Dieses Bild zeigt Göddeke

Prof. Dr. rer. nat.

Dominik Göddeke

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
+49 711 685-52022

Allmandring 5b
70569 Stuttgart
Deutschland
Room: 0.40

Consultation

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. 2018

    1. C. P. Bradley et al., “Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems,” Frontiers in Physiology, vol. 9, no. 816, 2018.
    2. M. Brehler, M. Schirwon, D. Göddeke, and P. Krummrich, “Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber  Transmission Systems on GPUs,” in Proceedings of Advanced Photonics 2018, 2018.
    3. N.-A. Dreier, M. Altenbernd, C. Engwer, and D. Göddeke, “A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application,” in Proceedings of 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2018), 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,” in Proceedings of the 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2018), 2018.
  2. 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, pp. 3622--3628, 2017.
  3. 2016

    1. M. Altenbernd and D. Göddeke, “Soft fault detection and correction for multigrid,” The International Journal of High Performance Computing Applications, 2016.
    2. 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.
    3. 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.
    4. 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, pp. 225–234, 2016.
  4. 2015

    1. 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.
  5. 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, R. Cascella, G. Kecskemeti, E. Jeannot, M. Cannataro, L. Ricci, S. Benkner, S. Petit, V. Scarano, J. Gracia, S. Hunold, S. Scott, S. Lankes, C. Lengauer, J. Carretero, J. Breitbart, and M. Alexander, Eds. Springer, 2014, pp. 530--541.
    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.
    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.
  6. 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.
    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.
    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.
  7. 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,” in 23rd International Conference on Parallel Computational Fluid Dynamics  (ParCFD’11), 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.
    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.
    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, pp. 22--32, 2011.
  8. 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.
    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.
    3. D. Göddeke, “Fast and Accurate Finite-Element Multigrid Solvers for PDE Simulations  on GPU Clusters,” PhD dissertation, Technische Universität Dortmund, Fakultät für Mathematik, 2010.
    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.
    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, pp. 75--82, 2010.
    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.
    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.
    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, pp. 2247--2265, 2010.
  9. 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.
    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, pp. 254--269, 2009.
    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, pp. 2534--2543, 2009.
  10. 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.
    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, pp. 36--55, 2008.
    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,” in International Supercomputing Conference (ISC’08), 2008.
  11. 2007

    1. D. Göddeke et al., “Exploring weak scalability for FEM calculations on a GPU-enhanced  cluster,” Parallel Computing, vol. 33, no. 10--11, pp. 685--699, 2007.
    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, pp. 221--256, 2007.
  12. 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.
    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.
  13. 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.
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)
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
2017 Joint Research Group Doing tomography differently: building the imaging tools for tomorrow (CLEARVIEW), proposal within the joint ANR/DFG programme, together with Dimitri Komatitsch (speaker) and Sébastien Chevrot. Proportional amount: 276.900€, 01/2018–12/2020, DFG grant number GO1758/4-1.
2017 Adaptive mixed-precision schemes based on computable error bounds, project proposal within the Extension Programme of the Cluster of Excellence SimTech, Stuttgart University. Proportional amount: one full PhD position (plus travel and consumables), 11/2017–12/2018, grant number PN2-24.
2016 Lead PI: SimTech CUDA Teaching Center, NVIDIA Corporation, 6/2016–7/2017. Amount: one high-end GPU, and developer support.
2016 Joint grant proposal for the HPC-II initiative of the Baden-Württemberg Stiftung: DiHu: Towards a digital human: Providing new possibilities to improve our understanding of the neuromuscular system by switching from small-sized cluster model problems to realistic simulations on HPC clusters together with O. Röhrle, M. Mehl and T. Ertl. Proportional amount 208.177€, 11/2016–10/2019.
2016 Sub-project „Innovative Smartphone-Teaching“ in the QuaLIKiSS 2 proposal of
the University of Stuttgart. BMBF, proportional amount 137.089€, 10/2016–09/2020.
2016 EXA-DUNE: Flexible PDE Solvers, Numerical Methods and Applications, joint grant proposal for the second phase of the Priority Programme „Software for Exascale Computing (SPP-1648)“ of the German Research Foundation, together with P. Bastian (Heidelberg), S. Turek (Dortmund), M. Ohlberger and Ch. Engwer (Münster), O. Ippisch (Clausthal) and O. Iliev (Kaiserslautern). Proportional amount 192.100€, 01/2016–12/2018, DFG grant number GO 1758/2-2.
2015 Hardware-adaptive and self-balancing algorithms and data structures for the numerical simulation of PDE problems, project proposal within the Cluster of Excellence SimTech, Stuttgart University. Proportional amount: one full PhD position (plus travel and consumables), 11/2015–10/2018, grant number PN2-17.
2013 Scalable, recursively configurable, massively-parallel FEM-multigrid solvers for heterogeneous computer architectures, German Research Foundation, together with S. Turek (Dortmund). Proportional amount 121.900€, 08/2013–07/2016, DFG grant number GO 1758/3-1.
2013 Asynchronous and fault-tolerant multigrid methods for future HPC systems, Mercator Research Center Ruhr (MERCUR), Initial Funding Programme. Amount: 45.475€, 06/2013–05/2014, grant number An-2013-0019.
2013 EXA-DUNE: Flexible PDE Solvers, Numerical Methods and Applications, joint grant proposal for the Priority Programme „Software for Exascale Computing (SPP-1648)“ of the German Research Foundation, together with P. Bastian and O. Ippisch (Heidelberg), S. Turek (Dortmund), M. Ohlberger and Ch. Engwer (Münster) and O. Iliev (Kaiserslautern). Proportional amount 185.200€, 01/2013–12/2015, DFG grant number GO 1758/2-1.
2012 Inclusion in the NVIDIA CUDA Teaching Center Program to support GPU Computing teaching activities. Amount: six GPUs, textbooks and 4600$, 07/2012–06/2013.