Publications

Scientific Computing

Publications of the IPVS Scientific Computing group

Publications (Puma)

  1. 2024

    1. Schwachhofer, D., Domanski, P., Becker, S., Wagner, S., Sauer, M., Pflüger, D., Polian, I.: Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties. (2024).
    2. Schwachhofer, D., Domanski, P., Becker, S., Wagner, S., Sauer, M., Pflüger, D., Polian, I.: Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties, https://arxiv.org/abs/2403.10086, (2024).

  2. 2023

    1. Strack, A., Pflüger, D.: Scalability of Gaussian Processes Using Asynchronous Tasks: A Comparison Between HPX and PETSc. In: WAMTA 2023: Asynchronous Many-Task Systems and Applications (2023). https://doi.org/10.1007/978-3-031-32316-4_5.
    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. Domanski, P., Ray, A., Firouzi, F., Lafata, K., Chakrabarty, K., Pflüger, D.: Blood Glucose Prediction for Type-1 Diabetics using Deep Reinforcement Learning. In: 2023 IEEE International Conference on Digital Health (ICDH). pp. 339–347 (2023). https://doi.org/10.1109/ICDH60066.2023.00042.
    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.
    5. Pollinger, T., Van Craen, A., Niethammer, C., Breyer, M., Pflüger, D.: Leveraging the Compute Power of Two HPC Systems for Higher-Dimensional Grid-Based Simulations with the Widely-Distributed Sparse Grid Combination Technique. Presented at the November 12 (2023). https://doi.org/10.1145/3581784.3607036.
    6. Domanski, P., Pflüger, D., Latty, R.: Learn to Tune: Robust Performance Tuning in Post-Silicon Validation. In: 2023 IEEE European Test Symposium (ETS). pp. 1–4 (2023). https://doi.org/10.1109/ETS56758.2023.10174123.

  3. 2022

    1. Van Craen, A., Breyer, M., Pflüger, D.: PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine. 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 818–827 (2022). https://doi.org/10.1109/IPDPSW55747.2022.00138.
    2. Craen, A. van, Breyer, M., Pflüger, D.: PLSSVM : Parallel Least Squares Support Vector Machine. Software impacts. 14, 100343 (2022). https://doi.org/10.1016/j.simpa.2022.100343.
    3. 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.
    4. Van Craen, A., Breyer, M., Pflüger, D.: PLSSVM—Parallel Least Squares Support Vector Machine. Software Impacts. 14, 100343 (2022). https://doi.org/10.1016/j.simpa.2022.100343.
    5. 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.
    6. 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.
    7. Amrouch, H., Anders, J., Becker, S., Betka, M., Bleher, G., Domanski, P., Elhamawy, N., Ertl, T., Gatzastras, A., Genssler, P., Hasler, S., Heinrich, M., van Hoorn, A., Jafarzadeh, H., Kallfass, I., Klemme, F., Koch, S., Küsters, R., Lalama, A., Latty, R., Liao, Y., Lylina, N., Haghi, Z.N., Pflüger, D., Polian, I., Rivoir, J., Sauer, M., Schwachhofer, D., Templin, S., Volmer, C., Wagner, S., Weiskopf, D., Wunderlich, H.-J., Yang, B., Zimmermann, M.: Intelligent Methods for Test and Reliability. In: 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). pp. 969–974 (2022). https://doi.org/10.23919/DATE54114.2022.9774526.
    8. Domanski, P., Pflüger, D., Rivoir, J., Latty, R.: Self-Learning Tuning for Post-Silicon Validation, (2022).

  4. 2021

    1. Domanski, P., Pflüger, D., Latty, R., Rivoir, J.: ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems. In: Wani, M.A., Sethi, I.K., Shi, W., Qu, G., Raicu, D.S., and Jin, R. (eds.) 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). pp. 1357–1364. IEEE, Piscataway (2021). https://doi.org/10.1109/ICMLA52953.2021.00220.

  5. 2020

    1. 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.
    2. 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).

  6. 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. Pfander, D., Daiß, G., Pflüger, D.: Heterogeneous Distributed Big Data Clustering on Sparse Grids. Algorithms. 12, 60 (2019).
    3. 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.
    4. 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).
    5. 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).
    6. Rehme, M.F., Franzelin, F., Pflüger, D.: B-Splines on Sparse Grids for Surrogates in Uncertainty Quantification, (2019).

  7. 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. 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).
    3. 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).
    4. 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.
    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., 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.
    7. Daiß, G.: Octo-Tiger: Binary Star Systems with HPX on Nvidia P100, (2018).
    8. Rehme, M.F., Pflüger, D.: Hierarchical Extended B-splines for Approximations on Sparse Grids, (2018).

  8. 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. 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.
    3. 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.
    4. 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.
    5. 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.

  9. 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., 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.
    6. 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).
    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. 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.
    9. 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.
    10. 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).

  10. 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. pp. 339--348. IOS Press (2015). https://doi.org/10.3233/978-1-61499-621-7-339.

  11. 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. 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).
    9. 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).
    10. 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).

  12. 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).

  13. 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. 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).
    9. 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).
    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).

  14. 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).

  15. 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).

  16. 2009

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

  17. 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).

  18. 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).

  19. 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).
To the top of the page