威尼斯赌博游戏_威尼斯赌博app-【官网】

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威尼斯赌博游戏_威尼斯赌博app-【官网】

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High-Performance Scientific Computing

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Snapshot: Guest talk by HLRS scientist Johannes Gebert

Today on 6th May 2025, Johannes Gebert of the High-Performance Computing Center Stuttgart ( HRLS) visited us at the HPSC Lab at the 威尼斯赌博游戏_威尼斯赌博app-【官网】 of Augsburg. He gave a guest talk as part of the Advanced Analytics and Predictive Sciences Seminar, titled "Enabling Massively Parallel Simulations of Directly Discretized CT Scans of Human Bones" (see abstract below). Furthermore, we had some interesting discussions on future computing in the HPC context and on the direction of the German and European HPC landscape. Thanks a lot for your visit, Johannes!

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Abstract

Quantifying the patient-specific elastic behavior of human bone can improve the design of implants, for example in total hip- or knee replacements. We calculate the mechanical stiffness tensor of human bone by direct discretization of computed tomography (CT) scans with more than 18 billion voxels. The software started as a serial implementation but now requires different massively parallel parameterizations, which poses significant challenges. Many computations may run ideally parallel; one computation may be a single large-scale job or any other variant. We solved the challenge with MPI and PETSc to distribute the linear algebraic system, allowing for computing large volumes of interest (VoIs) in bone. At the same time, pre- and post-processing are still serially implemented.
We will examine the challenges in the software's legacy components and its performance bottlenecks. Furthermore, we will show the perspective of the high-performance computing center in which the user deploys the simulation and measures to, e.g., optimize for short turnaround times.

Together with Arpit Babbar and Hendrik Ranocha, we have submitted our paper "Automatic differentiation for Lax-Wendroff-type discretizations".

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arXiv:2506.11719 reproduce me!

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Abstract

Lax-Wendroff methods combined with discontinuous Galerkin/flux reconstruction spatial discretization provide a high-order, single-stage, quadrature-free method for solving hyperbolic conservation laws. In this work, we introduce automatic differentiation (AD) in the element-local time average flux computation step (the predictor step) of Lax-Wendroff methods. The application of AD is similar for methods of any order and does not need positivity corrections during the predictor step. This contrasts with the approximate Lax-Wendroff procedure, which requires different finite difference formulas for different orders of the method and positivity corrections in the predictor step for fluxes that can only be computed on admissible states. The method is Jacobian-free and problem-independent, allowing direct application to any physical flux function. Numerical experiments demonstrate the order and positivity preservation of the method. Additionally, performance comparisons indicate that the wall-clock time of automatic differentiation is always on par with the approximate Lax-Wendroff method.

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