A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters

In this work, we consider the solution of boundary integral equations by means of a scalable hierarchical matrix approach on clusters equipped with graphics hardware, i.e. graphics processing units (GPUs). To this end, we extend our existing single-GPU hierarchical matrix library hmglib such that it is able to scale on many GPUs and such that it can be coupled to arbitrary application codes. Using a model GPU implementation of a boundary element method (BEM) solver, we are able to achieve more than 67 percent relative parallel speed-up going from 128 to 1024 GPUs for a model geometry test case with 1.5 million unknowns and a real-world geometry test case with almost 1.2 million unknowns. On 1024 GPUs of the cluster Titan, it takes less than 6 minutes to solve the 1.5 million unknowns problem, with 5.7 minutes for the setup phase and 20 seconds for the iterative solver. To the best of the authors' knowledge, we here discuss the first fully GPU-based distributed-memory parallel hierarchical matrix Open Source library using the traditional H-matrix format and adaptive cross approximation with an application to BEM problems

Sparse Approximation of Non-Local Operators

[no abstract available

Fast Numerical Methods for Non-local Operators

[no abstract available

Finite elements on degenerate meshes: inverse-type inequalities and applications

In this paper we obtain a range of inverse-type inequalities which are applicable to finite-element functions on general classes of meshes, including degenerate meshes obtained by anisotropic refinement. These are obtained for Sobolev norms of positive, zero and negative order. In contrast to classical inverse estimates, negative powers of the minimum mesh diameter are avoided. We give two applications of these estimates in the context of boundary elements: (i) to the analysis of quadrature error in discrete Galerkin methods and (ii) to the analysis of the panel clustering algorithm. Our results show that degeneracy in the meshes yields no degradation in the approximation properties of these method

Numerical treatment of retarded boundary integral equations by sparse panel clustering

We consider the wave equation in a boundary integral formulation. The discretization in time is done by using convolution quadrature techniques and a Galerkin boundary element method for the spatial discretization. In a previous paper, we have introduced a sparse approximation of the system matrix by cut-off, in order to reduce the storage costs. In this paper, we extend this approach by introducing a panel clustering method to further reduce these cost

Adaptive Multi-Fidelity Modeling for Efficient Design Exploration Under Uncertainty

This thesis work introduces a novel multi-fidelity modeling framework, which is designed to address the practical challenges encountered in Aerospace vehicle design when 1) multiple low-fidelity models exist, 2) each low-fidelity model may only be correlated with the high-fidelity model in part of the design domain, and 3) models may contain noise or uncertainty. The proposed approach approximates a high-fidelity model by consolidating multiple low-fidelity models using the localized Galerkin formulation. Also, two adaptive sampling methods are developed to efficiently construct an accurate model. The first acquisition formulation, expected effectiveness, searches for the global optimum and is useful for modeling engineering objectives. The second acquisition formulation, expected usefulness, identifies feasible design domains and is useful for constrained design exploration. The proposed methods can be applied to any engineering systems with complex and demanding simulation models