Research

Scientific Computing

Research Topics, projects, and software developments

Publications (Puma, under construction)

  1. 2023

    1. Pollinger, T., Rentrop, J., Pflüger, D., Kormann, K.: A stable and mass-conserving sparse grid combination technique with biorthogonal hierarchical basis functions for kinetic simulations. Journal of Computational Physics. (2023). https://doi.org/10.1016/j.jcp.2023.112338.
    2. Breyer, M., Van Craen, A., Pflüger, D.: Performance Evolution of Different SYCL Implementations Based on the Parallel Least Squares Support Vector Machine Library. In: Proceedings of the 2023 International Workshop on OpenCL. Association for Computing Machinery, Cambridge, United Kingdom (2023). https://doi.org/10.1145/3585341.3585369.
    3. Breyer, M., Van Craen, A., Pflüger, D.: Performance Evolution of Different SYCL Implementations Based on the Parallel Least Squares Support Vector Machine Library. In: Proceedings of the 2023 International Workshop on OpenCL. Association for Computing Machinery, Cambridge, United Kingdom (2023). https://doi.org/10.1145/3585341.3585369.
    4. Breyer, M., Van Craen, A., Pflüger, D.: Performance Evolution of Different SYCL Implementations Based on the Parallel Least Squares Support Vector Machine Library. In: ACM (ed.) Proceedings of the 2023 International Workshop on OpenCL. Association for Computing Machinery, Cambridge, United Kingdom (2023). https://doi.org/10.1145/3585341.3585369.

  2. 2022

    1. Van Craen, A., Breyer, M., Pflüger, D.: PLSSVM—Parallel Least Squares Support Vector Machine. Software Impacts. 100343 (2022). https://doi.org/10.1016/j.simpa.2022.100343.
    2. Van Craen, A., Breyer, M., Pflüger, D.: PLSSVM—Parallel Least Squares Support Vector Machine. Software Impacts. 100343 (2022). https://doi.org/10.1016/j.simpa.2022.100343.
    3. Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench Datasets, https://doi.org/10.18419/darus-2986, (2022). https://doi.org/10.18419/darus-2986.
    4. Van Craen, A., Breyer, M., Pflüger, D.: PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 818–827 (2022). https://doi.org/10.1109/IPDPSW55747.2022.00138.
    5. Leiteritz, R., Davis, K., Schulte, M., Pflüger, D.: Deep Learning-Based Surrogate Modelling of Thermal Plumes for Shallow Subsurface Temperature Approximation. In: AI for Earth Sciences ICLR Workshop 2022 (2022).
    6. Leiteritz, R., Buchfink, P., Haasdonk, B., Pflüger, D.: Surrogate-data-enriched Physics-Aware Neural Networks. In: Proceedings of the Northern Lights Deep Learning Workshop 2022 (2022). https://doi.org/10.7557/18.6268.
    7. Van Craen, A., Breyer, M., Pflüger, D.: PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 818–827 (2022). https://doi.org/10.1109/IPDPSW55747.2022.00138.
    8. Breyer, M., Van Craen, A., Pflüger, D.: A Comparison of SYCL, OpenCL, CUDA, and OpenMP for Massively Parallel Support Vector Machine Classification on Multi-Vendor Hardware. In: International Workshop on OpenCL. pp. 1–12. Association for Computing Machinery, Bristol, United Kingdom, United Kingdom (2022). https://doi.org/10.1145/3529538.3529980.
    9. Breyer, M., Van Craen, A., Pflüger, D.: A Comparison of SYCL, OpenCL, CUDA, and OpenMP for Massively Parallel Support Vector Machine Classification on Multi-Vendor Hardware. In: International Workshop on OpenCL. pp. 1–12. Association for Computing Machinery, Bristol, United Kingdom, United Kingdom (2022). https://doi.org/10.1145/3529538.3529980.
    10. Breyer, M., Van Craen, A., Pflüger, D.: A Comparison of SYCL, OpenCL, CUDA, and OpenMP for Massively Parallel Support Vector Machine Classification on Multi-Vendor Hardware. In: International Workshop on OpenCL. pp. 1–12. Association for Computing Machinery, Bristol, United Kingdom, United Kingdom (2022). https://doi.org/10.1145/3529538.3529980.
    11. Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks (2022).
    12. Van Craen, A., Breyer, M., Pflüger, D.: PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine, http://arxiv.org/abs/2202.12674, (2022).
    13. Leiteritz, R., Davis, K., Schulte, M., Pflüger, D.: A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps, https://arxiv.org/abs/2203.14961, (2022). https://doi.org/10.48550/ARXIV.2203.14961.

  3. 2021

    1. Leiteritz, R., Hurler, M., Pflüger, D.: Learning Free-Surface Flow with Physics-Informed Neural Networks. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). pp. 1664–1669 (2021). https://doi.org/10.1109/ICMLA52953.2021.00266.
    2. Pollinger, T., Hurler, M., Obersteiner, M., Pflüger, D.: Distributing Higher-Dimensional Simulations Across Compute Systems: A Widely Distributed Combination Technique. In: 2021 IEEE/ACM International Workshop on Hierarchical Parallelism for Exascale Computing (HiPar). pp. 1--9 (2021). https://doi.org/10.1109/HiPar54615.2021.00006.
    3. Breyer, M., Daiß, G., Pflüger, D.: Performance-Portable Distributed k-Nearest Neighbors using Locality-Sensitive Hashing and SYCL. In: International Workshop on OpenCL. pp. 1–12. Association for Computing Machinery, Munich, Germany (2021). https://doi.org/10.1145/3456669.3456692.
    4. Daiß, G., Simberg, M., Reverdell, A., Biddiscombe, J., Pollinger, T., Kaiser, H., Pflüger, D.: Beyond Fork-Join: Integration of Performance Portable Kokkos Kernels with HPX. In: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 377--386 (2021). https://doi.org/10.1109/IPDPSW52791.2021.00066.
    5. Rehme, M.F., Roberts, S.G., Pfluger, D.: Uncertainty quantification for the Hokkaido Nansei--Oki tsunami using B-splines on adaptive sparse grids. In: McLean, W., Macnamara, S., and Bunder, J. (eds.) Proceedings of the 19th Biennial Computational Techniques and Applications Conference, CTAC-2020. pp. C30--C44 (2021). https://doi.org/10.21914/anziamj.v62i0.16121.
    6. Leiteritz, R., Pflüger, D.: How to Avoid Trivial Solutions in Physics-Informed Neural Networks, https://arxiv.org/abs/2112.05620, (2021).
    7. Leiteritz, R., Hurler, M., Pflüger, D.: Learning Free-Surface Flow with Physics-Informed Neural Networks, http://arxiv.org/abs/2111.09705, (2021).

  4. 2020

    1. Pollinger, T., Pflüger, D.: Learning-Based Load Balancing for Massively Parallel Simulations of Hot Fusion Plasmas. Advances in Parallel Computing. 36, 137–146 (2020). https://doi.org/10.3233/APC200034.
    2. Brunn, M., Himthani, N., Biros, G., Mehl, M., Mang, A.: Fast GPU 3D Diffeomorphic Image Registration. arXiv preprint arXiv:2004.08893. (2020).
    3. Hirschmann, S., Kronenburg, A., Glass, C.W., Pflüger, D.: Load-Balancing for Large-Scale Soot Particle Agglomeration Simulations. In: Foster, I., Joubert, G.R., Kucera, L., Nagel, W.E., and Peters, F. (eds.) Parallel Computing: Technology Trends. pp. 147--156. IOS Press (2020). https://doi.org/10.3233/APC200035.
    4. Lago, R., Obersteiner, M., Pollinger, T., Rentrop, J., Bungartz, H.-J., Dannert, T., Griebel, M., Jenko, F., Pflüger, D.: EXAHD: A massively parallel fault tolerant sparse grid approach for high-dimensional turbulent plasma simulations. In: Software for Exascale Computing-SPPEXA 2016-2019. pp. 301--329. Springer International Publishing (2020).
    5. Hirschmann, S., Kronenburg, A., Glass, C.W., Pflüger, D.: Load-Balancing for Large-Scale Soot Particle Agglomeration Simulations. In: Foster, I., Joubert, G.R., Kucera, L., Nagel, W.E., and Peters, F. (eds.) Parallel Computing: Technology Trends. pp. 147--156. IOS Press (2020). https://doi.org/10.3233/APC200035.
    6. Van Craen, A.: PISM Performance Profiling - Analyse einer Eisschild Simulation, http://elib.uni-stuttgart.de/handle/11682/11604, (2020). https://doi.org/10.18419/OPUS-11587.
    7. Breyer, M.: Distributed k-nearest neighbors using Locality Sensitive Hashing and SYCL, http://dx.doi.org/10.18419/opus-11586, (2020).
    8. Müller, S.: Symplectic Neural Networks, (2020).
    9. de Wolff, T., Pflüger, D., Rehme, M.F., Heuer, J., Bittner, M.-I.: Evaluation of Pool-Based Testing Approaches to Enable Population-wide Screening for COVID-19, (2020).
    10. de Wolff, T., Pflüger, D., Rehme, M.F., Heuer, J., Bittner, M.-I.: Evaluation of Pool-Based Testing Approaches to Enable Population-wide Screening for COVID-19, (2020).

  5. 2019

    1. Hirschmann, S., Glass, C.W., Pflüger, D.: Enabling unstructured domain decompositions for inhomogeneous short-range molecular dynamics in ESPResSo. The European Physical Journal Special Topics. 227, 1779--1788 (2019). https://doi.org/10.1140/epjst/e2019-800159-0.
    2. Hirschmann, S., Glass, C.W., Pflüger, D.: Enabling unstructured domain decompositions for inhomogeneous short-range molecular dynamics in ESPResSo. The European Physical Journal Special Topics. 227, 1779--1788 (2019). https://doi.org/10.1140/epjst/e2019-800159-0.
    3. Brunn, M., Himthani, N., Biros, G., Mehl, M., Mang, A.: Fast 3D diffeomorphic image registration on GPUs. (2019).
    4. Pfander, D., Daiß, G., Pflüger, D.: Heterogeneous Distributed Big Data Clustering on Sparse Grids. Algorithms. 12, 60 (2019).
    5. Pfander, D., Daiß, G., Pflüger, D.: Heterogeneous Distributed Big Data Clustering on Sparse Grids. Algorithms. 12, 60 (2019).
    6. Rehme, M.F., Pflüger, D.: Stochastic Collocation with hierarchical extended B-splines on Sparse Grids. In: Fasshauer, G.E., Neamtu, M., and Schumaker, L. (eds.) Approximation Theory XVI. Springer (2019).
    7. Rehme, M.F., Pflüger, D.: Stochastic Collocation with hierarchical extended B-splines on Sparse Grids. In: Fasshauer, G.E., Neamtu, M., and Schumaker, L. (eds.) Approximation Theory XVI. Springer (2019).
    8. Rehme, M.F., Pflüger, D.: Active Subspaces with B-splines on Sparse Grids. In: Proceedings of the 3rd ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering (2019).
    9. Daiß, G., Amini, P., Biddiscombe, J., Diehl, P., Frank, J., Huck, K., Kaiser, H., Marcello, D., Pfander, D., Pflüger, D.: From Piz Daint to the Stars: Simulation of Stellar Mergers Using High-level Abstractions. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 62:1--62:37. ACM, Denver, Colorado (2019). https://doi.org/10.1145/3295500.3356221.
    10. Daiß, G., Amini, P., Biddiscombe, J., Diehl, P., Frank, J., Huck, K., Kaiser, H., Marcello, D., Pfander, D., Pflüger, D.: From Piz Daint to the Stars: Simulation of Stellar Mergers Using High-level Abstractions. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 62:1--62:37. ACM, Denver, Colorado (2019). https://doi.org/10.1145/3295500.3356221.
    11. Rehme, M.F., Pflüger, D.: Active Subspaces with B-splines on Sparse Grids. In: Proceedings of the 3rd ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering (2019).
    12. Rehme, M.F., Franzelin, F., Pflüger, D.: B-Splines on Sparse Grids for Surrogates in Uncertainty Quantification, (2019).
    13. Rehme, M.F., Franzelin, F., Pflüger, D.: B-Splines on Sparse Grids for Surrogates in Uncertainty Quantification, (2019).

  6. 2018

    1. Valentin, J., Sprenger, M., Pflüger, D., Röhrle, O.: Gradient-Based Optimization with B-Splines on Sparse Grids for Solving Forward-Dynamics Simulations of Three-Dimensional, Continuum-Mechanical Musculoskeletal System Models. International Journal for Numerical Methods in Biomedical Engineering. 1--16 (2018). https://doi.org/10.1002/cnm.2965.
    2. Hirschmann, S., Lahnert, M., Schober, C., Brunn, M., Mehl, M., Pflüger, D.: Load-Balancing and Spatial Adaptivity for Coarse-Grained Molecular Dynamics Applications. In: Nagel, W.E., Kröner, D.H., and Resch, M.M. (eds.) High Performance Computing in Science and Engineering ’18. pp. 1--510. Springer International Publishing (2018). https://doi.org/10.1007/978-3-030-13325-2.
    3. Pfander, D., Daiß, G., Marcello, D., Kaiser, H., Pflüger, D.: Accelerating Octo-Tiger : Stellar Mergers on Intel Knights Landing with HPX. In: Deakin, T. (ed.) IWOCL’18 : Proceedings of the International Workshop on OpenCL. pp. 68–75. ACM (2018). https://doi.org/10.1145/3204919.3204938.
    4. Pfander, D., Brunn, M., Pflüger, D.: AutoTuneTMP: Auto-Tuning in C++ With Runtime Template Metaprogramming. In: IPDPS Workshops. pp. 1123–1132. IEEE Computer Society (2018).
    5. Hirschmann, S., Lahnert, M., Schober, C., Brunn, M., Mehl, M., Pflüger, D.: Load-Balancing and Spatial Adaptivity for Coarse-Grained Molecular Dynamics Applications. In: Nagel, W.E., Kröner, D.H., and Resch, M.M. (eds.) High Performance Computing in Science and Engineering ’18. pp. 1--510. Springer International Publishing (2018). https://doi.org/10.1007/978-3-030-13325-2.
    6. Pfander, D., Daiß, G., Pflüger, D., Marcello, D., Kaiser, H.: Accelerating Octo-Tiger: Stellar Mergers on Intel Knights Landing with HPX. In: Proceedings of the 6th International Workshop on OpenCL. pp. 1--9. ACM (2018).
    7. Pfander, D., Brunn, M., Pflüger, D.: AutoTuneTMP: Auto-Tuning in C++ With Runtime Template Metaprogramming. In: 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 1--10. IEEE (2018).
    8. Valentin, J., Pflüger, D.: Fundamental Splines on Sparse Grids and Their Application to Gradient-Based Optimization. In: Garcke, J., Pflüger, D., Webster, C.G., and Zhang, G. (eds.) Sparse Grids and Applications - Miami 2016. pp. 229--251. Springer (2018). https://doi.org/10.1007/978-3-319-75426-0_10.
    9. Hirschmann, S., Lahnert, M., Schober, C., Brunn, M., Mehl, M., Pflüger, D.: Load-Balancing and Spatial Adaptivity for Coarse-Grained Molecular Dynamics Applications. In: Nagel, W.E., Kröner, D.H., and Resch, M.M. (eds.) High Performance Computing in Science and Engineering ’18. pp. 1--510. Springer International Publishing (2018). https://doi.org/10.1007/978-3-030-13325-2.
    10. Pfander, D., Daiß, G., Marcello, D., Kaiser, H., Pflüger, D.: Accelerating Octo-Tiger : Stellar Mergers on Intel Knights Landing with HPX. In: Deakin, T. (ed.) IWOCL’18 : Proceedings of the International Workshop on OpenCL. pp. 68–75. ACM (2018). https://doi.org/10.1145/3204919.3204938.
    11. Daiß, G.: Octo-Tiger: Binary Star Systems with HPX on Nvidia P100, (2018).
    12. Daiß, G.: Octo-Tiger: Binary Star Systems with HPX on Nvidia P100, (2018).
    13. Van Craen, A.: GPU-beschleunigte Support-Vector Machines, http://elib.uni-stuttgart.de/handle/11682/10165, (2018). https://doi.org/10.18419/OPUS-10148.
    14. Heene, M.: A Massively Parallel Combination Technique for the Solution of High-Dimensional PDEs, (2018).
    15. Rehme, M.F., Pflüger, D.: Hierarchical Extended B-splines for Approximations on Sparse Grids, (2018).
    16. Rehme, M.F., Pflüger, D.: Hierarchical Extended B-splines for Approximations on Sparse Grids, (2018).

  7. 2017

    1. Inci, G., Kronenburg, A., Weeber, R., Pflüger, D.: Langevin Dynamics Simulation of Transport and Aggregation of Soot Nano-particles in Turbulent Flows. Flow, Turbulence and Combustion. 1--21 (2017). https://doi.org/10.1007/s10494-016-9797-3.
    2. Hirschmann, S., Brunn, M., Lahnert, M., Glass, C.W., Mehl, M., Pflüger, D.: Load balancing with p4est for Short-Range Molecular Dynamics with ESPResSo. In: Advances in Parallel Computing. pp. 455--464. IOS Press (2017). https://doi.org/10.3233/978-1-61499-843-3-455.
    3. Diehl, P., Bußler, M., Pflüger, D., Frey, S., Ertl, T., Sadlo, F., Schweitzer, M.A.: Extraction of Fragments and Waves After Impact Damage in Particle-Based Simulations. In: Meshfree Methods for Partial Differential Equations VIII. pp. 17--34. Springer International Publishing (2017). https://doi.org/10.1007/978-3-319-51954-8_2.
    4. Hirschmann, S., Brunn, M., Lahnert, M., Glass, C.W., Mehl, M., Pflüger, D.: Load balancing with p4est for Short-Range Molecular Dynamics with ESPResSo. In: Advances in Parallel Computing. pp. 455--464. IOS Press (2017). https://doi.org/10.3233/978-1-61499-843-3-455.
    5. Hirschmann, S., Brunn, M., Lahnert, M., Glass, C.W., Mehl, M., Pflüger, D.: Load balancing with p4est for Short-Range Molecular Dynamics with ESPResSo. In: Advances in Parallel Computing. pp. 455--464. IOS Press (2017). https://doi.org/10.3233/978-1-61499-843-3-455.
    6. Heene, M., Hinojosa, A.P., Bungartz, H.-J., Pflüger, D.: A Massively-Parallel, Fault-Tolerant Solver for High-Dimensional PDEs. In: Desprez, F. and al., E. (eds.) Euro-Par 2016: Parallel Processing Workshops. pp. 635--647. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58943-5_51.
    7. Peherstorfer, B., Pflüger, D., Bungartz, H.-J.: Density Estimation with Adaptive Sparse Grids for Large Data Sets. In: Proceedings of the 2014 SIAM International Conference on Data Mining. pp. 443--451. SIAM (2017). https://doi.org/10.1137/1.9781611973440.51.
    8. Brunn, M.: Coupling of Particle Simulation and Lattice Boltzmann Background Flow on Adaptive Grids, http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=MSTR-0036&engl=1, (2017).

  8. 2016

    1. Diehl, P., Franzelin, F., Pflüger, D., Ganzenmüller, G.C.: Bond-based peridynamics: a quantitative study of Mode I crack opening. International Journal of Fracture. 1--14 (2016). https://doi.org/10.1007/s10704-016-0119-5.
    2. Hupp, P., Heene, M., Jacob, R., Pflüger, D.: Global communication schemes for the numerical solution of high-dimensional PDEs. Parallel Computing. 52, 78--105 (2016).
    3. Garcke, J., Pflüger, D. eds: Sparse Grids and Applications - Stuttgart 2014. Springer International Publishing (2016).
    4. Franzelin, F., Pflüger, D.: From Data to Uncertainty: An Efficient Integrated Data-Driven Sparse Grid Approach to Propagate Uncertainty. In: Sparse Grids and Applications - Stuttgart 2014. pp. 29--49. Springer International Publishing (2016). https://doi.org/10.1007/978-3-319-28262-6_2.
    5. Heene, M., Hinojosa, A.P., Bungartz, H.-J., Pflüger, D.: A massively-parallel, fault-tolerant solver for high-dimensional PDEs. In: European Conference on Parallel Processing. pp. 635--647 (2016).
    6. Heene, M., Pflüger, D.: Scalable Algorithms for the Solution of Higher-Dimensional PDEs. In: Bungartz, H.-Joachim., Philipp, N., and Wolfgang, N. (eds.) Software for Exascale Computing - SPPEXA 2013-2015. pp. 165--186. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40528-5_8.
    7. Hirschmann, S., Pflüger, D., Glass, C.W.: Towards Understanding Optimal Load-Balancing of Heterogeneous Short-Range Molecular Dynamics. In: 2016 IEEE 23rd International Conference on High Performance Computing Workshops (HiPCW). pp. 130--141. IEEE (2016). https://doi.org/10.1109/HiPCW.2016.027.
    8. Pfander, D., Heinecke, A., Pflüger, D.: A New Subspace-Based Algorithm for Efficient Spatially Adaptive Sparse Grid Regression, Classification and Multi-evaluation. In: Garcke, J. and Pflüger, D. (eds.) Sparse Grids and Applications - Stuttgart 2014. pp. 221--246. Springer International Publishing (2016).
    9. Hirschmann, S., Pflüger, D., Glass, C.W.: Towards Understanding Optimal Load-Balancing of Heterogeneous Short-Range Molecular Dynamics. In: 2016 IEEE 23rd International Conference on High Performance Computing Workshops (HiPCW). pp. 130--141. IEEE (2016). https://doi.org/10.1109/HiPCW.2016.027.
    10. Hegland, M., Harding, B., Kowitz, C., Pflüger, D., Strazdins, P.: Recent Developments in the Theory and Application of the Sparse Grid Combination Technique. In: Bungartz, H.-J., Neumann, P., and Nagel, W.E. (eds.) Software for Exascale Computing - SPPEXA 2013-2015. pp. 143--163. Springer International Publishing (2016). https://doi.org/10.1007/978-3-319-40528-5_7.
    11. Valentin, J., Pflüger, D.: Hierarchical Gradient-Based Optimization with B-Splines on Sparse Grids. In: Garcke, J. and Pflüger, D. (eds.) Sparse Grids and Applications - Stuttgart 2014. pp. 315--336. Springer (2016). https://doi.org/10.1007/978-3-319-28262-6_13.
    12. Pflüger, D., Pfander, D.: Computational Efficiency vs. Maintainability and Portability. Experiences with the Sparse Grid Code SG++. In: 2016 Fourth International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering (SE-HPCCSE). pp. 17--25. IEEE, Salt Lake City, UT, USA (2016).
    13. Brunn, M.: Data-mining on adaptively refined sparse grids with higher order basis functions on GPUs, http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=BCLR-2479&engl=1, (2016).

  9. 2015

    1. Peherstorfer, B., Kowitz, C., Pflüger, D., Bungartz, H.-J.: Selected Recent Applications of Sparse Grids. Numerical Mathematics: Theory, Methods and Applications. 8, 47--77 (2015).
    2. Hinojosa, A.P., Kowitz, C., Heene, M., Pflüger, D., Bungartz, H.-J.: Towards a fault-tolerant, scalable implementation of GENE. In: Proceedings of ICCE 2014. Springer-Verlag (2015).
    3. Wagner, S., Pflüger, D., Mehl, M.: Simulation Software Engineering: Experiences and Challenges. In: Proceedings of the 3rd International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering. pp. 1--4. ACM (2015). https://doi.org/10.1145/2830168.2830171.
    4. Heene, M., Pflüger, D.: Efficient and scalable distributed-memory hierarchization algorithms for the sparse grid combination technique. In: Parallel Computing: On the Road to Exascale, Proceedings of the International Conference on Parallel Computing, ParCo 2015, 1-4 September 2015, Edinburgh, Scotland, UK. pp. 339--348 (2015). https://doi.org/10.3233/978-1-61499-621-7-339.
    5. Heene, M., Pflüger, D.: Efficient and scalable distributed-memory hierarchization algorithms for the sparse grid combination technique. In: Parallel Computing: On the Road to Exascale. pp. 339--348. IOS Press (2015). https://doi.org/10.3233/978-1-61499-621-7-339.

  10. 2014

    1. Bungartz, H.-J., Heinecke, A., Pflüger, D., Schraufstetter, S.: Parallelizing a Black-Scholes solver based on finite elements and sparse grids. Concurrency and Computation: Practice and Experience. 1640--1653 (2014). https://doi.org/10.1002/cpe.2837.
    2. Garcke, J., Pflüger, D. eds: Sparse Grids and Applications - Munich 2012. Springer International Publishing (2014).
    3. Pflüger, D., Bungartz, H.-J., Griebel, M., Jenko, F., Dannert, T., Heene, M., Hinojosa, A.P., Kowitz, C., Zaspel, P.: EXAHD: An Exa-Scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond. In: Euro-Par 2014: Parallel Processing Workshops. Springer-Verlag (2014).
    4. Hupp, P., Jacob, R., Heene, M., Pflüger, D., Hegland, M.: Global Communication Schemes for the Sparse Grid Combination Technique. In: Bader, M., Bungartz, H.-J., Bode, A., Gerndt, M., and Joubert, G.R. (eds.) Parallel Computing: Accelerating Computational Science and Engineering (CSE). pp. 564--573. IOS Press, Amsterdam (2014).
    5. Khakhutskyy, V., Pflüger, D., Hegland, M.: Scalability and Fault Tolerance of the Alternating Direction Method of Multipliers for Sparse Grids. In: Bader, M., Bungartz, H.-J., Bode, A., Gerndt, M., and Joubert, G.R. (eds.) Parallel Computing: Accelerating Computational Science and Engineering (CSE). pp. 603--612. IOS Press, Amsterdam (2014).
    6. Khakhutskyy, V., Pflüger, D.: Alternating Direction Method of Multipliers for Hierarchical Basis Approximators. In: Garcke, J. and Pflüger, D. (eds.) Sparse Grids and Applications - Munich 2012. pp. 221--238. Springer International Publishing (2014).
    7. Peherstorfer, B., Franzelin, F., Pflüger, D., Bungartz, H.-J.: Classification with Probability Density Estimation on Sparse Grids. In: Garcke, J. and Pflüger, D. (eds.) Sparse Grids and Applications - Munich 2012. pp. 255--270. Springer International Publishing (2014).
    8. Heene, M., Kowitz, C., Pflüger, D.: Load Balancing for Massively Parallel Computations with the Sparse Grid Combination Technique. In: Parallel Computing: Accelerating Computational Science and Engineering (CSE). pp. 574--583 (2014).
    9. Franzelin, F., Diehl, P., Pflüger, D., Schweitzer, M.A.: Non-intrusive uncertainty quantification with sparse grids for multivariate peridynamic simulations. In: Griebel, M. and Schweitzer, M.A. (eds.) Meshfree Methods for Partial Differential Equations VII. Sonstige (2014).
    10. Hirschmann, S.: GPU-Based Regression Analysis on Sparse Grids. In: Plödereder, E., Grunske, L., Schneider, E., and Ull, D. (eds.) Informatik 2014, Big Data - Komplexität meistern. pp. 2425--2436. Gesellschaft für Informatik e.V., Bonn (2014).
    11. Buse, G., Pflüger, D., Jacob, R.: Efficient Pseudorecursive Evaluation Schemes for Non-Adaptive Sparse Grids. In: Garcke, J. and Pflüger, D. (eds.) Sparse Grids and Applications - Munich 2012. pp. 1--27. Springer International Publishing (2014).
    12. Heene, M., Kowitz, C., Pflüger, D.: Load Balancing for Massively Parallel Computations with the Sparse Grid Combination Technique. In: Bader, M., Bungartz, H.-J., Bode, A., Gerndt, M., and Joubert, G.R. (eds.) Parallel Computing: Accelerating Computational Science and Engineering (CSE). pp. 574--583. IOS Press, Amsterdam (2014).

  11. 2013

    1. Heinecke, A., Pflüger, D.: Emerging Architectures Enable to Boost Massively Parallel Data Mining using Adaptive Sparse Grids. International Journal of Parallel Programming. 41, 357--399 (2013).
    2. Heinecke, A., Karlstetter, R., Pflüger, D., Bungartz, H.-J.: Data Mining on Vast Datasets as a Cluster System Benchmark. Concurrency and Computation: Practice and Experience. (2013).
    3. Hans-Joachim, B., Stefan, Z., Martin, B., Pflüger, D.: Modellbildung und Simulation: Eine anwendungsorientierte Einführung. Springer-Verlag, Berlin (2013).
    4. Hans-Joachim, B., Stefan, Z., Martin, B., Pflüger, D.: Modeling and Simulation - An Application-Oriented Introduction. Springer-Verlag, Berlin (2013).
    5. Heinecke, A., Pflüger, D., Budnikov, D., Klemm, M., Narkis, A., Shevtsov, M., Zaks, A.: Demonstrating Performance Portability of A Custom OpenCL Data Mining Application to the Intel(R) Xeon Phi(TM) Coprocessor. In: International Workshop on OpenCL Proceedings 2013. Sonstige, Georgia, Tech (2013).
    6. Peherstorfer, B., Adorf, J., Pflüger, D., Bungartz, H.-J.: Image Segmentation with Adaptive Sparse Grids. In: Proceedings of 26th Australasian Joint Conference on Artificial Intelligence. Springer (2013).

  12. 2012

    1. Heinecke, A., Klemm, M., Pflüger, D., Bode, A., Bungartz, H.-J.: Extending a Highly Parallel Data Mining Algorithm to the Intel(R) Many Integrated Core Architecture. In: Euro-Par 2011: Parallel Processing Workshops: Proceedings of the 4th Workshop on UnConventional High Performance Computing 2011 (UCHPC 2011). pp. 375--384. Springer, Bordeaux, France (2012).
    2. Pflüger, D.: Spatially Adaptive Refinement. In: Garcke, J. and Griebel, M. (eds.) Sparse Grids and Applications. pp. 243--262. Springer, Berlin, Heidelberg, (2012).
    3. Buse, G., Jacob, R., Pflüger, D., Murarasu, A.: A Non-static Data Layout Enhancing Parallelism and Vectorization in Sparse Grid Algorithms. In: Proceeding of the 11th International Symposium on Parallel and Distributed Computing - ISPDC 2012. IEEE, Munich (2012).
    4. Heinecke, A., Klemm, M., Pabst, H., Pflüger, D.: Towards high-performance implementations of a custom HPC kernel using Intel(R) Array Building Blocks. In: Facing the Multicore-Challenge II. pp. 36--47. Springer, Berlin (2012).
    5. Heinecke, A., Peherstorfer, B., Pflüger, D., Song, Z.: Sparse Grid Classifiers as Base Learners for AdaBoost. In: 2012 International Conference on High Performance Computing and Simulation (HPCS),. pp. 161--166. IEEE, Madrid (2012).
    6. Alin, M., Gerrit, B., Josef, W., Pflüger, D., Bode, A.: fastsg: A Fast Routines Library for Sparse Grids. In: Proceedings of the International Conference on Computational Science, ICCS 2012. Sonstige (2012).
    7. Butnaru, D., Buse, G., Pflüger, D.: A Parallel and Distributed Surrogate Model Implementation for Computational Steering. In: Proceeding of the 11th International Symposium on Parallel and Distributed Computing - ISPDC 2012. IEEE, Munich (2012).
    8. Kowitz, C., Pflüger, D., Jenko, F., Hegland, M.: The Combination Technique for the Initial Value Problem in Linear Gyrokinetics. In: Griebel, M. and Garcke, J. (eds.) Sparse Grids and Applications. pp. 205--222. Springer, Heidelberg (2012).
    9. Benk, J., Pflüger, D.: Hybrid Parallel Solutions of the Black-Scholes PDE with the Truncated Combination Technique. In: Proceedings of the HPCS conference. Sonstige, Madrid (2012).
    10. Peherstorfer, B., Pflüger, D., Bungartz, H.-J.: Clustering Based on Density Estimation with Sparse Grids. In: KI 2012: Advances in Artificial Intelligence. Springer (2012).
    11. Butnaru, D., Peherstorfer, B., Pflüger, D., Bungartz, H.-J.: Fast Insight into High-Dimensional Parametrized Simulation Data. In: 11th International Conference on Machine Learning and Applications (ICMLA). IEEE, Boca Raton, Florida (2012).

  13. 2011

    1. Bungartz, H.-J., Heinecke, A., Pflüger, D., Schraufstetter, S.: Option Pricing with a Direct Adaptive Sparse Grid Approach. Journal of Computational and Applied Mathematics. 236, 3741--3750 (2011).
    2. Alin, M., Josef, W., Gerrit, B., Butnaru, D., Pflüger, D.: Compact Data Structure and Scalable Algorithms for the Sparse Grid Technique. In: Proceedings of the 16th ACM symposium on Principles and practice of parallel programming. pp. 25--34. ACM, New York, Usa (2011).
    3. Heinecke, A., Pflüger, D.: Multi- and Many-Core Data Mining with Adaptive Sparse Grids. In: Proceedings of the 8th ACM International Conference on Computing Frontiers. pp. 29--29. ACM, New York, Usa (2011).
    4. Butnaru, D., Pflüger, D., Bungartz, H.-J.: Towards High-Dimensional Computational Steering of Precomputed Simulation Data using Sparse Grids. In: Proceedings of the International Conference on Computational Science (ICCS) 2011. pp. 56--65. Springer-Verlag (2011).
    5. Peherstorfer, B., Pflüger, D., Bungartz, H.-J.: A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps. In: Wang, D. and Reynolds, M. (eds.) AI 2011: Advances in Artificial Intelligence. pp. 112--121. Springer, Berlin, Heidelberg (2011).

  14. 2010

    1. Frommert, M., Pflüger, D., Riller, T., Reinecke, M., Bungartz, H.-J., Enßlin, T.: Efficient cosmological parameter sampling using sparse grids. Efficient cosmological parameter sampling using sparse grids. 1177--1189 (2010).
    2. Pflüger, D., Peherstorfer, B., Bungartz, H.-J.: Spatially adaptive sparse grids for high-dimensional data-driven problems. Journal of Complexity. 26, 508--522 (2010).
    3. Pflüger, D.: Spatially Adaptive Sparse Grids for High-Dimensional Problems. Verlag Dr. Hut, München (2010).
    4. Bungartz, H.-J., Heinecke, A., Pflüger, D., Schraufstetter, S.: Parallelizing a Black-Scholes Solver based on Finite Elements and Sparse Grids. In: IEEE International Parallel & Distributed Processing Symposium. Sonstige (2010).

  15. 2009

    1. Bungartz, H.-J., Zimmer, S., Buchholz, M., Pflüger, D.: Modellbildung und Simulation: Eine anwendungsorientierte Einführung. Springer-Verlag, Berlin, Heidelberg (2009).

  16. 2008

    1. Bungartz, H.-J., Pflüger, D., Zimmer, S.: Adaptive Sparse Grid Techniques for Data Mining. In: Bock, H.G., Kostina, E., Hoang, X.P., and Rannacher, R. (eds.) Modelling, Simulation and Optimization of Complex Processes 2006. pp. 121--130. Springer-Verlag, Berlin, Heidelberg (2008).

  17. 2007

    1. Pflüger, D., Muntean, I.L., Bungartz, H.-J.: Adaptive Sparse Grid Classification Using Grid Environments. In: Shi, Y., van Albada, D., Dongarra, J., and Sloot, P. (eds.) ICCS 2007. pp. 708--715. Springer, Berlin, Heidelberg, (2007).

  18. 2004

    1. Buchholz, M., Pflüger, D., Poon, J.: Application of Machine Learning Techniques to the Re-ranking of Search Results. In: Biundo, S., Frühwirth, T., and Palm, G. (eds.) KI 2004: Advances in Artificial Intelligence. pp. 67--81. Springer-Verlag (2004).
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