Computational fluid dynamics (CFD) simulations are an increasingly important part of aircraft design. They allow in-depth insight into the aerodynamic behavior of components and help reducing cost and time in development. CODA is a next-generation CFD solver for aerodynamic simulations of fully equipped aircraft. It is developed by the German Aerospace Center (DLR), the French Aerospace Lab (ONERA), and Airbus, and is one of the key applications represented in the European Centre of Excellence for Engineering Applications (Excellerat). This work evaluates the CODA CFD solver on the CARA HPC system based on the AMD Naples architecture. The evaluation includes an assessment of the scalability on the largest available partition of the production system with the NASA common research model in a strong scaling scenario, a comparison of different hybrid-parallel setups suitable for the specific memory and NUMA layout and a comparison of the results with the Intel Cascade Lake architecture. Furthermore, it demonstrates the impact of node placement and unfavorable network loads on large scale runs. 

/pdf/scalability-evaluation-of-the-cfd-solver-coda-on-the-amd-25l0148s.pdf

Scalability Evaluation of the CFD Solver CODA on the AMD Naples Architecture

Over the last decade, an effort between MTU Aero Engines, DLR Cologne, and the Chair for Scientiﬁc Computing at the University of Kaiserslautern-Landau (RPTU) has been made to create an adjoint solver for DLR’s internal ﬂow solver TRACE. This paper summarizes the strategies and techniques adopted to achieve a memory and CPU-efﬁcient adjoint code. The derivatives required for setting up the adjoint are computed with operator overloading algorithmic differentiation (AD). For the handling of MPI and task-based parallelism, special libraries needed to be developed. In addition, a wrapper for the linear system solver Spliss is introduced. During the effort, the performance of the ad-joint solution was closely monitored and improvements were introduced. The robustness of the method was further enhanced for off-design operating points, e.g. near the stall line. This paper illustrates the above techniques by means of computational results for turbomachinery conﬁgurations along with run time and memory measurements.

On Recent Developments for Efficient Turbo-Machinery Design using Algorithmic Differentiation

Abstract A significant part in computational fluid dynamics (CFD) simulations is the solving of large sparse systems of linear equations resulting from implicit time integration of the Reynolds-averaged Navier-Stokes (RANS) equations. The sparse linear system solver Spliss aims to provide a linear solver library that, on the one hand, is tailored to these requirements of CFD applications but, on the other hand, independent of the particular CFD solver. Spliss allows leveraging a range of available HPC technologies such as hybrid CPU parallelization and the possibility to offload the computationally intensive linear solver to GPU accelerators, while at the same time hiding this complexity from the CFD solver. This work highlights the steps taken to establish multi-GPU capabilities for the Spliss solver allowing for efficient and scalable usage of large GPU systems. In addition, this work evaluates performance and scalability on CPU and GPU systems using a representative CODA test case as an example. CODA is the CFD software being developed as part of a collaboration between the French Aerospace Lab ONERA, the German Aerospace Center (DLR), Airbus, and their European research partners. CODA is jointly owned by ONERA, DLR and Airbus. The evaluation examines and compares performance and scalability in a strong scaling approach on Nvidia A100 GPUs and the AMD Rome architecture. 

A Look at Performance and Scalability of the GPU Accelerated Sparse Linear System Solver Spliss

Abstract Developments in numerical simulation of flows and high-performance computing influence one another. More detailed simulation methods create a permanent need for more computational power, while new hardware developments often require changes to the software to exploit new hardware features. This dependency is very pronounced in the case of vector-units which are featured by all modern processors to increase their numerical throughput but require vectorization of the software to be used efficiently. We study the vectorization of a simulation method that exhibits an inherent level of vector-parallelism. This is of particular interest as SIMD operations will hopefully be available with std::simd in a future C++ standard. The simulation method considered here results in the simultaneous solution of multiple sparse linear systems of equations which only differ by their main diagonal and right-hand sides. Such structure arises in the simulation of unsteady flow in turbomachinery by means of a frequency domain approach called harmonic balance. 

/pdf/simd-vectorization-for-simultaneous-solution-of-locally-2z08y9wl.pdf

SIMD vectorization for simultaneous solution of locally varying linear systems with multiple right-hand sides

Various numerical applications in the context of multidisciplinary high-fidelity aircraft design, e.g. fluid-structure interaction or shape optimization, require the consideration of changes in the geometry of the aircraft shape. Such geometry changes can be realized using mesh deformations, and we demonstrate such a volume mesh deformation method based on elasticity analogy in conjunction with line-implicit solvers. Line-implicit solvers have already been successfully used in CFD simulations with strongly anisotropic cells in a viscous boundary layer, and are now applied to mesh deformation problems of such meshes. In this approach, the solution of a block-tridiagonal subsystem of the Jacobian matrix is computed exactly using the Thomas algorithm. Here, the selection of the tridiagonal part – representing the lines – is of crucial importance for the speed of convergence. A new algorithm for line identification is presented and compared with existing ones. Also, a comparison and combination of these methods with multigrid methods is performed and the implications for industrially relevant test cases are demonstrated. 

Deformation of CFD Meshes with Anisotropic Cells in a Viscous Boundary Layer Using Line-Implicit Methods

We introduce Spliss, a novel block sparse linear solver library, which is designed to meet current and upcoming challenges in the CFD development. Spliss supports a wide range of linear operators typically used in CFD applications. This includes sparse block matrices of variable block sizes and different scalar types as well as matrix-free operators. The provided solving methods comprise standard methods such as Jacobi or Gauss-Seidel smoothers, Krylov subspace methods, and domain-specific methods such as direct block-tridiagonal solvers. Spliss uses contemporary and emerging HPC technologies such as one-sided communication, hybrid and heterogeneous parallelization. The C++ template interface allows for extensions of the library and specialization of its internals for optimization. We explain the integration of Spliss in the two CFD solvers CODA and TRACE. Finally, we show how CODA benefits from using Spliss by offloading the linear solver to GPUs.

Spliss: A Sparse Linear System Solver for Transparent Integration of Emerging HPC Technologies into CFD Solvers and Applications

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Spliss: A Sparse Linear System Solver for Transparent Integration of Emerging HPC Technologies into CFD Solvers and Applications