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Integrating GPU-Accelerated Tetrahedral Mesh Editing and Simulation

( 2023)
Ströter, Daniel
;
Halm, Andreas
;
Krispel, Ulrich
;
Mueller-Roemer, Johannes Sebastian
;
Fellner, Dieter

The use of computer-aided methods for the design of parts that must meet functional or stability requirements typically consists of an iterative cycle of design, physical simulation and testing or analysis, followed by redesign, etc. Each step is often performed with a domain-specific tool, e.g., a specific CAD modeling suite. This results in the need to convert the model representation between steps, such as meshing for finite element simulation for example. In recent work, a distributed application framework has been proposed that allows for the interactive modification and simulation of tetrahedral meshes derived from existing CAD models, e.g., to create customized versions of parts that were designed for mass production. This shortens the design cycle by eliminating the need for conversion and switching between tools. In this paper, we present a more detailed description and improvements to this architecture by using GPU parallelization not only for simulation but also for mesh editing, which leads to even shorter iteration cycles.
Fast harmonic tetrahedral mesh optimization

( 2022)
Ströter, D.
;
Mueller-Roemer, Johannes Sebastian
;
Weber, Daniel
;
Fellner, Dieter

Mesh optimization is essential to enable sufficient element quality for numerical methods such as the finite element method (FEM). Depending on the required accuracy and geometric detail, a mesh with many elements is necessary to resolve small-scale details. Sequential optimization of large meshes often imposes long run times. This is especially an issue for Delaunay-based methods. Recently, the notion of harmonic triangulations [1] was evaluated for tetrahedral meshes, revealing significantly faster run times than competing Delaunay-based methods. A crucial aspect for efficiency and high element quality is boundary treatment. We investigate directional derivatives for boundary treatment and massively parallel GPUs for mesh optimization. Parallel flipping achieves compelling speedups by up to 318 ×. We accelerate harmonic mesh optimization by 119 × for boundary preservation and 78 × for moving every boundary vertex, while producing superior mesh quality.
Industrial digitalization in the industry 4.0 era: Classification, reuse and authoring of digital models on Digital Twin platforms

( 2022)
Zambrano, Valentina
;
Mueller-Roemer, Johannes Sebastian
;
Sandberg, Michael
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Talasila, Prasad
;
Zanin, Davide
;
Larsen, Peter Gorm
;
Loeschner, Elke
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Thronicke, Wolfgang
;
Pietraroia, Dario
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Landolfi, Giuseppe
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Fontana, Alessandro
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Laspalas, Manuel
;
Antony, Jibinraj
;
Poser, Valerie
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Kiss, Tamas
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Bergweiler, Simon
;
Pena Serna, Sebastian
;
Izquierdo, Salvador
;
Viejo , Ismael
;
Juan , Asier
;
Serrano, Francisco
;
Stork, Andre

Digital Twins (DTs) are real-time digital models that allow for self-diagnosis, self-optimization and selfconfiguration without the need for human input or intervention. While DTs are a central aspect of the ongoing fourth industrial revolution (I4.0), this leap forward may be reserved for the established, large-cap companies since the adoption of digital technologies among Small and Medium-size Enterprises (SMEs) is still modest. The aim of the H2020 European Project "DIGITbrain" is to support a modular construction of DTs by reusing their fundamental building blocks, i.e., the Models that describe the behavior of the DT, their associated Algorithms and the Data required for the evaluation. By offering these building blocks as a service via a DT Platform (a Digital Twin Environment), the technical barriers among SMEs to adopt these technologies are lowered. This paper describes how digital models can be classified, reused and authored on such DT Platforms. Through experimental analyses of three industrial cases, the paper exemplifies how DTs are employed in relation to product assembly of agricultural robots, polymer injection molding, as well as laser-cutting and sheet-metal forming of aluminum.
Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics

( 2022)
Schinko, Christoph
;
Shao, Lin
;
Mueller-Roemer, Johannes Sebastian
;
Weber, Daniel
;
Zhang, Xingzi
;
Lee, Eugene
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Sander, Bastian
;
Steinhardt, Alexander
;
Settgast, Volker
;
Eggeling, Eva
;
Erdt, Marius
;
Chen, Kan

The Coronavirus Disease 2019 (COVID-19) has shown us the necessity to understand its transmission mechanisms in detail in order to establish practice in controlling such infectious diseases. An important instrument in doing so are mathematical models. However, they do not account for the spatiotemporal heterogeneity introduced by the movement and interaction of individuals with their surroundings. Computational fluid dynamics (CFD) simulations can be used to analyze transmission on micro- and mesostructure level, however become infeasible in larger scale scenarios. Agent-based modeling (ABM) on the other hand is missing means to simulate airborne virus transmission on a micro- and mesostructure level. Therefore, we present a system that combines CFD simulations with the dynamics given by trajectories from an ABM to form a basis for producing deeper insights. The proposed system is still work in progress; thus we focus on the system architecture and show preliminary results.
TEdit: A Distributed Tetrahedral Mesh Editor with Immediate Simulation Feedback

( 2021)
Ströter, Daniel
;
Krispel, Ulrich
;
Mueller-Roemer, Johannes Sebastian
;
Fellner, Dieter W.

The cycle of computer aided design and verification via physics simulation is often burdened by the use of separate tools for modeling and simulation, which requires conversion between formats, e.g. meshing for finite element simulation. This separation is often unavoidable because the tools contain specific domain knowledge which is mandatory for the task, for example a specific CAD modeling suite. We propose a distributed application that allows interactive modification of tetrahedral meshes, derived from existing CAD models. It provides immediate simulation feedback by offloading resource-intensive tasks onto multiple machines thereby enabling fast design cycles for individualized versions of mass-produced parts.
Generative Machine Learning for Resource-Aware 5G and IoT Systems

( 2021)
Piatkowski, Nico
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Mueller-Roemer, Johannes Sebastian
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Hasse, Peter
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Bachorek, Adam
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Werner, Tim
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Birnstill, Pascal
;
Morgenstern, Andreas
;
Stobbe, Lutz

Extrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system-allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible-e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.
Rapid Interactive Structural Analysis

( 2020)
Weber, Daniel
;
Grasser, Tim
;
Mueller-Roemer, Johannes Sebastian
;
Stork, André

Auf dem Weg zu einem hocheffizienten Produktentwicklungsprozess, der aus Design, Simulation, Analyse und Iterationen besteht, liegen noch einige ungenutzte Potenziale. Bei der Analyse des Prozesses wird häufig die Integration verschiedener Software-Werkzeuge entlang der Prozesskette als eine der Schwachstellen identifiziert. Hierbei sind Interoperabilität und die Standardisierung von Austauschformaten insbesondere für die Kombination von Software verschiedener Hersteller von zentraler Bedeutung. Jedoch hat die Dauer der Simulation ebenfalls einen maßgeblichen Einfluss auf die Effizienz, um schlussendlich Produkte schneller auf den Markt zu bringen oder eine höhere Qualität zu erzielen. Wenn die Ergebnisse von Simulationen praktisch direkt nach ihrem Start zur Verfügung stehen würden, so könnten einzelne Iterationsschleifen drastisch verkürzt und das mit eine Vielzahl von Design-Variationen exploriert werden. Auch die rechnergestützte Formoptimierung, bei der Hunderte von Simulationsrechnungen automatisiert durchgeführt werden, würde von solch kurzen Simulationszeiten stark profitieren. Im Projekt Rapid Interactive Structural Analysis wurde eine schnelle, interaktive Simulationslösung mit direkter Visualisierung auf Basis finiter Elemente entwickelt. Durch Nutzung der zur Verfügung stehenden, immensen Rechenpower von Graphikkarten (GPUs) können Simulationen, wie beispielsweise strukturmechanische Analysen, signifikant beschleunigt werden. Der Lösungsansatz basiert auf massiv-parallelen Algorithmen und beschleunigt dadurch linear-elastische Struktursimulationen um einen Faktor von bis zu 80. Mit dieser schnellen Simulationstechnologie werden neuartige Anwendungen möglich, wie beispielsweise die direkte Identifikation von Korrelationen zwischen geometrischen Änderungen und Spannungsverteilung oder die signifikante Beschleunigung von Form- oder Topologieoptimierungen. Die Genauigkeit und Geschwindigkeit des vorgestellten Ansatzes zu herkömmlichen Simulationen auf Basis der Finite-Elemente-Methode (FEM) wird verglichen.
Integrating Server-based Simulations into Web-based Geo-applications

( 2019)
Bormann, Pascal
;
Gutbell, Ralf
;
Mueller-Roemer, Johannes Sebastian

In this work, we present a novel approach for combining fluid simulations running on a GPU server with terrain rendered by a web-based 3D GIS system. We introduce a hybrid rendering approach, combining server-side and client-side rendering, to interactively display the results of a shallow water simulation on client devices using web technology. To display water and terrain in unison, we utilize image merging based on depth values. We extend it to deal with numerical and compression artifacts as well as Level-of-detail rendering and use Depth Image Based Rendering to counteract network latency.
Joint Schedule and Layout Autotuning for Sparse Matrices with Compound Entries on GPUs

( 2019)
Mueller-Roemer, Johannes Sebastian
;
Stork, André
;
Fellner, Dieter W.

Large sparse matrices with compound entries, i.e., complex and quaternionic matrices as well as matrices with dense blocks, are a core component of many algorithms in geometry processing, physically based animation, and other areas of computer graphics. We generalize several matrix layouts and apply joint schedule and layout autotuning to improve the performance of the sparse matrix-vector product on massively parallel graphics processing units. Compared to schedule tuning without layout tuning, we achieve speedups of up to 5:5x. In comparison to cuSPARSE, we achieve speedups of up to 4:7x.
GPU Data Structures and Code Generation for Modeling, Simulation, and Visualization

( 2019)
Mueller-Roemer, Johannes Sebastian
;
Fellner, Dieter W.
;
Stork, André
;
Müller, Heinrich

Virtual prototyping, the iterative process of using computer-aided (CAx) modeling, simulation, and visualization tools to optimize prototypes and products before manufacturing the first physical artifact, plays an increasingly important role in the modern product development process. Especially due to the availability of affordable additive manufacturing (AM) methods (3D printing), it is becoming increasingly possible to manufacture customized products or even for customers to print items for themselves. In such cases, the first physical prototype is frequently the final product. In this dissertation, methods to efficiently parallelize modeling, simulation, and visualization operations are examined with the goal of reducing iteration times in the virtual prototyping cycle, while simultaneously improving the availability of the necessary CAx tools. The presented methods focus on parallelization on programmable graphics processing units (GPUs). Modern GPUs are fully programmable massively parallel many core processors that are characterized by their high energy efficiency and good price performance ratio. Additionally, GPUs are already present in many workstations and home computers due to their use in computer-aided design (CAD) and computer games. However, specialized algorithms and data structures are required to make efficient use of the processing power of GPUs. Using the novel GPU-optimized data structures and algorithms as well as the new applications of compiler technology introduced in this dissertation, speedups between approximately one (10×) and more than two orders of magnitude (> 100×) are achieved compared to the state of the art in the three core areas of virtual prototyping. Additionally, memory use and required bandwidths are reduced by up to nearly 86%. As a result, not only can computations on existing models be executed more efficiently but larger models can be created and processed as well. In the area of modeling, efficient discrete mesh processing algorithms are examined with a focus on volumetric meshes. In the field of simulation, the assembly of the large sparse system matrices resulting from the finite element method (FEM) and the simulation of fluid dynamics are accelerated. As sparse matrices form the foundation of the presented approaches to mesh processing and simulation, GPU-optimized sparse matrix data structures and hardware- and domain-specific automatic tuning of these data structures are developed and examined as well. In the area of visualization, visualization latencies in remote visualization of cloud-based simulations are reduced by using an optimizing query compiler. By using hybrid visualization, various user interactions can be performed without network round trip latencies.

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