Karl Meerbergen
KU Leuven, Computer Science, Faculty Member
- Linear algebra is a field for the success of many problems in scientific computing and data analytics. My expertise i... moreLinear algebra is a field for the success of many problems in scientific computing and data analytics. My expertise is in the solution of large scale matrix problems. In particular, this includes eigenvalue problems, model order reduction, Krylov methods, preconditioning and, high performance computing. I enjoy solving new challenging problems, designing new algorithms and analysing their mathematical and numerical properties, as well, as their efficiency on high end computers for relevant applications.edit
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ABSTRACT Optimization problems such as the parameter design of dynamical systems are often computationally expensive. In this paper, we apply Krylov based model order reduction techniques to the parameter design problem of an acoustic... more
ABSTRACT Optimization problems such as the parameter design of dynamical systems are often computationally expensive. In this paper, we apply Krylov based model order reduction techniques to the parameter design problem of an acoustic cavity to accelerate the computation of both function values and derivatives, and therefore, drastically improve the performance of the optimization algorithms. Two types of model reduction techniques are explored: conventional model reduction and parameterized model reduction. The moment matching properties of derivative computation via the reduced model are discussed. Numerical results show that both methods are efficient in reducing the optimization time.
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ABSTRACT The bulk synchronous parallel (BSP) model, as well as parallel programming interfaces based on BSP, classically target distributed-memory parallel architectures. In earlier work, Yzelman and Bisseling designed a MulticoreBSP for... more
ABSTRACT The bulk synchronous parallel (BSP) model, as well as parallel programming interfaces based on BSP, classically target distributed-memory parallel architectures. In earlier work, Yzelman and Bisseling designed a MulticoreBSP for Java library specifically for shared-memory architectures. In the present article, we further investigate this concept and introduce the new high-performance MulticoreBSP for C library. Among other features, this library supports nested BSP runs. We show that existing BSP software performs well regardless whether it runs on distributed-memory or shared-memory architectures, and show that applications in MulticoreBSP can attain high-performance results. The paper details implementing the Fast Fourier Transform and the sparse matrix–vector multiplication in BSP, both of which outperform state-of-the-art implementations written in other shared-memory parallel programming interfaces. We furthermore study the applicability of BSP when working on highly non-uniform memory access architectures.
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ABSTRACT Biologische en fysische systemen worden meer en meer bestudeerd via computersimulaties. Dit boek is een inleiding tot het programmeren en oplossen van wiskundige modellen aan de hand van het populaire rekenpakket MATLAB. Het boek... more
ABSTRACT Biologische en fysische systemen worden meer en meer bestudeerd via computersimulaties. Dit boek is een inleiding tot het programmeren en oplossen van wiskundige modellen aan de hand van het populaire rekenpakket MATLAB. Het boek bestaat uit twee delen: het eerste deel bevat een beschrijving van de mogelijkheden van MATLAB en een inleiding tot het programmeren in het algemeen; het tweede deel bestaat uit oefeningen. "Leren werken met MATLAB" is uitermate geschikt voor zelfstudie. Zo is het eerste deel rijk ge\"il\-lus\-treerd met eenvoudige voorbeelden, en ook met het tweede deel kan de lezer zelf aan de slag. De auteurs zijn verbonden aan het Departement Computerwetenschappen van de KU Leuven waar zij hun expertise in numerieke algoritmen en programmeren via intensief onderzoek hebben opgebouwd. Zij verzorgen eveneens verschillende cursussen op bachelor- en masterniveau.