A high-order finite element method based on Hermite interpolation polynomials is described and a software implementation in the deal.II finite element library ( https://dealii.org ) is proposed. This method can be used to strongly enforce continuity of spatial derivatives as well as solution value over element boundaries, which is necessary for solving fourth order partial differential equations (PDEs) in primal form and useful for some second order PDEs. The implementation is verified by solving the wave equation based on a modification of the PDE with a Lax–Wendroff-like procedure, allowing a high-order solution method with negligible additional computing time.

We present a novel, highly scalable and optimized solver for turbulent flows based on high-order discontinuous Galerkin discretizations of the incompressible Navier-Stokes equations aimed to minimize time-to-solution. The solver uses explicit-implicit time integration with variable step size. The central algorithmic component is the matrix-free evaluation of discretized finite element operators. The node-level performance is optimized by sum-factorization kernels for tensor-product elements with unique algorithmic choices that reduce the number of arithmetic operations, improve cache usage, and vectorize the arithmetic work across elements and faces. These ingredients are integrated into a framework scalable to the massive parallelism of supercomputers by the use of optimal-complexity linear solvers, such as mixed-precision, hybrid geometric-polynomial-algebraic multigrid solvers for the pressure Poisson problem. The application problem under consideration are fluid dynamical simulations of the human respiratory system under mechanical ventilation conditions, using unstructured/structured adaptively refined meshes for geometrically complex domains typical of biomedical engineering.

We present the design and implementation details of a geometric multigrid method on adaptively refined meshes for massively parallel computations. The method uses local smoothing on the refined part of the mesh. Partitioning is achieved by using a space filling curve for the leaf mesh and distributing ancestors in the hierarchy based on the leaves. We present a model of the efficiency of mesh hierarchy distribution and compare its predictions to runtime measurements. The algorithm is implemented as part of the deal.II finite-element library and as such available to the public.

The traditional workflow in continuum mechanics simulations is that a geometry description —for example obtained using Constructive Solid Geometry (CSG) or Computer Aided Design (CAD) tools—forms the input for a mesh generator. The mesh is then used as the sole input for the finite element, finite volume, and finite difference solver, which at this point no longer has access to the original, “underlying” geometry. However, many modern techniques—for example, adaptive mesh refinement and the use of higher order geometry approximation methods—really do need information about the underlying geometry to realize their full potential. We have undertaken an exhaustive study of where typical finite element codes use geometry information, with the goal of determining what information geometry tools would have to provide. Our study shows that nearly all geometry-related needs inside the simulators can be satisfied by just two “primitives”: elementary queries posed by the simulation software to the geometry description. We then show that it is possible to provide these primitives in all of the frequently used ways in which geometries are described in common industrial workflows, and illustrate our solutions using a number of examples.

This work presents the efficient, matrix-free finite-element library hyper.deal for solving partial differential equations in two to six dimensions with high-order discontinuous Galerkin methods. It builds upon the low-dimensional finite-element library deal.II to create complex low-dimensional meshes and to operate on them individually. These meshes are combined via a tensor product on the fly and the library provides new special-purpose highly optimized matrixfree functions exploiting domain decomposition as well as shared memory via MPI-3.0 features. Both node-level performance analyses and strong/weak-scaling studies on up to 147,456 CPU cores confirm the efficiency of the implementation. Results of the library hyper.deal are reported for high-dimensional advection problems and for the solution of the Vlasov-Poisson equation in up to 6D phase space. Copyright © 2020, arXiv, All rights reserved.

We present an algorithmic framework for matrix-free evaluation of discontinuous Galerkin finite element operators. It relies on fast quadrature with sum factorization on quadrilateral and hexahedral meshes, targeting general weak forms of linear and nonlinear partial differential equations. Different algorithms and data structures are compared in an in-depth performance analysis. The implementations of the local integrals are optimized by vectorization over several cells and faces and an even-odd decomposition of the one-dimensional interpolations. Up to 60% of the arithmetic peak on Intel Haswell, Broadwell, and Knights Landing processors is reached when running from caches and up to 40% of peak when also considering the access to vectors from main memory. On 2×14 Broadwell cores, the throughput is up to 2.2 billion unknowns per second for the 3D Laplacian and up to 4 billion unknowns per second for the 3D advection on affine geometries, close to a simple copy operation at 4.7 billion unknowns per second. Our experiments show that MPI ghost exchange has a considerable impact on performance and we present strategies to mitigate this effect. Finally, various options for evaluating geometry terms and their performance are discussed. Our implementations are publicly available through the deal.II finite element library.

Many operations that need to be performed in modern finite element codes can be described as an operation that needs to be done independently on every cell, followed by a reduction of these local results into a global data structure. For example, matrix assembly, estimating discretization errors, or converting nodal values into data structures that can be output in visualization file formats all fall into this class of operations. Using this realization, we identify a software design pattern that we call WorkStream and that can be used to model such operations and enables the use of multicore shared memory parallel processing. We also describe in detail how this design pattern can be efficiently implemented, and we provide numerical scalability results from its use in the deal .II software library.

We present an efficient implementation of parallel finite element operator application for hexahedral elements. The implementation is tailored to data structures for adaptively refined meshes and exploits parallelism on modern computer systems. The evaluation of local shape functions and gradients is performed with sum-factorization that makes use of the tensor-product form. For shared memory parallelization, we propose a novel two-level partitioning/coloring approach that avoids race conditions when writing into the result vector. We give evidence for the good performance of our implementation. We employ the optimized operator implementation on a problem in quantum dynamics described by the time-dependent Schroedinger equation. We obtain a speedup of more than a factor four over conventional solvers based on sparse matrices for a moderate polynomial order of four in three dimensions.

Today's largest supercomputers have 100,000s of processor cores and offer the potential to solve partial differential equations discretized by billions of unknowns. However, the complexity of scaling to such large machines and problem sizes has so far prevented the emergence of generic software libraries that support such computations, although these would lower the threshold of entry and enable many more applications to benefit from large-scale computing. We are concerned with providing this functionality for mesh-adaptive finite element computations. We assume the existence of an “oracle” that implements the generation and modification of an adaptive mesh distributed across many processors, and that responds to queries about its structure. Based on querying the oracle, we develop scalable algorithms and data structures for generic finite element methods. Specifically, we consider the parallel distribution of mesh data, global enumeration of degrees of freedom, constraints, and postprocessing. Our algorithms remove the bottlenecks that typically limit large-scale adaptive finite element analyses. We demonstrate scalability of complete finite element workflows on up to 16,384 processors. An implementation of the proposed algorithms, based on the open source software p4est as mesh oracle, is provided under an open source license through the widely used deal.II finite element software library.