Numerical investigation of compressible flows faces two main challenges. In order to accurately describe the flow characteristics, high-resolution nonlinear numerical schemes are needed to capture discontinuities and resolve wide convective, acoustic and interfacial scale ranges. The simulation of realistic three-dimensional (3D) problems with state-of-the-art finite-volume method (FVM) based on approximate Riemann solvers with weighted nonlinear reconstruction schemes requires the usage of high-performance computing (HPC) architectures. Efficient compression algorithms reduce computational and memory load. Fully adaptive multiresolution (MR) algorithms with LTS have proven their potential for such applications. While modern central processing units (CPUs) requires multiple levels of parallelism to achieve peak performance, the fine-grained MR mesh adaptivity results in challenging compute/communication patterns. Moreover, local time stepping (LTS) incurs strong data dependencies which challenge a parallelization strategy. We address these challenges with a block-based MR algorithm, where arbitrary cuts in the underlying octree are possible. This allows for a parallelization on distributed-memory machines via the Message Passing Interface (MPI). We obtain neighbor relations by a simple bit-logic in a modified Morton Order. The block-based concept allows for a modular setup of the source code framework in which the building blocks of the algorithm, such as the choice of the Riemann solver or the reconstruction stencil, are interchangeable without loss of parallel performance. We present the capabilities of the modular framework with a range of test cases and scaling analysis with effective resolutions beyond one billion cells using O(104) cores. © 2021 Elsevier B.V     «

Numerical investigation of compressible flows faces two main challenges. In order to accurately describe the flow characteristics, high-resolution nonlinear numerical schemes are needed to capture discontinuities and resolve wide convective, acoustic and interfacial scale ranges. The simulation of realistic three-dimensional (3D) problems with state-of-the-art finite-volume method (FVM) based on approximate Riemann solvers with weighted nonlinear reconstruction schemes requires the usage of high...     »

Funding text 1 The authors would like to thank Felix Sp?th for his help in the implementation of the SFC and the communication cache. The first author would like to further thank Vladimir Bogdanov for helping with the bit-wise logic operations. The authors have received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 667483). The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. for funding this project by providing computing time on the CoolMUC-2 Linux-Cluster at Leibniz Supercomputing Centre. The authors acknowledge the Bavarian State Ministry of Science and the Arts for partial funding through the Competence Network for Scientific High Performance Computing in Bavaria (KONWIHR). Funding text 2 The authors acknowledge the Bavarian State Ministry of Science and the Arts for partial funding through the Competence Network for Scientific High Performance Computing in Bavaria (KONWIHR). Funding text 3 The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. for funding this project by providing computing time on the CoolMUC-2 Linux-Cluster at Leibniz Supercomputing Centre. Funding text 4 The authors have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 667483 ).      «

Funding text 1 The authors would like to thank Felix Sp?th for his help in the implementation of the SFC and the communication cache. The first author would like to further thank Vladimir Bogdanov for helping with the bit-wise logic operations. The authors have received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 667483). The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V....     »