The DUNE-ALUGrid Module

In this paper we present the new DUNE-ALUGrid module. This module contains a major overhaul of the sources from the ALUgrid library and the binding to the DUNE software framework. The main changes include user defined load balancing, parallel grid construction, and an redesign of the 2d grid which can now also be used for parallel computations. In addition many improvements have been introduced into the code to increase the parallel efficiency and to decrease the memory footprint. The original ALUGrid library is widely used within the DUNE community due to its good parallel performance for problems requiring local adaptivity and dynamic load balancing. Therefore, this new model will benefit a number of DUNE users. In addition we have added features to increase the range of problems for which the grid manager can be used, for example, introducing a 3d tetrahedral grid using a parallel newest vertex bisection algorithm for conforming grid refinement. In this paper we will discuss the new features, extensions to the DUNE interface, and explain for various examples how the code is used in parallel environments.Comment: 25 pages, 11 figure

A robust adaptive algebraic multigrid linear solver for structural mechanics

The numerical simulation of structural mechanics applications via finite elements usually requires the solution of large-size and ill-conditioned linear systems, especially when accurate results are sought for derived variables interpolated with lower order functions, like stress or deformation fields. Such task represents the most time-consuming kernel in commercial simulators; thus, it is of significant interest the development of robust and efficient linear solvers for such applications. In this context, direct solvers, which are based on LU factorization techniques, are often used due to their robustness and easy setup; however, they can reach only superlinear complexity, in the best case, thus, have limited applicability depending on the problem size. On the other hand, iterative solvers based on algebraic multigrid (AMG) preconditioners can reach up to linear complexity for sufficiently regular problems but do not always converge and require more knowledge from the user for an efficient setup. In this work, we present an adaptive AMG method specifically designed to improve its usability and efficiency in the solution of structural problems. We show numerical results for several practical applications with millions of unknowns and compare our method with two state-of-the-art linear solvers proving its efficiency and robustness.Comment: 50 pages, 16 figures, submitted to CMAM

A Hybrid Grid Compressible Flow Solver for Large-Scale Supersonic Jet Noise Simulations on Multi-GPU Clusters

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97132/1/AIAA2012-564.pd

Highly parallel multi-physics simulation of muscular activation and EMG

Simulation of skeletal muscle activation can help to interpret electromyographic measurements and infer the behavior of the muscle fibers. Existing models consider simplified geometries or a low number of muscle fibers to reduce the computation time. We demonstrate how to simulate a finely-resolved model of biceps brachii with a typical number of 270.000 fibers. We have used domain decomposition to run simulations on 27.000 cores of the supercomputer HazelHen at HLRS in Stuttgart, Germany. We present details on opendihu, our software framework. Its configurability, efficient data structures and modular software architecture target usability, performance and extensibility for future models. We present good parallel weak scaling of the simulations

AllScale API

Effectively implementing scientific algorithms in distributed memory parallel applications is a difficult task for domain scientists, as evident by the large number of domain-specific languages and libraries available today attempting to facilitate the process. However, they usually provide a closed set of parallel patterns and are not open for extension without vast modifications to the underlying system. In this work, we present the AllScale API, a programming interface for developing distributed memory parallel applications with the ease of shared memory programming models. The AllScale API is closed for a modification but open for an extension, allowing new user-defined parallel patterns and data structures to be implemented based on existing core primitives and therefore fully supported in the AllScale framework. Focusing on high-level functionality directly offered to application developers, we present the design advantages of such an API design, detail some of its specifications and evaluate it using three real-world use cases. Our results show that AllScale decreases the complexity of implementing scientific applications for distributed memory while attaining comparable or higher performance compared to MPI reference implementations