Accelerating the FlowSimulator: Tracing and Profiling of Python Toolchains for Industry-Grade Simulations

Abstract

High-performance computing is increasingly important in the aeronautical industry, enabling part of the certification process to be done via simulation. Current aviation regulations allow the use of simulation techniques to replace costly and potentially hazardous tests for aircraft and helicopters. But, for simulations to be accepted, their accuracy must match the one of physical testing. High-fidelity simulation models, required for the needed accuracy, involve very fine meshes, making high-performance computing essential. However, as hardware complexity grows, time-to-solution may not meet expectations. Complex toolchains can experience unexpected bottlenecks even in simple simulation blocks, making tracing and profiling tools crucial for identifying issues. In this work, we investigate a complex simulation toolchain to compute the static aeroelastic equilibrium of a wing. On the outer level, a Python control layer manages individual solvers for computational fluid dynamics, computational structural mechanics, mesh deformation, and interpolation methods. These solver components, in turn, are implemented using a Python layer wrapping the underlying C++ libraries. We use Score-P to extract simulation run traces and analyze them with Vampir. Results show the actual relation between the runtimes of individual simulation blocks and to the whole runtime. Last, but not least, the contribution discusses the usability of the performance tools in the context of mixed C++/ Python workflows.