Professur für Höchstleistungsrechnen


Description:

Die Forschungsaktivitäten der Professur sind an der Schnittstelle zwischen numerischer Anwendung und modernen parallelen Hochleistungsrechnern angesiedelt. Zentrales Arbeitsgebiet ist die effiziente Implementierung, Optimierung und Parallelisierung numerischer Methoden und Anwendungsprogrammen auf heterogenen, (hoch) parallelen Rechnern. Dabei werden innovative Optimierungs- und Parallelisierungsansätze entwickelt, welche sich an den besonderen Eigenschaften neuartiger Rechnerarchitekturen orientieren. Verfolgt wird bei den Forschungsarbeiten ein strukturierter Performancemodell-basierter Ansatz (Performance Engineering). Darüber hinaus werden einfache Werkzeuge entwickelt die den Prozess des Performance Engineering unterstützen. Anwendungsorientierte Schwerpunkte der Professur sind Stencil-basierte Applikationen sowie Basisoperationen und Eigenwertlöser für große dünn besetzte Systeme. Verbunden ist die Professur mit der Gruppenleitung der HPC Gruppe des Regionalen Rechenzentrums Erlangen. 

Address:
Martensstraße 3
91058 Erlangen


Research Fields

Performance Engineering
Werkzeuge für Performancemodellierung und Performanceanalyse
Hardwareeffiziente Bausteine für dünn besetzte lineare Algebra und stencil-basierten Verfahren


Related Project(s)

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(SPP 1648: Software for Exascale Computing):
SPPEXA: EXASTEEL II - Bridging Scales for Multiphase Steels
Prof. Dr. Gerhard Wellein
(01/01/2016 - 31/12/2018)


(SPP 1648: Software for Exascale Computing):
TERRA-NEO - Integrated Co-Design of an Exascale Earth Mantle Modeling Framework
Prof. Dr. Gerhard Wellein
(01/11/2012 - 31/12/2015)


(SPP 1648: Software for Exascale Computing):
SPPEXA: ESSEX - Equipping Sparse Solvers for Exascale
Prof. Dr. Gerhard Wellein
(01/11/2012 - 31/12/2015)


(SPP 1648: Software for Exascale Computing):
EXASTEEL - Bridging Scales for Multiphase Steels
Prof. Dr. Gerhard Wellein
(01/11/2012 - 31/12/2015)


(SPP 1648: Software for Exascale Computing):
ESSEX - Equipping Sparse Solvers for Exascale
Dr. Georg Hager; Prof. Dr. Gerhard Wellein
(01/11/2012 - 30/06/2019)



Publications (Download BibTeX)

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Alvermann, A., Basermann, A., Bungartz, H.J., Carbogno, C., Ernst, D., Fehske, H.,... Wellein, G. (2019). Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects. Japan Journal of Industrial and Applied Mathematics. https://dx.doi.org/10.1007/s13160-019-00360-8
Anzt, H., Kreutzer, M., Ponce, E., Peterson, G.D., Wellein, G., & Dongarra, J. (2018). Optimization and performance evaluation of the IDR iterative Krylov solver on GPUs. International Journal of High Performance Computing Applications, 32(2), 220-230. https://dx.doi.org/10.1177/1094342016646844
Kreutzer, M., Ernst, D., Bishop, A.R., Fehske, H., Hager, G., Nakajima, K., & Wellein, G. (2018). Chebyshev filter diagonalization on modern manycore processors and GPGPUs. Springer Verlag.
Wittmann, M., Haag, V., Zeiser, T., Köstler, H., & Wellein, G. (2018). Lattice Boltzmann benchmark kernels as a testbed for performance analysis. Computers & Fluids, 172, 582-592. https://dx.doi.org/10.1016/j.compfluid.2018.03.030
Wittmann, M., Hager, G., Janalik, R., Lanser, M., Klawonn, A., Rheinbach, O.,... Wellein, G. (2018). Multicore Performance Engineering of Sparse Triangular Solves Using a Modified Roofline Model. In 2018 30TH INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING (SBAC-PAD 2018) (pp. 233-241). Lyon, FR: NEW YORK: IEEE.
Shahzad, F., Thies, J., Kreutzer, M., Zeiser, T., Hager, G., & Wellein, G. (2018). CRAFT: A library for easier application-level Checkpoint/Restart and Automatic Fault Tolerance. IEEE Transactions on Parallel and Distributed Systems. https://dx.doi.org/10.1109/TPDS.2018.2866794
Laukemann, J., Hammer, J., Hofmann, J., Hager, G., & Wellein, G. (2018). Automated Instruction Stream Throughput Prediction for Intel and AMD Microarchitectures. In 2018 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) (pp. 121-131). Dallas, TX, US: IEEE.
Hofmann, J., Fey, D., Riedmann, M., Eitzinger, J., Hager, G., & Wellein, G. (2017). Performance analysis of the Kahan-enhanced scalar product on current multi-core and many-core processors. Concurrency and Computation-Practice & Experience, 29(9). https://dx.doi.org/10.1002/cpe.3921
Hammer, J., Eitzinger, J., Hager, G., & Wellein, G. (2017). Kerncraft: A Tool for Analytic Performance Modeling of Loop Kernels. In Niethammer C, Gracia J, Hilbrich T, Knüpfer A, Resch MM, Nagel WE (Eds.), Tools for High Performance Computing 2016 (pp. 1--22). Stuttgart, Germany: Cham: Springer International Publishing.
Anzt, H., Gates, M., Dongarra, J., Kreutzer, M., Wellein, G., & Köhler, M. (2017). Preconditioned Krylov solvers on GPUs. Parallel Computing, 68, 32-44. https://dx.doi.org/10.1016/j.parco.2017.05.006
Hofmann, J., Hager, G., Wellein, G., & Fey, D. (2017). An Analysis of Core- and Chip-Level Architectural Features in Four Generations of Intel Server Processors. In High Performance Computing. ISC 2017. Lecture Notes in Computer Science, vol 10266. Frankfurt: Cham: Springer.
Galgon, M., Krämer, L., Lang, B., Alvermann, A., Fehske, H., Pieper, A.,... Thies, J. (2017). Improved coefficients for polynomial filtering in ESSEX. In Proceedings of the 1st InternationalWorkshop on Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing, EPASA 2015 (pp. 63-79). Springer Verlag.
Röhl, T., Eitzinger, J., Hager, G., & Wellein, G. (2017). LIKWID monitoring stack: A flexible framework enabling job specific performance monitoring for the masses. In Proceedings of the 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 (pp. 781-784). Institute of Electrical and Electronics Engineers Inc..
Wellein, G., Alvermann, A., Fehske, H., Hager, G., Kreutzer, M., Lang, B.,... Galgon, M. (2016). High-performance implementation of Chebyshev filter diagonalization for interior eigenvalue computations. Journal of Computational Physics, 325, 226-243. https://dx.doi.org/10.1016/j.jcp.2016.08.027
Hager, G., Eitzinger, J., Habich, J., & Wellein, G. (2016). Exploring performance and power properties of modern multi-core chips via simple machine models. Concurrency and Computation-Practice & Experience, 28(2), 189-210. https://dx.doi.org/10.1002/cpe.3180
Hofmann, J., Fey, D., Riedman, M., Eitzinger, J., Hager, G., & Wellein, G. (2016). Performance analysis of the Kahan-enhanced scalar product on current multi-corecore and many-core processors. Concurrency and Computation-Practice & Experience, 28(12). https://dx.doi.org/10.1002/cpe.3921
Anzt, H., Dongarra, J., Kreutzer, M., Wellein, G., & Köhler, M. (2016). Efficiency of general Krylov methods on GPUs - An experimental study. In Proceedings of the 30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016 (pp. 683-691). IEEE Computer Society.
Hofmann, J., Fey, D., Eitzinger, J., Hager, G., & Wellein, G. (2016). Analysis of intel’s haswell microarchitecture using the ECM model and microbenchmarks. Springer Verlag.
Kreutzer, M., Thies, J., Röhrig-Zöllner, M., Pieper, A., Shahzad, F., Galgon, M.,... Wellein, G. (2016). GHOST: Building Blocks for High Performance Sparse Linear Algebra on Heterogeneous Systems. International Journal of Parallel Programming, 1-27. https://dx.doi.org/10.1007/s10766-016-0464-z
Thies, J., Galgon, M., Shahzad, F., Alvermann, A., Kreutzer, M., Pieper, A.,... Wellein, G. (2016). Towards an exascale enabled sparse solver repository. Springer Verlag.


Publications in addition (Download BibTeX)


Shahzad, F., Thies, J., Kreutzer, M., Zeiser, T., Hager, G., & Wellein, G. (2018). CRAFT: A library for easier application-level Checkpoint/Restart and Automatic Fault Tolerance. IEEE Transactions on Parallel and Distributed Systems. https://dx.doi.org/10.1109/TPDS.2018.2866794

Last updated on 2019-24-04 at 10:15