Makro/Mikro-Simulation des Phasenzerfalls im Transkritischen Bereich

Benchmarking the performance of one’s application in high performance computing (HPC) systems is critically important for reducing runtime and energy costs. Yet, accessing the plethora of relevant metrics that impact performance is often challenging, particularly for users without hardware experience. In this paper, we introduce the novel benchmarking tool xbat developed by MEGWARE GmbH. xbat requires no setup from the user side, and it allows the user to run, monitor and evaluate their application from the tool’s web interface, consolidating the entire benchmarking process in an approachable, intuitive workflow. We demonstrate the capabilities of the tool using benchmark applications of varying complexity and show that it can manage all aspects of the benchmarking workflow in a seamless manner. In particular, we focus on the open-source molecular dynamics research software ls1 mardyn, and the closed-source optimisation package Gurobi. Both packages present unique challenges. Mixed-integer programming solvers, such as those integrated in the Gurobi software, exhibit significant performance variability, so that seemingly innocuous parameter changes and machine characteristics can affect the runtime drastically, and ls1 mardyn comes with an auto-tuning library AutoPas, that enables the selection of various node-level algorithms to compute molecular trajectories. Focusing on these two packages, we showcase the practicality, versatility and utility of xbat, and share its current and future developments.

In coupled molecular-continuum flow simulations, molecular dynamics (MD) simulations exhibit thermal fluctuations. Finding a way to minimize the impact of these fluctuations on the CFD solver, e.g. in terms of stability, and to control the corresponding statistical error plays a key role in order to obtain reliable results. In this paper, statistical error analysis is employed for MD simulations to determine the statistical error in flow velocities and the number of MD data samples to bound this error. The corresponding error estimator is augmented by a dynamic ensemble handling approach, which allows to couple a variable number of MD simulation instances to a single CFD solver. The ensemble members can be simulated independently from each other over separate coupling time intervals, enabling a high level of (MPI-based) parallelism. Adding or removing MD simulations to/from the ensemble allows to regulate the error and keep it under a prescribed threshold. All functionality is implemented in the massively parallel macro-micro-coupling tool (MaMiCo). We validate our approach by coupled molecular-continuum Couette flow simulation for liquid argon and provide scalability tests on up to 131.072 cores. The computational overhead for handling the dynamic MD ensemble is found to be rather negligible.

Molecular-continuum methods, as considered in this work, decompose the computational domain into continuum and molecular dynamics (MD) sub-domains. Compared to plain MD simulations, they greatly reduce computational effort. However, the quality of a fully two-way coupled simulation result strongly depends on a variety of system-specific parameters, and the corresponding sensitivity is only rarely addressed in the literature. Using a state-flux molecular-continuum coupling algorithm, we investigated the influences of various parameters, such as the size of the overlapping region, the coupling time step and the quality of ensemble-based sampling of flow velocities, in a Couette flow scenario. In particular, we considered a big setup in terms of domain size and number of time steps, which allowed us to investigate the long-term behavior of the coupling algorithm close to the incompressible regime. While mostly good agreement was reached on short time scales, it was the long-term behavior which differed even with slightly differently parametrized simulations. We demonstrated our findings by measuring the error in velocity, and we summarize our main observations with a few lessons learned.