Load-balancing for multi-physics simulations

In many real-world scenarios, multiple different kinds of physics appear together in the same system. In order to predict the behavior of such a system, they need to be combined into one multi-physics simulation. Simulations using partitioned coupling approaches have proved to be especially efficient concerning resource usage and development costs. They divide the simulation domain into distinct subdomains based on the occurring physics, and then solve them separately using single-physics solvers. This makes them suited for execution on modern supercomputers since they are able to profit off the massively available parallelism. Albeit only when the available cores are distributed in accordance with the load of the single-physics solvers. Otherwise, we face resource wastage and unnecessary increases in run-time. The most commonly used approach to this problem is to estimate the load of the single-physics solvers by comparing their degrees of freedom and then scaling the number of cores accordingly. This thesis proposes a new approach based on empirical performance analysis. By employing machine learning techniques, predictive models for the run-time of the single-physics solvers are created. Based on these models ideal core assignments are derived through solving of an integer optimization problem. To generate the models two approaches are considered: The first one creates several different regression models and then picks the best fitting one, whereas the second one uses neural networks to approximate the solver run-time. Both of them allow us to incorporate new parameters into the models in addition to the number of cores and degrees of freedom. This enables generalization to previously unseen parameter combinations, for example, new discretization levels. For a simple test case, the regression approach successfully predicts the solver run-time with high accuracy, leading to performance improvements of over 40% compared to the old load-balancing approach. When considering multiple parameters, the neural network approach generally outperforms the regression approach

Load management strategy for Particle-In-Cell simulations in high energy particle acceleration

In the wake of the intense effort made for the experimental CILEX project, numerical simulation cam- paigns have been carried out in order to finalize the design of the facility and to identify optimal laser and plasma parameters. These simulations bring, of course, important insight into the fundamental physics at play. As a by-product, they also characterize the quality of our theoretical and numerical models. In this paper, we compare the results given by different codes and point out algorithmic lim- itations both in terms of physical accuracy and computational performances. These limitations are illu- strated in the context of electron laser wakefield acceleration (LWFA). The main limitation we identify in state-of-the-art Particle-In-Cell (PIC) codes is computational load imbalance. We propose an innovative algorithm to deal with this specific issue as well as milestones towards a modern, accurate high-per- formance PIC code for high energy particle acceleration

On parallel scalability aspects of strongly coupled partitioned fluid-structure-acoustics interaction

Multi-physics simulations, such as fluid-structure-acoustics interaction (FSA), require a high performance computing environment in order to perform the simulation in a reasonable amount of computation time. Currently used coupling methods use a staggered execution of the fluid and solid solver [6], which leads to inherent load imbalances. In [12] a new coupling scheme based on a quasi-Newton method is proposed for fluidstructure interaction which coupled the fluid and solid solver in parallel. The quasi- Newton method requires approximately the same number of coupling iterations per time step compared to a staggered coupling approach, resulting in a better load balance when running in a parallel environment. This contribution investigates the scalability limit and load-balancing for a strongly coupled fluid-structure interaction problem, and also for a fluid-structure-acoustics interaction problem. The acoustic far field of the fluid-structure-acoustics interaction problem is loosely coupled with the flow field