SPP 1648:  Software für Exascale Computing

• Fachliche Zuordnung: Informatik, System- und Elektrotechnik ; Biologie ; Geowissenschaften ; Maschinenbau und Produktionstechnik ; Mathematik ; Physik ; Wärmetechnik/Verfahrenstechnik
• Förderung: Förderung von 2012 bis 2019
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 214420555

• Erstellungsjahr: 2020

Zusammenfassung der Projektergebnisse

Keine Zusammenfassung vorhanden

Projektbezogene Publikationen (Auswahl)

• Using automated performance modeling to find scalability bugs in complex codes. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, p. 45. IEEE, Piscataway (2013)
  Calotoiu, A., Hoefler, T., Poke, M., Wolf, F.
  
• DASH: data structures and algorithms with support for hierarchical locality. In: European Conference on Parallel Processing, pp. 542–552. Springer, Berlin (2014)
  Fürlinger, K., Glass, C., Gracia, J., Knüpfer, A., Tao, J., Hünichnich, D., Idrees, K., Maiterth, M., Mhedheb, Y., Zhou, H.
  
• EXA-DUNE: flexible PDE solvers, numerical methods and applications. In: European Conference on Parallel Processing, pp. 530–541. Springer, Berlin (2014)
  Bastian, P., Engwer, C., Göddeke, D., Iliev, O., Ippisch, O., Ohlberger, M., Turek, S., Fahlke, J., Kaulmann, S., Steffen Müthing, S., et al.
  
• Increasing the performance of the Jacobi–Davidson method by blocking. SIAM J. Sci. Comput. 37(6), C697–C722 (2015)
  Röhrig-Zöllner, M., Thies, J., Kreutzer, M., Alvermann, A., Pieper, A., Basermann, A., Hager, G., Wellein, G., Fehske, H.
  
• Portable node-level performance optimization for the fast multipole method. In: Recent Trends in Computational Engineering-CE2014, pp. 29–46. Springer, Berlin (2015)
  Beckmann, A., Kabadshow, I.
  
• Time-parallel gravitational collapse simulation. Commun. Appl. Math. Comput. Sci. 12(1), 109–128 (2015)
  Kreienbuehl, A., Benedusi, P., Ruprecht, D., Krause, R.
  
• Global communication schemes for the numerical solution of high-dimensional PDEs. Parallel Comput. 52, 78–105 (2016)
  Hupp, P., Heene, M., Jacob, R., Pflüger, D.
  
• High-performance implementation of Chebyshev filter diagonalization for interior eigenvalue computations. J. Comput. Phys. 325, 226– 243 (2016)
  Pieper, A., Kreutzer, M., Alvermann, A., Galgon, M., Fehske, H., Hager, G., Lang, B., Wellein, G.
  
• Parallel coupling numerics for partitioned fluid–structure interaction simulations. Comput. Math. Appl. 71(4), 869–891 (2016)
  Mehl, M., Uekermann, B., Bijl, H., Blom, D., Gatzhammer, B., Van Zuijlen, A.
  
• Resilience for massively parallel multigrid solvers. SIAM J. Sci. Comput. 38(5), S217–S239 (2016)
  Huber, M., Gmeiner, B., Rüde, U., Wohlmuth, B.
  
• A highly scalable, algorithm-based fault-tolerant solver for gyrokinetic plasma simulations. In: Proceedings of the 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, pp. 1–8 (2017)
  Obersteiner, M., Hinojosa, A.P., Heene, M., Bungartz, H.J., Pflüger, D.
  
• A scala prototype to generate multigrid solver implementations for different problems and target multi-core platforms. Int. J. Comput. Sci. Eng. 14(2), 150–163 (2017)
  Köstler, H., Schmitt, C., Kuckuk, S., Kronawitter, S., Hannig, F., Teich, J., Rüde, U., Lengauer, C.
  
• Isoefficiency in practice: configuring and understanding the performance of task-based applications. In: ACM SIGPLAN Notices, vol. 52, pp. 131–143. ACM, New York (2017)
  Shudler, S., Calotoiu, A., Hoefler, T., Wolf, F.
  
• Toward decoupling the selection of compression algorithms from quality constraints. In: International Conference on High Performance Computing, pp. 3–14. Springer, Berlin (2017)
  Kunkel, J., Novikova, A., Betke, E., Schaare, A.
  
• A high-level C++ approach to manage local errors, asynchrony and faults in an MPI application. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 714–721. IEEE, Piscataway (2018)
  Engwer, C., Altenbernd, M., Dreier, N.A., Göddeke, D.
  
• Automating the development of high-performance multigrid solvers. Proc. IEEE 106(11), 1969– 1984 (2018)
  Schmitt, C., Kronawitter, S., Hannig, F., Teich, J., Lengauer, C.
  
• Efficiency of high-performance discontinuous Galerkin spectral element methods for under-resolved turbulent incompressible flows. Int. J. Numer. Methods Fluids 88(1), 32–54 (2018)
  Fehn, N., Wall, W.A., Kronbichler, M.
  
• GekkoFS—a temporary distributed file system for HPC applications. In: 2018 IEEE International Conference on Cluster Computing (CLUS- TER), pp. 319–324. IEEE, Piscataway (2018)
  Vef, M.A., Moti, N., Süß, T., Tocci, T., Nou, R., Miranda, A., Cortes, T., Brinkmann, A.
  
• Performance portability of earth system models with user-controlled GGDML code translation. In: International Conference on High Performance Computing, pp. 693–710. Springer, Berlin (2018)
  Jum’ah, N., Kunkel, J.
  
• Space-time FE-DG Discretization of the anisotropic diffusion equation in any dimension: the spectral symbol. SIAM J. Matrix Anal. Appl. 39(3), 1383–1420 (2018)
  Benedusi, P., Garoni, C., Krause, R., Li, X., Serra-Capizzano, S.
  
• Thread-local concurrency: a technique to handle data race detection at programming model abstraction. In: Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing, pp. 144–155 (2018)
  Protze, J., Schulz, M., Ahn, D.H., Müller, M.S.
  
• A new load balancing approach for coupled multi-physics simulations. In: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 676–682. IEEE, Piscataway (2019)
  Totounferoush, A., Pour, N.E., Schröder, J., Roller, S., Mehl, M.
  
• Engineering a distributed histogram sort. In: 2019 IEEE International Conference on Cluster Computing (CLUSTER), pp. 1–11. IEEE, Piscataway (2019)
  Kowalewski, R., Jungblut, P., Fürlinger, K.
  
• Fast matrix-free evaluation of discontinuous Galerkin finite element operators. ACM Trans. Math. Softw. 45(3), 1–40 (2019)
  Kronbichler, M., Kormann, K.
  
• FFMK: an HPC OS based on the L4Re microkernel. In: Operating Systems for Supercomputers and High Performance Computing, pp. 335–357. Springer, Berlin (2019)
  Weinhold, C., Lackorzynski, A., Härtig, H.
  
• Large-scale simulation of mantle convection based on a new matrix-free approach. J. Comput. Sci. 31, 60–76 (2019)
  Bauer, S., Huber, M., Ghelichkhan, S., Mohr, M., Rüde, U., Wohlmuth, B.
  
• More bang for your buck: Improved use of GPU nodes for GROMACS 2018. J. comput. chem. 40(27), 2418–2431 (2019)
  Kutzner, C., Páll, S., Fechner, M., Esztermann, A., de Groot, B.L., Grubmüller, H.
  
• Multilevel checkpoint/restart for large computational jobs on distributed computing resources. In: IEEE 38th Symposium on Reliable Distributed System (SRDS) (2019)
  Gholami, M., Schintke, F.
  
• On the quality of wall time estimates for resource allocation prediction. In: Proceedings of the 48th International Conference on Parallel Processing: Workshops, pp. 1–8. ACM, New York (2019)
  Soysal, M., Berghoff, M., Klusáček, D., Streit, A.
  
• Myx—runtime correctness analysis for multi-level parallel programming paradigms. In: Software for Exascale Computing: SPPEXA 2016– 2019. Lecture Notes in Computational Science and Engineering. Springer, Berlin (2020)
  Protze, J., Tsuji, M., Terboven, C., Dufaud, T., Murai, H., Petiton, S., Emad, N., Müller, M., Boku, T.