. 1, pp. 239–247. doi: 10.1016/j.procs.2010.04.027.
    13. 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.
    14. 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.
    15. 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, Nov. 2010, doi: 10.1002/cpe.1584.
    16. 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,” in Concurrency and Computation: Practice and Experience, 2010, no. 22, 16, pp. 2247–2265. doi: 10.1002/cpe.1584.
    17. S. Turek, D. Göddeke, C. Becker, S. H. M. Buijssen, and H. Wobker, “UCHPC - Unconventional High-Performance Computing for Finite Element Simulations,” presented at the 23. International Supercomputing Conference (ISC’08), Dresden, 2010.
  13. 2009

    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, Stuttgart, 2009, vol. 2008, pp. 425–440. doi: 10.1007/978-3-540-88303-6_30.
    2. D. Göddeke, S. H. M. Buijssen, H. Wobker, and S. Turek, “GPU Acceleration of an Unmodified Parallel Finite Element Navier-Stokes Solver,” in Proceedings of the 2009 International Conference on High Performance Computing & Simulation (HPCS 2009), Leipzig, 2009, pp. 12–21. doi: 10.1109/HPCSIM.2009.5191718.
    3. 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, Jun. 2009, pp. 12--21. doi: 10.1109/HPCSIM.2009.5191718.
    4. 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,” vol. 4, no. 4, Art. no. 4, 2009, doi: 10.1504/IJCSE.2009.029162.
    5. 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, Oct. 2009, doi: 10.1504/IJCSE.2009.029162.
    6. 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,” vol. 180, no. 12, Art. no. 12, 2009, doi: 10.1016/j.cpc.2009.04.018.
    7. 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, Dec. 2009, doi: 10.1016/j.cpc.2009.04.018.
  14. 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, Nov. 2008, doi: 10.1504/IJCSE.2008.021111.
    3. D. Göddeke et al., “Using GPUs to Improve Multigrid Solver Performance on a Cluster,” vol. 4, no. 1, Art. no. 1, 2008, doi: 10.1504/IJCSE.2008.021111.
    4. D. Göddeke and R. Strzodka, Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations (Prt 2: double precision GPUs), no. 370. Dortmund: Technische Universität, Fakultät für Mathematik, 2008.
    5. 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, Aug. 2008.
    6. M. Köster, D. Göddeke, H. Wobker, and S. Turek, How to gain speedups of 1000 on single processor with fast FEM solvers benchmarking numerical and computational efficiency, no. 382. Dortmund: Technische Universität, Fakultät für Mathematik, 2008.
    7. 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, Oct. 2008.
    8. S. Turek, D. Göddeke, C. Becker, S. H. M. Buijssen, and H. Wobker, “UCHPC -- Unconventional High-Performance Computing for Finite  Element Simulations,” Jun. 2008.
  15. 2007

    1. D. Göddeke, H. Wobker, R. Strzodka, J. Mohd-Yusof, P. McCormick, and S. Turek, “Co-processor acceleration of an unmodified parallel solid mechanics code with FEASTGPU,” vol. 4, no. 4, Art. no. 4, 2007, doi: 10.1504/IJCSE.2009.029162.
    2. D. Göddeke, R. Strzodka, and S. Turek, “Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations,” vol. 22, no. 4, Art. no. 4, 2007, doi: 10.1080/17445760601122076.
    3. 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, Sep. 2007, doi: 10.1016/j.parco.2007.09.002.
    4. 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.” Nov. 2007.
    5. 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, Jan. 2007, doi: 10.1080/17445760601122076.
    6. D. Göddeke et al., “Exploring weak scalability for FEM calculations on a GPU-enhanced cluster,” vol. 33, no. 10–11, Art. no. 10–11, 2007, doi: 10.1016/j.parco.2007.09.002.
  16. 2006

    1. D. Göddeke, C. Becker, and S. Turek, “Integrating GPUs as fast co-processors into the parallel FE package FEAST,” in ASIM 2006, Hannover, 2006, no. 16, pp. 277–282.
    2. 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), Sep. 2006, pp. 277--282.
    3. 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), Apr. 2006, pp. 259--270. doi: 10.1109/FCCM.2006.57.
    4. R. Strzodka and D. Göddeke, “Mixed Precision Methods for Convergent Iterative Schemes,” in Proceedings of the Workshop on Edge Computing Using New Commodity Architectures, Chapel Hill, North Carolina, 2006, p. D-59-60.
    5. R. Strzodka and D. Göddeke, “Pipelined Mixed Precision Algorithms on FPGAs for Fast and Accurate PDE Solvers from Low Precision Components,” in 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, 2006, Napa, CA, USA, 2006, pp. 259–270. doi: 10.1109/FCCM.2006.57.
    6. R. Strzodka and D. Göddeke, “Mixed Precision Methods for Convergent Iterative Schemes,” in Proceedings of the Workshop on Edge Computing Using New Commodity  Architectures, May 2006, p. D-59--60.
  17. 2005

    1. D. Göddeke, R. Strzodka, and S. Turek, “Accelerating Double Precision FEM Simulations with GPUs,” in 18th Symposium Simulationstechnique (ASIM’05), Sep. 2005, pp. 139--144.
    2. D. Göddeke, GPGPU-Basic Math Tutorial, no. 300. Dortmund: Univ., 2005.
    3. D. Göddeke, R. Strzodka, and S. Turek, “Accelerating Double Precision FEM Simulations with GPUs,” in Proceedings / 18. Symposium Simulationstechnique, Erlangen, 2005, no. 15, pp. 139–144.
    4. D. Göddeke, “GPGPU--Basic Math Tutorial,” Fachbereich Mathematik, Universität Dortmund, Nov. 2005.
  since 1/2019 Pl, Cluster of Excellence "Data-Integrated Simulation Science" (Exc 2075)
  since 1/2016 Fellow, Stuttgart Centre for Simulation Sciences, and Director, Institute of Applied Analysis and Numerical Simulation
  since 2/2015 Full professor (W3 m.L.), Chair Computational Mathematics for Complex Simulations in Science and Engineering, Institute of Applied Analysis and Numerical Simulation, University of Stuttgart
  8/2011–1/2015 Juniorprofessor for „Hardware-oriented Numerics for Large Systems“ at the Chair for Applied Mathematics and Numerics (LS3), Department of Mathematics, TU Dortmund Technical University
  9/2014 Positive intermediate evaluation of the Junior Professorship, formally equivalent to Habilitation in Mathematics
  10/2004–08/2011 Research assistant at the above Chair
  May 10, 2010 Graduation: Dr. rer. nat. (with highest honours)
Dissertation title: Fast and Accurate Finite-Element Multigrid Solvers for PDE Simulations on GPU Clusters (Supervisors: S. Turek, H. Müller)
  10/2004–05/2010 Doctoral studies, Department of Mathematics, TU Dortmund
  August 31, 2004 Graduation: Diploma in Computer Science (with honours)
Dissertation title: Geometric Projection Methods on Surface Triangulations for Numerical Flow Simulation with Hierarchical Multigrid Methods (Supervisors: S. Turek, H. Müller)
     
  10/1999–08/2004 Studies of Computer Science (major) and Mathematics (minor), Dortmund University, Pre-Diploma in Computer Science (9/2001) and Mathematics (4/2002)
2020 Digital Teaching Award by the students of the Department of Mathematics, University of Stuttgart
2019 Award for extraordinary engagement as a lecturer by the student body STUVUS.
2018 Best lecture award for the best intermediate level (Stochastik und angewandte Mahematik für LA) lecture in 2017/2018, awarded by the students of the Department of Mathematics, University of Stuttgart.
2016 Best lecture awards for the best entry-level (Numerical Linear Algebra) and the best intermediate level (Numerics for ODEs) lectures in 2016, awarded by the students of the Department of Mathematics, University of Stuttgart.
2011 Rudolf Chaudoire Award, jointly awarded by TU Dortmund and the Chaudoire Society to honour excellent junior researchers. The award includes 5.000€ financial support for a longer research visit abroad.
2011 Young Researcher Best Paper Award of the Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, jointly awarded with M. Geveler, D. Ribbrock, P. Zajac and S. Turek (all TU Dortmund) for our submission Efficient Finite Element Geometric Multigrid Solvers for Unstructured Grids on Graphics Processing Units (500€ cash prize).
2010 Dissertation Award for the best doctoral dissertation of the Department of Mathematics, TU Dortmund.
2009 SIAM Travel Award for the SIAM Conference on Computational Science and Engineering (stipend of 500$).
2008 PRACE Award of the Partnership for Advanced Computing in Europe, together with S. Turek, Ch. Becker, S.H.M. Buijssen and H. Wobker (all TU Dortmund) for our contribution UCHPC – UnConventional High Performance Computing for Finite Element Simulations. This award has been granted for the first time in 2008 and honours young European researchers in HPC (stipend to attend a thematic conference).
2002–2004 Studienstiftung des Deutschen Volkes

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