An Efficient and Flexible Implementation of Aspect-Oriented Languages

Compilers for modern object-oriented programming languages generate code in a platform independent intermediate language preserving the concepts of the source language; for example, classes, fields, methods, and virtual or static dispatch can be directly identified within the intermediate code. To execute this intermediate code, state-of-the-art implementations of virtual machines perform just-in-time (JIT) compilation of the intermediate language; i.e., the virtual instructions in the intermediate code are compiled to native machine code at runtime. In this step, a declarative representation of source language concepts in the intermediate language facilitates highly efficient adaptive and speculative optimization of the running program which may not be possible otherwise. In contrast, constructs of aspect-oriented languages - which improve the separation of concerns - are commonly realized by compiling them to conventional intermediate language instructions or by driving transformations of the intermediate code, which is called weaving. This way the aspect-oriented constructs' semantics is not preserved in a declarative manner at the intermediate language level. This representational gap between aspect-oriented concepts in the source code and in the intermediate code hinders high performance optimizations and weakens features of software engineering processes like debugging support or the continuity property of incremental compilation: modifying an aspect in the source code potentially requires re-weaving multiple other modules. To leverage language implementation techniques for aspect-oriented languages, this thesis proposes the Aspect-Language Implementation Architecture (ALIA) which prescribes - amongst others - the existence of an intermediate representation preserving the aspect-oriented constructs of the source program. A central component of this architecture is an extensible and flexible meta-model of aspect-oriented concepts which acts as an interface between front-ends (usually a compiler) and back-ends (usually a virtual machine) of aspect-oriented language implementations. The architecture and the meta-model are embodied for Java-based aspect-oriented languages in the Framework for Implementing Aspect Languages (FIAL) respectively the Language-Independent Aspect Meta-Model (LIAM) which is part of the framework. FIAL generically implements the work flows required from an execution environment when executing aspects provided in terms of LIAM. In addition to the first-class intermediate representation of aspect-oriented concepts, ALIA - and the FIAL framework as its incarnation - treat the points of interaction between aspects and other modules - so-called join points - as being late-bound to an implementation. In analogy to the object-oriented terminology for late-bound methods, the join points are called virtual in ALIA. Together, the first-class representation of aspect-oriented concepts in the intermediate representation as well as treating join points as being virtual facilitate the implementation of new and effective optimizations for aspect-oriented programs. Three different instantiations of the FIAL framework are presented in this thesis, showcasing the feasibility of integrating language back-ends with different characteristics with the framework. One integration supports static aspect deployment and produces results similar to conventional aspect weavers; the woven code is executable on any standard Java virtual machine. Two instantiations are fully dynamic, where one is realized as a portable plug-in for standard Java virtual machines and the other one, called Steamloom^ALIA , is realized as a deep integration into a specific virtual machine, the Jikes Research Virtual Machine Alpern2005. While the latter instantiation is not portable, it exhibits an outstanding performance. Virtual join point dispatch is a generalization of virtual method dispatch. Thus, well established and elaborate optimization techniques from the field of virtual method dispatch are re-used with slight adaptations in Steamloom^ALIA . These optimizations for aspect-oriented concepts go beyond the generation of optimal bytecode. Especially strikingly, the power of such optimizations is shown in this thesis by the examples of the cflow dynamic property, which may be necessary to evaluate during virtual join point dispatch, and dynamic aspect deployment - i.e., the selective modification of specific join points' dispatch. In order to evaluate the optimization techniques developed in this thesis, a means for benchmarking has been developed in terms of macro-benchmarks; i.e., real-world applications are executed. These benchmarks show that for both concepts the implementation presented here is at least circa twice as fast as state-of-the-art implementations performing static optimizations of the generated bytecode; in many cases this thesis's optimizations even reach a speed-up of two orders of magnitude for the cflow implementation and even four orders of magnitude for the dynamic deployment. The intermediate representation in terms of LIAM models is general enough to express the constructs of multiple aspect-oriented languages. Therefore, optimizations of features common to different languages are available to applications written in all of them. To proof that the abstractions provided by LIAM are sufficient to act as intermediate language for multiple aspect-oriented source languages, an automated translation from source code to LIAM models has been realized for three very different and popular aspect-oriented languages: AspectJ, JAsCo and Compose*. In addition, the feasibility of translating from CaesarJ to LIAM models is shown by discussion. The use of an extensible meta-model as intermediate representation furthermore simplifies the definition of new aspect-oriented language concepts as is shown in terms of a tutorial-style example of designing a domain specific extension to the Java language in this thesis

Grain growth behavior and efficient large scale simulations of recrystallization with the phase-field method

This book summarizes the found insights of grain growth behavior, of multidimensional decomposition for regular grids to efficiently parallelize computing and how to simulate recrystallization by coupling the finite element method with the phase-field method for microstructure texture analysis. The frame of the book is created by the phase-field method, which is the tool used in this work, to investigate microstructure phenomena

Scalable and Declarative Information Extraction in a Parallel Data Analytics System

Informationsextraktions (IE) auf sehr großen Datenmengen erfordert hochkomplexe, skalierbare und anpassungsfähige Systeme. Obwohl zahlreiche IE-Algorithmen existieren, ist die nahtlose und erweiterbare Kombination dieser Werkzeuge in einem skalierbaren System immer noch eine große Herausforderung. In dieser Arbeit wird ein anfragebasiertes IE-System für eine parallelen Datenanalyseplattform vorgestellt, das für konkrete Anwendungsdomänen konfigurierbar ist und für Textsammlungen im Terabyte-Bereich skaliert. Zunächst werden konfigurierbare Operatoren für grundlegende IE- und Web-Analytics-Aufgaben definiert, mit denen komplexe IE-Aufgaben in Form von deklarativen Anfragen ausgedrückt werden können. Alle Operatoren werden hinsichtlich ihrer Eigenschaften charakterisiert um das Potenzial und die Bedeutung der Optimierung nicht-relationaler, benutzerdefinierter Operatoren (UDFs) für Data Flows hervorzuheben. Anschließend wird der Stand der Technik in der Optimierung nicht-relationaler Data Flows untersucht und herausgearbeitet, dass eine umfassende Optimierung von UDFs immer noch eine Herausforderung ist. Darauf aufbauend wird ein erweiterbarer, logischer Optimierer (SOFA) vorgestellt, der die Semantik von UDFs mit in die Optimierung mit einbezieht. SOFA analysiert eine kompakte Menge von Operator-Eigenschaften und kombiniert eine automatisierte Analyse mit manuellen UDF-Annotationen, um die umfassende Optimierung von Data Flows zu ermöglichen. SOFA ist in der Lage, beliebige Data Flows aus unterschiedlichen Anwendungsbereichen logisch zu optimieren, was zu erheblichen Laufzeitverbesserungen im Vergleich mit anderen Techniken führt. Als Viertes wird die Anwendbarkeit des vorgestellten Systems auf Korpora im Terabyte-Bereich untersucht und systematisch die Skalierbarkeit und Robustheit der eingesetzten Methoden und Werkzeuge beurteilt um schließlich die kritischsten Herausforderungen beim Aufbau eines IE-Systems für sehr große Datenmenge zu charakterisieren.Information extraction (IE) on very large data sets requires highly complex, scalable, and adaptive systems. Although numerous IE algorithms exist, their seamless and extensible combination in a scalable system still is a major challenge. This work presents a query-based IE system for a parallel data analysis platform, which is configurable for specific application domains and scales for terabyte-sized text collections. First, configurable operators are defined for basic IE and Web Analytics tasks, which can be used to express complex IE tasks in the form of declarative queries. All operators are characterized in terms of their properties to highlight the potential and importance of optimizing non-relational, user-defined operators (UDFs) for dataflows. Subsequently, we survey the state of the art in optimizing non-relational dataflows and highlight that a comprehensive optimization of UDFs is still a challenge. Based on this observation, an extensible, logical optimizer (SOFA) is introduced, which incorporates the semantics of UDFs into the optimization process. SOFA analyzes a compact set of operator properties and combines automated analysis with manual UDF annotations to enable a comprehensive optimization of data flows. SOFA is able to logically optimize arbitrary data flows from different application areas, resulting in significant runtime improvements compared to other techniques. Finally, the applicability of the presented system to terabyte-sized corpora is investigated. Hereby, we systematically evaluate scalability and robustness of the employed methods and tools in order to pinpoint the most critical challenges in building an IE system for very large data sets

Application of Saliency Maps for Optimizing Camera Positioning in Deep Learning Applications

In the fields of process control engineering and robotics, especially in automatic control, optimization challenges frequently manifest as complex problems with expensive evaluations. This thesis zeroes in on one such problem: the optimization of camera positions for Convolutional Neural Networks (CNNs). CNNs have specific attention points in images that are often not intuitive to human perception, making camera placement critical for performance. The research is guided by two primary questions. The first investigates the role of Explainable Artificial Intelligence (XAI), specifically GradCAM++ visual explanations, in Computer Vision for aiding in the evaluation of different camera positions. Building on this, the second question assesses a novel algorithm that leverages these XAI features against traditional black-box optimization methods. To answer these questions, the study employs a robotic auto-positioning system for data collection, CNN model training, and performance evaluation. A case study focused on classifying flow regimes in industrial-grade bioreactors validates the method. The proposed approach shows improvements over established techniques like Grid Search, Random Search, Bayesian optimization, and Simulated Annealing. Future work will focus on gathering more data and including noise for generalized conclusions.:Contents 1 Introduction 1.1 Motivation 1.2 Problem Analysis 1.3 Research Question 1.4 Structure of the Thesis 2 State of the Art 2.1 Literature Research Methodology 2.1.1 Search Strategy 2.1.2 Inclusion and Exclusion Criteria 2.2 Blackbox Optimization 2.3 Mathematical Notation 2.4 Bayesian Optimization 2.5 Simulated Annealing 2.6 Random Search 2.7 Gridsearch 2.8 Explainable A.I. and Saliency Maps 2.9 Flowregime Classification in Stirred Vessels 2.10 Performance Metrics 2.10.1 R2 Score and Polynomial Regression for Experiment Data Analysis 2.10.2 Blackbox Optimization Performance Metrics 2.10.3 CNN Performance Metrics 3 Methodology 3.1 Requirement Analysis and Research Hypothesis 3.2 Research Approach: Case Study 3.3 Data Collection 3.4 Evaluation and Justification 4 Concept 4.1 System Overview 4.2 Data Flow 4.3 Experimental Setup 4.4 Optimization Challenges and Approaches 5 Data Collection and Experimental Setup 5.1 Hardware Components 5.2 Data Recording and Design of Experiments 5.3 Data Collection 5.4 Post-Experiment 6 Implementation 6.1 Simulation Unit 6.2 Recommendation Scalar from Saliency Maps 6.3 Saliency Map Features as Guidance Mechanism 6.4 GradCam++ Enhanced Bayesian Optimization 6.5 Benchmarking Unit 6.6 Benchmarking 7 Results and Evaluation 7.1 Experiment Data Analysis 7.2 Recommendation Scalar 7.3 Benchmarking Results and Quantitative Analysis 7.3.1 Accuracy Results from the Benchmarking Process 7.3.2 Cumulative Results Interpretation 7.3.3 Analysis of Variability 7.4 Answering the Research Questions 7.5 Summary 8 Discussion 8.1 Critical Examination of Limitations 8.2 Discussion of Solutions to Limitations 8.3 Practice-Oriented Discussion of Findings 9 Summary and OutlookIm Bereich der Prozessleittechnik und Robotik, speziell bei der automatischen Steuerung, treten oft komplexe Optimierungsprobleme auf. Diese Arbeit konzentriert sich auf die Optimierung der Kameraplatzierung in Anwendungen, die Convolutional Neural Networks (CNNs) verwenden. Da CNNs spezifische, für den Menschen nicht immer ersichtliche, Merkmale in Bildern hervorheben, ist die intuitive Platzierung der Kamera oft nicht optimal. Zwei Forschungsfragen leiten diese Arbeit: Die erste Frage untersucht die Rolle von Erklärbarer Künstlicher Intelligenz (XAI) in der Computer Vision zur Bereitstellung von Merkmalen für die Bewertung von Kamerapositionen. Die zweite Frage vergleicht einen darauf basierenden Algorithmus mit anderen Blackbox-Optimierungstechniken. Ein robotisches Auto-Positionierungssystem wird zur Datenerfassung und für Experimente eingesetzt. Als Lösungsansatz wird eine Methode vorgestellt, die XAI-Merkmale, insbesondere solche aus GradCAM++ Erkenntnissen, mit einem Bayesschen Optimierungsalgorithmus kombiniert. Diese Methode wird in einer Fallstudie zur Klassifizierung von Strömungsregimen in industriellen Bioreaktoren angewendet und zeigt eine gesteigerte performance im Vergleich zu etablierten Methoden. Zukünftige Forschung wird sich auf die Sammlung weiterer Daten, die Inklusion von verrauschten Daten und die Konsultation von Experten für eine kostengünstigere Implementierung konzentrieren.:Contents 1 Introduction 1.1 Motivation 1.2 Problem Analysis 1.3 Research Question 1.4 Structure of the Thesis 2 State of the Art 2.1 Literature Research Methodology 2.1.1 Search Strategy 2.1.2 Inclusion and Exclusion Criteria 2.2 Blackbox Optimization 2.3 Mathematical Notation 2.4 Bayesian Optimization 2.5 Simulated Annealing 2.6 Random Search 2.7 Gridsearch 2.8 Explainable A.I. and Saliency Maps 2.9 Flowregime Classification in Stirred Vessels 2.10 Performance Metrics 2.10.1 R2 Score and Polynomial Regression for Experiment Data Analysis 2.10.2 Blackbox Optimization Performance Metrics 2.10.3 CNN Performance Metrics 3 Methodology 3.1 Requirement Analysis and Research Hypothesis 3.2 Research Approach: Case Study 3.3 Data Collection 3.4 Evaluation and Justification 4 Concept 4.1 System Overview 4.2 Data Flow 4.3 Experimental Setup 4.4 Optimization Challenges and Approaches 5 Data Collection and Experimental Setup 5.1 Hardware Components 5.2 Data Recording and Design of Experiments 5.3 Data Collection 5.4 Post-Experiment 6 Implementation 6.1 Simulation Unit 6.2 Recommendation Scalar from Saliency Maps 6.3 Saliency Map Features as Guidance Mechanism 6.4 GradCam++ Enhanced Bayesian Optimization 6.5 Benchmarking Unit 6.6 Benchmarking 7 Results and Evaluation 7.1 Experiment Data Analysis 7.2 Recommendation Scalar 7.3 Benchmarking Results and Quantitative Analysis 7.3.1 Accuracy Results from the Benchmarking Process 7.3.2 Cumulative Results Interpretation 7.3.3 Analysis of Variability 7.4 Answering the Research Questions 7.5 Summary 8 Discussion 8.1 Critical Examination of Limitations 8.2 Discussion of Solutions to Limitations 8.3 Practice-Oriented Discussion of Findings 9 Summary and Outloo

Grain growth behavior and efficient large scale simulations of recrystallization with the phase-field method

This book summarizes the found insights of grain growth behavior, of multidimensional decomposition for regular grids to efficiently parallelize computing and how to simulate recrystallization by coupling the finite element method with the phase-field method for microstructure texture analysis. The frame of the book is created by the phase-field method, which is the tool used in this work, to investigate microstructure phenomena

Top-k aggregation queries in large-scale distributed systems

Distributed top-k query processing has recently become an essential functionality in a large number of emerging application classes like Internet traffic monitoring and Peer-to-Peer Web search. This work addresses efficient algorithms for distributed top-k queries in wide-area networks where the index lists for the attribute values (or text terms) of a query are distributed across a number of data peers. More precisely, in this thesis, we make the following distributions: We present the family of KLEE algorithms that are a fundamental building-block towards efficient top-k query processing in distributed systems. We present means to model score distributions and show how these score models can be used to reason about parameter values that play an important role in the overall performance of KLEE. We present GRASS, a family of novel algorithms based on three optimization techniques significantly increased overall performance of KLEE and related algorithms. We present probabilistic guarantees for the result quality. Moreover, we present Minerva1, a distributed search engine. Minerva offers a highly distributed (in both the data dimension and the computational dimension), scalable, and efficient solution toward the development of internet-scale search engines.Top-k Anfragen spielen eine große Rolle in einer Vielzahl von Anwendungen, insbesondere im Bereich von Informationssystemen, bei denen eine kleine, sorgfältig ausgewählte Teilmenge der Ergebnisse den Benutzern präsentiert werden soll. Beispiele hierfür sind Suchmaschinen wie Google, Yahoo oder MSN. Obwohl die Forschung in diesem Bereich in den letzten Jahren große Fortschritte gemacht hat, haben Top-k-Anfragen in verteilten Systemen, bei denen die Daten auf verschiedenen Rechnern verteilt sind, vergleichsweise wenig Aufmerksamkeit erlangt. In dieser Arbeit beschäftigen wir uns mit der effizienten Verarbeitung eben dieser Anfragen. Die Hauptbeiträge gliedern sich wie folgt. Wir präsentieren KLEE, eine Familie neuartiger Top-k-Algorithmen. Wir entwickeln Modelle mit denen Datenverteilungen beschrieben werden können. Diese Modelle sind die Grundlage für eine Schätzung diverser Parameter, die einen großen Einfluss auf die Performanz von KLEE und anderen ähnlichen Algorithmen haben. Wir präsentieren GRASS, eine Familie von Algorithmen, basierend auf drei neuartigen Optimierungstechniken, mit denen die Performanz von KLEE und ähnlichen Algorithmen verbessert wird. Wir präsentieren probabilistische Garantien für die Ergebnisgüte. Wir präsentieren Minerva, eine neuartige verteilte Peer-to-Peer-Suchmaschine

Sparse Gradient Optimization and its Applications in Image Processing

Millions of digital images are captured by imaging devices on a daily basis. The way imaging devices operate follows an integral process from which the information of the original scene needs to be estimated. The estimation is done by inverting the integral process of the imaging device with the use of optimization techniques. This linear inverse problem, the inversion of the integral acquisition process, is at the heart of several image processing applications such as denoising, deblurring, inpainting, and super-resolution. We describe in detail the use of linear inverse problems in these applications. We review and compare several state-of-the-art optimization algorithms that invert this integral process. Linear inverse problems are usually very difficult to solve. Therefore, additional prior assumptions need to be introduced to successfully estimate the output signal. Several priors have been suggested in the research literature, with the Total Variation (TV) being one of the most prominent. In this thesis, we review another prior, the l0 pseudo-norm over the gradient domain. This prior allows full control over how many non-zero gradients are retained to approximate prominent structures of the image. We show the superiority of the l0 gradient prior over the TV prior in recovering genuinely piece-wise constant signals. The l0 gradient prior has shown to produce state-of-the-art results in edge-preserving image smoothing. Moreover, this general prior can be applied to several other applications, such as edge extraction, clip-art JPEG artifact removal, non-photorealistic image rendering, detail magnification, and tone mapping. We review and evaluate several state-of-the-art algorithms that solve the optimization problem based on the l0 gradient prior. Subsequently we apply the l0 gradient prior to two applications where we show superior results as compared to the current state-of-the-art. The first application is that of single-image reflection removal. Existing solutions to this problem have shown limited success because of the highly ill-posed nature of the problem. We show that the standard l0 gradient prior with a modified data-fidelity term based on the Laplacian operator is able to sufficiently remove unwanted reflections from images in many realistic scenarios. We conduct extensive experiments and show that our method outperforms the state-of-the-art. In the second application of haze removal from visible-NIR image pairs we propose a novel optimization framework, where the prior term penalizes the number of non-zero gradients of the difference between the output and the NIR image. Due to the longer wavelengths of NIR, an image taken in the NIR spectrum suffers significantly less from haze artifacts. Using this prior term, we are able to transfer details from the haze-free NIR image to the final result. We show that our formulation provides state-of-the-art results compared to haze removal methods that use a single image and also to those that are based on visible-NIR image pairs

Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009

Dieser Tagungsband enthält die Beiträge des 19. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 2.-4. Dezember 2009 im Haus Bommerholz bei Dortmund stattfindet

Efficient Generation and Execution of DAG-Structured Query Graphs

Traditional database management systems use tree-structured query evaluation plans. While easy to implement, a tree-structured query evaluation plan is not expressive enough for some optimizations like factoring common algebraic subexpressions or magic sets. These require directed acyclic graphs (DAGs), i.e. shared subplans. This work covers the different aspects of DAG-structured query graphs. First, it introduces a novel framework to reason about sharing of subplans and thus DAG-structured query evaluation plans. Second, it describes the first plan generator capable of generating optimal DAG-structured query evaluation plans. Third, an efficient framework for reasoning about orderings and groupings used by the plan generator is presented. And fourth, a runtime system capable of executing DAG-structured query evaluation plans with minimal overhead is discussed. The experimental results show that with no or only a modest increase of plan generation time, a major reduction of query execution time can be achieved for common queries. This shows that DAG-structured query evaluation plans are serviceable and should be preferred over tree-structured query plans