SCCER-FURIES: Future Swiss Electrical Infrastructure
The main research challenge of the WP2 is to build a realistic technical model of the Swiss energy system including the transmission systems, which model can be used for planning, operation, and economic evaluation of the system. This model must comprise: (i) location of renewable generation and the limited predictability of these sources; (ii) location of storage devices, both large scale, i.e. pumped hydro storage, and distributed devices; (iii) interconnections with regional grids; (iv) interconnections on the bulk power level, i.e. high voltage lines and gas pipelines; (v) possibility to interface it with models for market and other economic simulations.
This work involves the modelling of individual components, which requires interaction with other WPs and possibly other SCCER, but also the development of a general system framework taking into account different energy carriers. Furthermore, since the model will be of very high order and complexity, it should be formulated in such a way that large-scale optimization methods could be applied. In this way different optimization objectives could be used in the planning and operation of the system. The results from the optimization have to be robust.
The developed models should cover both slowly varying phenomena, i.e. time scale of minutes, but also faster phenomena, i.e. time scale of seconds. The models used for studying and analysing phenomena of different time scales will be different but with the same architecture. Another research challenge is the dynamics of a system with considerably lower inertia than the existing systems. Innovative geoinformation system will be used to identify locations for renewable generation and storage devices. The developed model, achieved with real data related to the system, is expected to be a powerful tool for optimizing the future Swiss energy system in terms of planning and operation.
The project is supported by the SCCER-FURIES: Future Swiss Electrical Infrastructure. Period: July 2014 - Dec 2017.
ANSWERS: Accelerating nano-device simulations with extreme-scale algorithms and software co-integration
Nanosizing has revolutionized the design of electronic components to the point where their material properties and atomic configuration almost entirely determine their functionality. To accelerate the emergence of novel device concepts, advanced simulation tools relying on quantum mechanics and treating the different material regions at the atomic scale are needed. Electronic structure calculators and quantum transport simulators have established themselves as powerful engines to study the equilibrium and out-of-equilibrium properties of nanostructures. However, both approaches suffers from the same deficiencies: they are usually limited to small atomic systems and they are subject to lame compromises between short simulation times (empirical models) and accurate results (ab-initio approaches). These restrictions are mainly due to the underlying numerical algorithms, matrix diagonalizations for electronic structure calculations and sparse linear systems of equations for quantum transport problems, that do not scale well on large core numbers and poorly exploit the available computational resources.
In this PASC project, we will integrate general-purpose parallel numerical libraries and improved physical models to advance the state-of-the-art in electronic structure and quantum transport calculations of nanostructures. The treatment of larger atomic systems, the reduction of the simulation time, the improvement of the result accuracy, and the usage of novel computer architectures are the objectives of this project.
The project is supported by the Swiss Platform for Advanced Scientific Computing (PASC). Period: July 2014 - Dec 2016.
GeoPC: Infrastructure development for hybrid parallel smoothers for multigrid preconditioners
The goal of this project is to develop a computational infrastructure to enable massively parallel, scalable smoothers and coarse grid solvers to be used within multigrid preconditioners. This is intended to (i) facilitate the execution of high resolution, 3D geodynamic models of the planetary evolution; (ii) provide the Earth science community (and others) with a suite of continually maintained and re-usable HPC components to build robust multi-level preconditioners (iii) position Swiss computational geosciences in the emerging large-scale computing era.
The project is supported by the Swiss Platform for Advanced Scientific Computing (PASC). Period: Jan 2014 - Dec 2016.
EXA2CT: Exascale Algorithms and Advanced Computational Technique
Numerical simulation is a crucial part of science and industry in Europe. The advancement of simulation as a discipline relies on increasingly compute intensive models that require more computational resources to run. This is the driver for the evolution to exascale. Due to limits in the increase in single processor performance, exascale machines will rely on massive parallelism on and off chip, with a complex hierarchy of resources. The large number of components and the machine complexity introduce severe problems for reliability and programmability. The former of these will require novel fault-aware algorithms and support software. In addition, the scale of the numerical models exacerbates the difficulties by making the use of more complex simulation algorithms necessary, for numerical stability reasons. A key example of this is increased reliance on solvers. Such solvers require global communication, which impacts scalability, and are often used with preconditioners, increasing complexity again. Unless there is a major rethink of the design of solver algorithms, their components and software structure, a large class of important numerical simulations will not scale beyond petascale. This in turn will hold back the development of European science and industry which will fail to reap the benefits from exascale. The EXA2CT project brings together experts at the cutting edge of the development of solvers, related algorithmic techniques, and HPC software architects for programming models and communication. It will take a revolutionary approach to exascale solvers and programming models, rather than the incremental approach of other projects. We will produce modular open source proto-applications that demonstrate the algorithms and programming techniques developed in the project, to help boot-strap the creation of genuine exascale codes.
Project partners: IMEC (Belgium)), Intel Exascale Lab (France), University of Antwerpen (Belgium), INRIA (France), University of Versailles Saint-Quentin-en-Yvelines (France), T-Systems (Germany), Frauenhofer Gesellschaft (Germany), Technical University of Ostrava (Czech), NAG (UK)
The project is supported within the EU FP7-ICT programme. Period: Sep 2013 - Aug 2016.
EU project web page: www.exa2ct.eu
TORNAD: Towards extreme-scale coupled electrothermal simulations of realistic nano-devices
The goal of this project is to accelerate the simulation of realistic nano-devices with coupled electrothermal transport using advanced numerical methods on massively parallel architectures and a hierarchical modeling approach. The increase in heat dissipation and power consumption is currently reaching a critical level in integrated circuits. If this trend does not stop in the near future, it will no longer be possible to sufficiently cool down electronic devices, thus severely degrading their performance and lifetime. This problem is becoming even more important now that the size of the transistors, the active components of ICs, does not exceed a few nanometers  and their gate contact lets more and more electrons leak between source and drain in stand by mode.
The TORNAD project will engage in research along two directions to provide the device modeling community and the semiconductor industry with a beyond state-of-the-art quantum-transport simulation approach dedicated to nextgeneration nano-transistors such as 3-D Si, Ge, InGaAs nanowire and 2-D MoS2 transistors: USI will develop massively parallel and advanced sparse numerical linear algebra algorithms and implement it to solve the non-equilibrium Green’s function equations in the presence of dissipative scattering mechanisms. They will be benchmarked against existing techniques;
Prof. Andreas Schenk,ETH Zurich (Co-PI)
The project is supported by the Swiss National Science Foundation, grant SNF 200021_149454. Period: October 2013 - September 2016.
GeoScale: A framework for multiscale seismic modelling and inversion
Complex multi-scale interactions are characteristic for the physics of the Earth, and their proper quantification is key to the integration of inter-dependent geophysical systems that are today mostly treated as isolated. Resolving structures and processes on a wide range of interacting spatio-temporal scales is a unifying grand challenge in all branches of geophysics that must be addressed in order to achieve a comprehensive understanding of the Earth as a multiphysics system. As part of the Domain Science Network Solid Earth Dynamics, our goal is to address the challenge of multi-scale Earth modelling and inversion through a coordinated inter-disciplinary effort combined with long-term application support that ensures a sustained impact on the geoscientific community.
The project is supported by the Swiss Platform for Advanced Scientific Computing (PASC). Period: Sep 2013 - Aug 2016.
HPC Application Support for the PASC Solid Earth Dynamics Community
Resolving structures and processes on a wide range of interacting spatio-temporal scales is a unifying grand challenge in all branches of geophysics that must be addressed in order to achieve a comprehensive understanding of the Earth as a multi-physics system. Geophysical modelling and inversion across the scales heavily relies on modern HPC resources. This HPC dependence is particularly pronounced in geodynamic and seismic applications where convection processes and the propagation of elastic waves must be modelled with high accuracy in a broad spatio-temporal range. Applications include (i) waveform tomography from the exploration to the global scale, (ii) studies of earthquake-induced ground motion to quantify seismic risk, (iii) seismic source inversion and studies of earthquake rupture physics, (iv) the assimilation of geomagentic observations into dynamo simulations, and (v) the modelling of mantle convection and plate tectonics using increasingly realistic and highly nonlinear rheologies. A coordinated effort of HPC specialists and Earth scientists is needed to ensure that current and future computational resources can be harnessed optimally, and that progress continues to be made in the heavily HPC-dependent Solid Earth Sciences. This effort must include the long-term support of HPC applications, that already today requires expert knowledge and experience.
The project funds an HPC application support position shared between the Universita della Svizzera Italiana (USI) and ETH Zurich. This position will be used to (i) maintain the core HPC applications described in the Appendix at a state-of-the-art level, (ii) enable new HPC applications, (iii) develop large-scale visualisation tools, and (iv) educate both students and research staff on current HPC developments and methods. These actions will ensure both access to modern computational resources in Europe and the international competitiveness of the Swiss Solid Earth Sciences.
The project is supported by the Swiss Platform for Advanced Scientific Computing (PASC). Period: Sep 2013 - Aug 2018.
Prof. Domenico Giardini, ETH Zurich,
Prof. Stefan Wiemer, ETH Zurich
Prof. Johan Olof Anders Robertsson, ETH Zurich
Fast Methods for Frequency-Domain Full-Waveform Inversion in Strongly Heterogeneous Media
Many scientific and engineering problems — in such diverse areas as wave propagation in ultrasound tomography, wireless communication, geophysical seismic imaging, and other areas such as atmospheric sciences, image registration, medicine, structural-fluid interactions, and chemical process industry — can be expressed in the form of a PDE-constrained optimization problem. For instance, the difficult task of non-destructively investigating a solid body, such as the Earth’s interior, a piece of steel, or the human body only from surface measurements of propagating wave fields, can be formulated as a PDE-constrained optimization problem. Indeed, a distinct feature of waves propagating through a homogeneous medium is their ability to travel over long distances while retaining much of their shape and initial energy. Thus waves are ubiquitous for remote-sensing of well-defined bodies (e.g., micro-cracks, land mines) or more general inhomogeneities (e.g., tumor cells in medical imaging, or oil deposits in seismic imaging). The prediction of the scattered fields from known incident waves and given material properties is called simulation or forward problem. In the frequency domain, the forward problem in computational wave propagation is governed by the Helmholtz equation. In contrast, estimating the material properties from measured scattered fields is generally called the inverse problem. In seismology, the inverse problem is often called seismic imaging problem; the qualifier “full-waveform” is used, when the true Helmholtz equation without any approximation is used to model the propagating wave fields. Seismic imaging has experienced significant developments during the last decade. It can be either implemented in the time-domain or in the frequency-domain. Time-domain approaches require storing and accessing the whole time-history of the forward and the backward wave propagation, which can be prohibitively expensive in terms of computational time and storage. An alternative approach is to work in the frequency-domain formulation, i.e., the Helmholtz equation and its various generalizations. This approach is very attractive because it avoids storing the entire wave propagation history. However, the frequency approach is not widespread for three-dimensional seismic simulation or imaging applications due to the lack of efficient 3D Helmholtz solvers. Recent research on Helmholtz solvers and inexact interior-point optimization methods indicates that these algorithms can also be applied to large-scale frequency-domain full-waveform inversion. In particular, the imposition of a priori bounds to avoid false minima has proved very effective. Moreover, larger parallel architectures and new algorithms for wave propagation now provide the computational ability to simulate three-dimensional waves in heterogeneous media with greater accuracy. To achieve accurate full-wave form inversion in three space dimensions, we propose to extend recent developments in computational methods for nonlinear optimization and wave propagation, such as inverse calculation of selected entries in the Helmholtz operator, sweeping or moving boundary PML preconditioners, and their application on highly-parallel architectures. More specifically, the scientific goals of our project are: (1) to develop 3D Helmholtz preconditioning solvers for large-scale high-frequency 3D applications in strongly, (2) heterogeneous media, (3) to develop new inexact-Newton full-waveform inversion methods that include a priori bounds, (4), to apply these computational methods to realistic seismic applications.
The project is supported by theSwiss National Science Foundation, grant SNF 200021_140706. Period: Jan 2013 - Dec 2014.
Multiscale analysis and simulation of waves in strongly heterogeneous media
When a wave propagates through a homogeneous medium it retains its initial shape even over long distances. As it encounters an inhomogeneity, however, the wave interacts with the medium and complicated scattered wave patterns emerge. From that information, it is possible to infer characteristics of the inhomogeneity, such as its shape or density, even when buried deeply inside the medium. Many applications in science, engineering and medicine relie on the particular features of waves propagating through an inhomogeneous medium for remote sensing, such as geophysical imaging, ultrasound, non-destructive testing in material science, mine detection, or the design of meta-materials or photonic crystals. If the variations of the medium occur at a scale " much smaller than the size of the domain or the wave length, standard numerical methods become prohibitively expensive due to their need to discretize the entire computational domain down to the smallest scales. Thus we seek heterogeneous multiscale methods (HMM) that permit the simulation of waves propagating through strongly varying heterogenous media, at a computational cost independent of ". At later time, as the wave propagates through a strongly heterogeous medium, it develops a secondary wave train of dispersive nature, which is not captured by classical homogenization theory. Therefore we shall devise an HMM scheme for the wave equation in strongly heterogeous media, which applies in more general situations without precomputing the homogenized limit problem. Clearly to explore and discover unknown inhomogeneities deeply buried inside a medium, an efficient forward solver is not sufficient. By comparing the response from the simulation with true measurements, it is possible to iteratively improve upon the initial guess of the medium characteristics and determine the hidden scatterer. Such an inverse medium problem is probably best formulated as a PDE-constrained optimization problem. It is generally ill-posed, contains many (false) local solutions and it is usually significantly more difficult to solve than the forward problem. To overcome these difficulties, we shall devise numerical methods that guarantee superlinear global convergence, include inequality constraints to exclude unphysical false solutions, and handle large ill-conditioned indefinite linear systems.
Prof. Marcus Grote, University of Basel (PI)
Prof. Olaf Schenk, USI Lugano (Co-PI)
The project is supported by Swiss National Science Foundation, grant SNF 200020_130050, Period: Jan 2011 - Dec 2012.
Large-Scale Parallel Nonlinear Optimization for High Resolution 3D-Seismic Imaging
Current methods in global or local-scale seismic tomography rely on approximate descriptions of wave propagation with the result of severely limiting the resolution of tomographic images. However, to truly understand the dynamics of our planet, we need to be able to seismically map its deep structure at resolutions much higher than it is nowadays possible. Major geophysical questions that require high resolution 3D imaging at the planetary scale include a better understanding, e.g., of the nature of mantle plumes and sinking tectonic plates. At the regional scale, reliable seismic images are crucial for more accurate earthquake location and the compilation of seismic hazard maps. Recent advances in algorithms, software development, and high performance computing systems have resulted in PDE-based solvers that scale up to millions of variables, make use of thousands of processors, and accommodate complex multiple-physics. As partial differential equations (PDE) solvers also mature in the Earth Sciences, there is an increasing interest in solving nonlinear seismic inversion problems governed by PDE-based models. Larger computer architectures and new algorithms for optimization and wave propagation now provide the computational ability to address the geophysical issues mentioned above in a more rigorous way: namely, to abandon asymptotic ray-theory approximations in favor of time-dependent PDE-based models, and replace linearized inversions by truly nonlinear optimization. To achieve this goal, it will be necessary to combine recent developments in computational methods for nonlinear optimization and wave propagation, such as high-order finite element discretizations, local time-stepping, iterative methods, and inexact parallel interior-point methods. More specifically, the scientific goals of the project are: to develop parallel numerical methods for forward wave propagation and large-scale nonlinear optimization, to explore the performance of such methods on emerging petascale architectures and to develop a new generation of a seismic inversion code for 3D Earth imaging.
Prof. Olaf Schenk, USI Lugano (Principal Investigator)
Dr. Lapo Boschi, ETH Zurich (Co-PI)
Prof. Dominico Giardini, ETH Zurich (Co-PI)
Prof. Marcus Grote, University of Basel (Co-PI)
The project is supported by HP2C Initiative. Period: Jan 2010 - June 2013.
Courses in Spring 2016:
Courses in Fall 2015:
Courses in Spring 2015:
Courses in Fall 2014:
Courses in Spring 2014:
Special Topics in Informatics and Applied Mathematics and Computational Science (3 ECTS)
Parallel and Distributed Computing Lab (3 ECTS)
Computational Science (6 ECTS)
Courses in Fall 2013:
Parallel and Distributed Computing (6 ECTS)
Courses in Spring 2013:
Special Topics in Informatics and Applied Mathematics and Computational Science (3 ECTS)
Parallel and Distributed Computing Lab (3 ECTS)
Computer Simulations in Science and Engineering Summer school (3 ECTS)
Computational Science (6 ECTS)
Courses in Fall 2012:
Parallel and Distributed Computing (6 ECTS)
J. Brumm, D. Mikushin, S. Scheidegger, O. Schenk, Scalable High-Dimensional Dynamic Stochastic Economic Modeling, Journal of Computational Science, accepted, in press.
D. Korounis, O. Schenk, Constraint handling for gradient-based optimization of compositional reservoir flow, Journal of Computational Geosciences, accepted, in press.
M. Rietmann, M.J. Grote, D. Peter, O. Schenk, B. Ucar, Load-balanced local time stepping for large scale wave propagation, in Proceedings of the 29th IEEE International Parallel&Distributed Processing Symposium, IPDPS’15 (acceptance rate: 21.8%, 108/496), DOI:10.1109/IPDPS.2015.10
A. De Coninc, D. Kourounis, F. Verbosio, O. Schenk, B. De Baets, S.. Maenhout, J. Fostier, Towards Parallel Large-scale Genomic Prediction by Coupling Sparse and Dense Matrix Algebra, in Proceedings of the 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing,2015, DOI: http://dx.doi.org/10.1109/PDP.2015.94
C. Petra, O. Schenk, M. Anitescu, Real-time Stochastic Optimization of Complex Energy Systems on High Performance Computers, IEEE Computing in Science & Engineering - Leadership Computing (Volume:16, Issue: 5), pp. 32 - 42, DOI: 10.1109/MCSE.2014.53
D. Mikushin, N. Likhogrud, E. Z. Zhang, C. Bergström, KERNELGEN – the design and implementation of a next generation compiler platform for accelerating numerical models on GPUs, IPDPSW '14, Proceedings of the 2014 IEEE International Parallel \& Distributed Processing Symposium Workshops, pp. 1011-1020, http://dl.acm.org/citation.cfm?id=2672916
M. J. Grote, J. Huber, D. Kourounis, O. Schenk, Inexact Interior-Point Method for PDE-Constrained Nonlinear Optimization,SIAM J. Sci. Comput. 36-3 (2014),pp. A1251-A1276, http://dx.doi.org/10.1137/130921283
C. Petra, O. Schenk, M.Lubin, K. Gärtner, An augmented incomplete factorization approach for computing the Schur complement in stochastic optimization, SIAM J. Sci. Comput 36-2 (2014), pp. C139-C162, http://dx.doi.org/10.1137/130908737
G. Kollias, M. Sathe, O. Schenk, A. Grama, Fast Parallel Algorithms for Graph Similarity and Matching, Journal of Parallel and Distributed Computing, Volume 75, Issue 5, May 2014, pp. 2400–2410, http://dx.doi.org/10.1016/j.jpdc.2013.12.010
D. Kourounis, L.J. Durlofsky, J. D. Jansen, and K. Aziz, Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow, Journal of Computational Geosciences, pp.1-21, 2014. http://dx.doi.org/10.1007/s10596-013-9385-8
P. Sanan, S. Schnepp, D. May, O Schenk. Composite solvers for linear saddle point problems arising from the incompressible Stokes equations with highly heterogeneous viscosity structure. AGU Fall Meeting, San Francisco, Dec. 15-19, 2014
S. Wagner, M. Sathe, O. Schenk, Optimization for Process Plans in Sheet Metal Forming, The International Journal of Advanced Manufacturing Technology, Springer, DOI: 10.1007/s00170-013-5515-7, Dec. 2013.
P. Basini,T. Nissen-Meyer, L. Boschi, E. Casarotti, J. Verbeke, O. Schenk, D. Giardini, The influence of non-uniform ambient noise on crustal tomography in Europe, Accepted in Journal of Geochemistry, Geophysics, Geosystems (G-cubed)
M. Luisier, O. Schenk, Gate-Stack Engineering in n-type Ultra-Scaled Si Nanowire Field-Effect Transistors, IEEE Transactions on Electron Devices, vol. 60, no 10, pp. 3325-3329, Oct 2013.
A. Kuzmin, M. Luisier, O. Schenk, Fast Methods for Computing Selected Elements of the Green's Function in Massively Parallel Nanoelectronic Device Simulations, Euro-Par 2013 Conference,August 26-30, Accepted, in press.
L. Gaudio, M. J. Grote, O. Schenk, Interior point method for time-dependent inverse problems, 11th International Conference on Mathematical and Numerical Aspects of Wave, WAVE2013, June 3-7, 2013, Accepted, in press.
M. Sathe, O. Schenk, H. Burkhart, An Auction-Based Weighted Matching Implementation on Massively Parallel Architectures, Parallel Computing 38 (2012), pp. 595-614, http://dx.doi.org/10.1016/j.parco.2012.09.001
F. Curtis, J. Huber, O. Schenk, A. Wächter, A Note on the Implementation of an Interior-Point Algorithm for Nonlinear Optimization with Inexact Step Computations. Mathematical Programming B, pp. 1-19 (2012), Springer Berlin / Heidelberg, doi: 10.1007/s10107-012-0557-4.
M. Rietmann, O. Schenk, P. Messmer, T. Nissen-Meyer, D. Peter, P. Basini, D. Komatitsch, J. Tromp, L. Boschi, D. Giardini, Forward and Adjoint Simulations of Seismic Wave Propagation on Emerging Large-Scale GPU Architectures, ACM/IEEE Supercomputing 2012.
M. Christen, O. Schenk, Y. Cui, PATUS: Parallel Auto-Tuned Stencils For Scalable Earthquake Simulation Codes, ACM/IEEE Supercomputing 2012.
H. Burkhart, M. Sathe, M. Christen, M. Rietmann, O. Schenk, Run, Stencil, Run, HPC Productivity Studies in the Classroom, 6th Conference on Partitioned Global Address Space Programming Models, October 10-12, 2012, Santa Barbara, USA.
M. Christen, O. Schenk, PATUS: A Code Generation and Auto-Tuning Framework For Parallel Stencil Computations, Second International Workshop on Advances in High- Performance Computational Earth Sciences: Applications and Frameworks (IHPCES) in conjunction with the International Conference on Computational Science (ICCS 2012), June 4-6, 2012, Omaha, Nebraska, USA.
Combinatorial Scientific Computing, Uwe Naumann, Olaf Schenk (Editor), Publisher: Chapman and Hall/CRC, ISBN-10: 1439827354, ISBN-13: 978-1439827352
J. Huber, U. Naumann, O. Schenk, A. Wächter, Algorithmic Differentiation and Nonlinear Optimization for an Inverse Medium Problem, chapter in Combinatorial Scientific Computing by U. Nauman and O. Schenk (Editors), pp. 203-232, book in the Computational Science series from Chapman and Hall/CRC, ISBN-10: 1439827354, ISBN-13: 978-1439827352.
O. Schenk, M. Sathe, B. Ucar, A. Sameh, Towards A Scalable Hybrid Linear Solver Based On Combinatorial Algorithms, chapter in Combinatorial Scientific Computing by U. Nauman and O. Schenk (Editors), pp. 96-127, book in the Computational Science series from Chapman and Hall/CRC, ISBN-10: 1439827354, ISBN-13: 978-1439827352.
O. Schenk, M. Christen, H. Burkhart, Parallel Stencil Computations on Manycore Architectures in Hyperthermia Applications, Scientific Computing with Multicore and Accelerators by D. Bader and J. Dongarra (Editors), Computational Science series from Chapman & Hall / CRC Press, Taylor and Francis Group. pp. 255-277, ISBN: 978-1-4398253-6-5, 2011.
O. Schenk, K. Gärtner, Parallel Numerical Linear Algebra, invited book chapter in Encyclopedia of Parallel Computing, D. Padua (Editor), pp. 1458-1464, 2012, Springer, ISBN 978-0-387-09765-7.
P. Arbenz, Y. Saad, A. Sameh, O. Schenk: "Guest editorial: Special issue on Parallel Matrix Algorithms and Applications (PMAA'10)". Parallel Computing 37 (12): 731-732 (2011), doi:10.1016/j.parco.2011.10.011.
M. Christen, O. Schenk, and H. Burkhart, Automatic Code Generation and Tuning for Stencil Kernels on Modern Microarchitecture, Journal Computer Science Research and Development, Proceedings of the International Supercomputing Conference ISC11. Volume 26, pp. 205-210, 2011, DOI 10.1007/s00450-011-0160-6
F. Curtis, O. Schenk, and W. Waechter, An Interior-Point Algorithm for Large-Scale Nonlinear Optimization with Inexact Step Computations. SIAM J. Sci. Comput. Volume 32, Issue 6, pp. 3447-3475 (2010)
M. Christen, O. Schenk, and H. Burkhart, Patus: A Code Generation and Autotuning Framework For Parallel Iterative Stencil Computations on Modern Microarchitectures, 25th IEEE International Parallel Distributed Processing Symposium, May 16-20, 2011. Anchorage (Alaska) USA, in press.
Uwe Naumann, Olaf Schenk (Editor), Publisher: Chapman and Hall/CRC (Dec 15 2011) ISBN-10: 1439827354, ISBN-13: 978-1439827352
Combinatorial Scientific Computing explores the latest research on creating algorithms and software tools to solve key combinatorial problems on large-scale high-performance computing architectures. It includes contributions from international researchers who are pioneers in designing software and applications for high-performance computing systems. The book offers a state-of-the-art overview of the latest research, tool development, and applications. It focuses on load balancing and parallelization on high-performance computers, large-scale optimization, algorithmic differentiation of numerical simulation code, sparse matrix software tools, and combinatorial challenges and applications in large-scale social networks. The authors unify these seemingly disparate areas through a common set of abstractions and algorithms based on combinatorics, graphs, and hypergraphs. Combinatorial algorithms have long played a crucial enabling role in scientific and engineering computations and their importance continues to grow with the demands of new applications and advanced architectures. By addressing current challenges in the field, this volume sets the stage for the accelerated development and deployment of fundamental enabling technologies in high-performance scientific.
Hyperthermia cancer treatment simulation
Cell BE architecure for biomedical simulations
PDE-constrained optimization in a biomedical application
Simulation of acoustic and electromagnetic waves
The Anderson Model of Localization in Computational Physics
FE Modeling in Semiconductor Device Simulation
Graphs in Combinatorial Scientific Computing
Sparse Linear Algebra in Automobile Sheet Metal Forming
Cell processors of our IBM Faculty Award Project
External Scattered Field modelled with the Helmholtz Equation