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Many loads acting on a vehicle depend on the condition and quality of roads
traveled as well as on the driving style of the motorist. Thus, during vehicle development,
good knowledge on these further operations conditions is advantageous.
For that purpose, usage models for different kinds of vehicles are considered. Based
on these mathematical descriptions, representative routes for multiple user
types can be simulated in a predefined geographical region. The obtained individual
driving schedules consist of coordinates of starting and target points and can
thus be routed on the true road network. Additionally, different factors, like the
topography, can be evaluated along the track.
Available statistics resulting from travel survey are integrated to guarantee reasonable
trip length. Population figures are used to estimate the number of vehicles in
contained administrative units. The creation of thousands of those geo-referenced
trips then allows the determination of realistic measures of the durability loads.
Private as well as commercial use of vehicles is modeled. For the former, commuters
are modeled as the main user group conducting daily drives to work and
additional leisure time a shopping trip during workweek. For the latter, taxis as
example for users of passenger cars are considered. The model of light-duty commercial
vehicles is split into two types of driving patterns, stars and tours, and in
the common traffic classes of long-distance, local and city traffic.
Algorithms to simulate reasonable target points based on geographical and statistical
data are presented in detail. Examples for the evaluation of routes based
on topographical factors and speed profiles comparing the influence of the driving
style are included.

In computer graphics, realistic rendering of virtual scenes is a computationally complex problem. State-of-the-art rendering technology must become more scalable to
meet the performance requirements for demanding real-time applications.
This dissertation is concerned with core algorithms for rendering, focusing on the
ray tracing method in particular, to support and saturate recent massively parallel computer systems, i.e., to distribute the complex computations very efficiently
among a large number of processing elements. More specifically, the three targeted
main contributions are:
1. Collaboration framework for large-scale distributed memory computers
The purpose of the collaboration framework is to enable scalable rendering
in real-time on a distributed memory computer. As an infrastructure layer it
manages the explicit communication within a network of distributed memory
nodes transparently for the rendering application. The research is focused on
designing a communication protocol resilient against delays and negligible in
overhead, relying exclusively on one-sided and asynchronous data transfers.
The hypothesis is that a loosely coupled system like this is able to scale linearly
with the number of nodes, which is tested by directly measuring all possible
communication-induced delays as well as the overall rendering throughput.
2. Ray tracing algorithms designed for vector processing
Vector processors are to be efficiently utilized for improved ray tracing performance. This requires the basic, scalar traversal algorithm to be reformulated
in order to expose a high degree of fine-grained data parallelism. Two approaches are investigated: traversing multiple rays simultaneously, and performing
multiple traversal steps at once. Efficiently establishing coherence in a group
of rays as well as avoiding sorting of the nodes in a multi-traversal step are the
defining research goals.
3. Multi-threaded schedule and memory management for the ray tracing acceleration structure
Construction times of high-quality acceleration structures are to be reduced by
improvements to multi-threaded scalability and utilization of vector processors. Research is directed at eliminating the following scalability bottlenecks:
dynamic memory growth caused by the primitive splits required for high-
quality structures, and top-level hierarchy construction where simple task par-
allelism is not readily available. Additional research addresses how to expose
scatter/gather-free data-parallelism for efficient vector processing.
Together, these contributions form a scalable, high-performance basis for real-time,
ray tracing-based rendering, and a prototype path tracing application implemented
on top of this basis serves as a demonstration.
The key insight driving this dissertation is that the computational power necessary
for realistic light transport for real-time rendering applications demands massively
parallel computers, which in turn require highly scalable algorithms. Therefore this
dissertation provides important research along the path towards virtual reality.

While the design step should be free from computational related constraints and operations due to its artistic aspect, the modeling phase has to prepare the model for the later stages of the pipeline.
This dissertation is concerned with the design and implementation of a framework for local remeshing and optimization. Based on the experience gathered, a full study about mesh quality criteria is also part of this work.
The contributions can be highlighted as: (1) a local meshing technique based on a completely novel approach constrained to the preservation of the mesh of non interesting areas. With this concept, designers can work on the design details of specific regions of the model without introducing more polygons elsewhere; (2) a tool capable of recovering the shape of a refined area to its decimated version, enabling details on optimized meshes of detailed models; (3) the integration of novel techniques into a single framework for meshing and smoothing which is constrained to surface structure; (4) the development of a mesh quality criteria priority structure, being able to classify and prioritize according to the application of the mesh.
Although efficient meshing techniques have been proposed along the years, most of them lack the possibility to mesh smaller regions of the base mesh, preserving the mesh quality and density of outer areas.
Considering this limitation, this dissertation seeks answers to the following research questions:
1. Given that mesh quality is relative to the application it is intended for, is it possible to design a general mesh evaluation plan?
2. How to prioritize specific mesh criteria over others?
3. Given an optimized mesh and its original design, how to improve the representation of single regions of the first, without degrading the mesh quality elsewhere?
Four main achievements came from the respective answers:
1. The Application Driven Mesh Quality Criteria Structure: Due to high variation in mesh standards because of various computer aided operations performed for different applications, e.g. animation or stress simulation, a structure for better visualization of mesh quality criteria is proposed. The criteria can be used to guide the mesh optimization, making the task consistent and reliable. This dissertation also proposes a methodology to optimize the criteria values, which is adaptable to the needs of a specific application.
2. Curvature Driven Meshing Algorithm: A novel approach, a local meshing technique, which works on a desired area of the mesh while preserving its boundaries as well as the rest of the topology. It causes a slow growth in the overall amount of polygons by making only small regions denser. The method can also be used to recover the details of a reference mesh to its decimated version while refining it. Moreover, it employs a geometric fast and easy to implement approach representing surface features as simple circles, being used to guide the meshing. It also generates quad-dominant meshes, with triangle count directly dependent on the size of the boundary.
3. Curvature-based Method for Anisotropic Mesh Smoothing: A geometric-based method is extended to 3D space to be able to produce anisotropic elements where needed. It is made possible by mapping the original space to another which embeds the surface curvature. This methodology is used to enhance the smoothing algorithm by making the nearly regularized elements follow the surface features, preserving the original design. The mesh optimization method also preserves mesh topology, while resizing elements according to the local mesh resolution, effectively enhancing the design aspects intended.
4. Framework for Local Restructure of Meshed Surfaces: The combination of both methods creates a complete tool for recovering surface details through mesh refinement and curvature aware mesh smoothing.

In this thesis we consider the directional analysis of stationary point processes. We focus on three non-parametric methods based on second order analysis which we have defined as Integral method, Ellipsoid method, and Projection method. We present the methods in a general setting and then focus on their application in the 2D and 3D case of a particular type of anisotropy mechanism called geometric anisotropy. We mainly consider regular point patterns motivated by our application to real 3D data coming from glaciology. Note that directional analysis of 3D data is not so prominent in the literature.
We compare the performance of the methods, which depends on the relative parameters, in a simulation study both in 2D and 3D. Based on the results we give recommendations on how to choose the methods´ parameters in practice.
We apply the directional analysis to the 3D data coming from glaciology, which consist in the locations of air-bubbles in polar ice cores. The aim of this study is to provide information about the deformation rate in the ice and the corresponding thinning of ice layers at different depths. This information is substantial for the glaciologists in order to build ice dating models and consequently to give a correct interpretation of the climate information which can be found by analyzing ice cores. In this thesis we consider data coming from three different ice cores: the Talos Dome core, the EDML core and the Renland core.
Motivated by the ice application, we study how isotropic and stationary noise influences the directional analysis. In fact, due to the relaxation of the ice after drilling, noise bubbles can form within the ice samples. In this context we take two classification algorithms into consideration, which aim to classify points in a superposition of a regular isotropic and stationary point process with Poisson noise.
We introduce two methods to visualize anisotropy, which are particularly useful in 3D and apply them to the ice data. Finally, we consider the problem of testing anisotropy and the limiting behavior of the geometric anisotropy transform.

In this thesis, we consider the problem of processing similarity queries over a dataset of top-k rankings and class constrained objects. Top-k rankings are the most natural and widely used technique to compress a large amount of information into a concise form. Spearman’s Footrule distance is used to compute the similarity between rankings, considering how well rankings agree on the positions (ranks) of ranked items. This setup allows the application of metric distance-based pruning strategies, and, alternatively, enables the use of traditional inverted indices for retrieving rankings that overlap in items. Although both techniques can be individually applied, we hypothesize that blending these two would lead to better performance. First, we formulate theoretical bounds over the rankings, based on Spearman's Footrule distance, which are essential for adapting existing, inverted index based techniques to the setting of top-k rankings. Further, we propose a hybrid indexing strategy, designed for efficiently processing similarity range queries, which incorporates inverted indices and metric space indices, such as M- or BK-trees, resulting in a structure that resembles both indexing methods with tunable emphasis on one or the other. Moreover, optimizations to the inverted index component are presented, for early termination and minimizing bookkeeping. As vast amounts of data are being generated on a daily bases, we further present a distributed, highly tunable, approach, implemented in Apache Spark, for efficiently processing similarity join queries over top-k rankings. To combine distance-based filtering with inverted indices, the algorithm works in several phases. The partial results are joined for the computation of the final result set. As the last contribution of the thesis, we consider processing k-nearest-neighbor (k-NN) queries over class-constrained objects, with the additional requirement that the result objects are of a specific type. We introduce the MISP index, which first indexes the objects by their (combination of) class belonging, followed by a similarity search sub index for each subset of objects. The number of such subsets can combinatorially explode, thus, we provide a cost model that analyzes the performance of the MISP index structure under different configurations, with the aim of finding the most efficient one for the dataset being searched.

Visualization is vital to the scientific discovery process.
An interactive high-fidelity rendering provides accelerated insight into complex structures, models and relationships.
However, the efficient mapping of visualization tasks to high performance architectures is often difficult, being subject to a challenging mixture of hardware and software architectural complexities in combination with domain-specific hurdles.
These difficulties are often exacerbated on heterogeneous architectures.
In this thesis, a variety of ray casting-based techniques are developed and investigated with respect to a more efficient usage of heterogeneous HPC systems for distributed visualization, addressing challenges in mesh-free rendering, in-situ compression, task-based workload formulation, and remote visualization at large scale.
A novel direct raytracing scheme for on-the-fly free surface reconstruction of particle-based simulations using an extended anisoptropic kernel model is investigated on different state-of-the-art cluster setups.
The versatile system renders up to 170 million particles on 32 distributed compute nodes at close to interactive frame rates at 4K resolution with ambient occlusion.
To address the widening gap between high computational throughput and prohibitively slow I/O subsystems, in situ topological contour tree analysis is combined with a compact image-based data representation to provide an effective and easy-to-control trade-off between storage overhead and visualization fidelity.
Experiments show significant reductions in storage requirements, while preserving flexibility for exploration and analysis.
Driven by an increasingly heterogeneous system landscape, a flexible distributed direct volume rendering and hybrid compositing framework is presented.
Based on a task-based dynamic runtime environment, it enables adaptable performance-oriented deployment on various platform configurations.
Comprehensive benchmarks with respect to task granularity and scaling are conducted to verify the characteristics and potential of the novel task-based system design.
A core challenge of HPC visualization is the physical separation of visualization resources and end-users.
Using more tiles than previously thought reasonable, a distributed, low-latency multi-tile streaming system is demonstrated, being able to sustain a stable 80 Hz when streaming up to 256 synchronized 3840x2160 tiles and achieve 365 Hz at 3840x2160 for sort-first compositing over the internet, thereby enabling lightweight visualization clients and leaving all the heavy lifting to the remote supercomputer.

Topology-Based Characterization and Visual Analysis of Feature Evolution in Large-Scale Simulations
(2019)

This manuscript presents a topology-based analysis and visualization framework that enables the effective exploration of feature evolution in large-scale simulations. Such simulations pose additional challenges to the already complex task of feature tracking and visualization, since the vast number of features and the size of the simulation data make it infeasible to naively identify, track, analyze, render, store, and interact with data. The presented methodology addresses these issues via three core contributions. First, the manuscript defines a novel topological abstraction, called the Nested Tracking Graph (NTG), that records the temporal evolution of features that exhibit a nesting hierarchy, such as superlevel set components for multiple levels, or filtered features across multiple thresholds. In contrast to common tracking graphs that are only capable of describing feature evolution at one hierarchy level, NTGs effectively summarize their evolution across all hierarchy levels in one compact visualization. The second core contribution is a view-approximation oriented image database generation approach (VOIDGA) that stores, at simulation runtime, a reduced set of feature images. Instead of storing the features themselves---which is often infeasable due to bandwidth constraints---the images of these databases can be used to approximate the depicted features from any view angle within an acceptable visual error, which requires far less disk space and only introduces a neglectable overhead. The final core contribution combines these approaches into a methodology that stores in situ the least amount of information necessary to support flexible post hoc analysis utilizing NTGs and view approximation techniques.

Various physical phenomenons with sudden transients that results into structrual changes can be modeled via
switched nonlinear differential algebraic equations (DAEs) of the type
\[
E_{\sigma}\dot{x}=A_{\sigma}x+f_{\sigma}+g_{\sigma}(x). \tag{DAE}
\]
where \(E_p,A_p \in \mathbb{R}^{n\times n}, x\mapsto g_p(x),\) is a mapping, \(p \in \{1,\cdots,P\}, P\in \mathbb{N}
f \in \mathbb{R} \rightarrow \mathbb{R}^n , \sigma: \mathbb{R} \rightarrow \{1,\cdots, P\}\).
Two related common tasks are:
Task 1: Investigate if above (DAE) has a solution and if it is unique.
Task 2: Find a connection among a solution of above (DAE) and solutions of related
partial differential equations.
In the linear case \(g(x) \equiv 0\) the task 1 has been tackeled already in a
distributional solution framework.
A main goal of the dissertation is to give contribution to task 1 for the
nonlinear case \(g(x) \not \equiv 0\) ; also contributions to the task 2 are given for
switched nonlinear DAEs arising while modeling sudden transients in water
distribution networks. In addition, this thesis contains the following further
contributions:
The notion of structured switched nonlinear DAEs has been introduced,
allowing also non regular distributions as solutions. This extend a previous
framework that allowed only piecewise smooth functions as solutions. Further six mild conditions were given to ensure existence and uniqueness of the solution within the space of piecewise smooth distribution. The main
condition, namely the regularity of the matrix pair \((E,A)\), is interpreted geometrically for those switched nonlinear DAEs arising from water network graphs.
Another contribution is the introduction of these switched nonlinear DAEs
as a simplication of the PDE model used classically for modeling water networks. Finally, with the support of numerical simulations of the PDE model it has been illustrated that this switched nonlinear DAE model is a good approximation for the PDE model in case of a small compressibility coefficient.

Shared memory concurrency is the pervasive programming model for multicore architectures
such as x86, Power, and ARM. Depending on the memory organization, each architecture follows
a somewhat different shared memory model. All these models, however, have one common
feature: they allow certain outcomes for concurrent programs that cannot be explained
by interleaving execution. In addition to the complexity due to architectures, compilers like
GCC and LLVM perform various program transformations, which also affect the outcomes of
concurrent programs.
To be able to program these systems correctly and effectively, it is important to define a
formal language-level concurrency model. For efficiency, it is important that the model is
weak enough to allow various compiler optimizations on shared memory accesses as well
as efficient mappings to the architectures. For programmability, the model should be strong
enough to disallow bogus “out-of-thin-air” executions and provide strong guarantees for well-synchronized
programs. Because of these conflicting requirements, defining such a formal
model is very difficult. This is why, despite years of research, major programming languages
such as C/C++ and Java do not yet have completely adequate formal models defining their
concurrency semantics.
In this thesis, we address this challenge and develop a formal concurrency model that is very
good both in terms of compilation efficiency and of programmability. Unlike most previous
approaches, which were defined either operationally or axiomatically on single executions,
our formal model is based on event structures, which represents multiple program executions,
and thus gives us more structure to define the semantics of concurrency.
In more detail, our formalization has two variants: the weaker version, WEAKEST, and the
stronger version, WEAKESTMO. The WEAKEST model simulates the promising semantics proposed
by Kang et al., while WEAKESTMO is incomparable to the promising semantics. Moreover,
WEAKESTMO discards certain questionable behaviors allowed by the promising semantics.
We show that the proposed WEAKESTMO model resolve out-of-thin-air problem, provide
standard data-race-freedom (DRF) guarantees, allow the desirable optimizations, and can be
mapped to the architectures like x86, PowerPC, and ARMv7. Additionally, our models are
flexible enough to leverage existing results from the literature to establish data-race-freedom
(DRF) guarantees and correctness of compilation.
In addition, in order to ensure the correctness of compilation by a major compiler, we developed
a translation validator targeting LLVM’s “opt” transformations of concurrent C/C++
programs. Using the validator, we identified a few subtle compilation bugs, which were reported
and were fixed. Additionally, we observe that LLVM concurrency semantics differs
from that of C11; there are transformations which are justified in C11 but not in LLVM and
vice versa. Considering the subtle aspects of LLVM concurrency, we formalized a fragment
of LLVM’s concurrency semantics and integrated it into our WEAKESTMO model.

The systems in industrial automation management (IAM) are information systems. The management parts of such systems are software components that support the manufacturing processes. The operational parts control highly plug-compatible devices, such as controllers, sensors and motors. Process variability and topology variability are the two main characteristics of software families in this domain. Furthermore, three roles of stakeholders -- requirement engineers, hardware-oriented engineers, and software developers -- participate in different derivation stages and have different variability concerns. In current practice, the development and reuse of such systems is costly and time-consuming, due to the complexity of topology and process variability. To overcome these challenges, the goal of this thesis is to develop an approach to improve the software product derivation process for systems in industrial automation management, where different variability types are concerned in different derivation stages. Current state-of-the-art approaches commonly use general-purpose variability modeling languages to represent variability, which is not sufficient for IAM systems. The process and topology variability requires more user-centered modeling and representation. The insufficiency of variability modeling leads to low efficiency during the staged derivation process involving different stakeholders. Up to now, product line approaches for systematic variability modeling and realization have not been well established for such complex domains. The model-based derivation approach presented in this thesis integrates feature modeling with domain-specific models for expressing processes and topology. The multi-variability modeling framework includes the meta-models of the three variability types and their associations. The realization and implementation of the multi-variability involves the mapping and the tracing of variants to their corresponding software product line assets. Based on the foundation of multi-variability modeling and realization, a derivation infrastructure is developed, which enables a semi-automated software derivation approach. It supports the configuration of different variability types to be integrated into the staged derivation process of the involved stakeholders. The derivation approach is evaluated in an industry-grade case study of a complex software system. The feasibility is demonstrated by applying the approach in the case study. By using the approach, both the size of the reusable core assets and the automation level of derivation are significantly improved. Furthermore, semi-structured interviews with engineers in practice have evaluated the usefulness and ease-of-use of the proposed approach. The results show a positive attitude towards applying the approach in practice, and high potential to generalize it to other related domains.