In continuation of a successful series of events, the 4th Many-core Applications Research Community (MARC) symposium took place at the HPI in Potsdam on December 8th and 9th 2011. Over 60 researchers from different fields presented their work on many-core hardware architectures, their programming models, and the resulting research questions for the upcoming generation of heterogeneous parallel systems.

Die immer dichtere und schnellere Vernetzung von Rechnern und Rechenzentren über Hochgeschwindigkeitsnetzwerke ermöglicht eine neue Art des wissenschaftlich verteilten Rechnens, bei der geographisch weit auseinanderliegende Rechenkapazitäten zu einer Gesamtheit zusammengefasst werden können. Dieser so entstehende virtuelle Superrechner, der selbst aus mehreren Grossrechnern besteht, kann dazu genutzt werden Probleme zu berechnen, für die die einzelnen Grossrechner zu klein sind. Die Probleme, die numerisch mit heutigen Rechenkapazitäten nicht lösbar sind, erstrecken sich durch sämtliche Gebiete der heutigen Wissenschaft, angefangen von Astrophysik, Molekülphysik, Bioinformatik, Meteorologie, bis hin zur Zahlentheorie und Fluiddynamik um nur einige Gebiete zu nennen.<br /> <br /> Je nach Art der Problemstellung und des Lösungsverfahrens gestalten sich solche "Meta-Berechnungen" mehr oder weniger schwierig. Allgemein kann man sagen, dass solche Berechnungen um so schwerer und auch um so uneffizienter werden, je mehr Kommunikation zwischen den einzelnen Prozessen (oder Prozessoren) herrscht. Dies ist dadurch begründet, dass die Bandbreiten bzw. Latenzzeiten zwischen zwei Prozessoren auf demselben Grossrechner oder Cluster um zwei bis vier Grössenordnungen höher bzw. niedriger liegen als zwischen Prozessoren, welche hunderte von Kilometern entfernt liegen.<br /> <br /> Dennoch bricht nunmehr eine Zeit an, in der es möglich ist Berechnungen auf solch virtuellen Supercomputern auch mit kommunikationsintensiven Programmen durchzuführen. Eine grosse Klasse von kommunikations- und berechnungsintensiven Programmen ist diejenige, die die Lösung von Differentialgleichungen mithilfe von finiten Differenzen zum Inhalt hat. Gerade diese Klasse von Programmen und deren Betrieb in einem virtuellen Superrechner wird in dieser vorliegenden Dissertation behandelt. Methoden zur effizienteren Durchführung von solch verteilten Berechnungen werden entwickelt, analysiert und implementiert. Der Schwerpunkt liegt darin vorhandene, klassische Parallelisierungsalgorithmen zu analysieren und so zu erweitern, dass sie vorhandene Informationen (z.B. verfügbar durch das Globus Toolkit) über Maschinen und Netzwerke zur effizienteren Parallelisierung nutzen. Soweit wir wissen werden solche Zusatzinformationen kaum in relevanten Programmen genutzt, da der Grossteil aller Parallelisierungsalgorithmen implizit für die Ausführung auf Grossrechnern oder Clustern entwickelt wurde. / In order to face the rapidly increasing need for computational resources of various scientific and engineering applications one has to think of new ways to make more efficient use of the worlds current computational resources. In this respect, the growing speed of wide area networks made a new kind of distributed computing possible: Metacomputing or (distributed) Grid computing. This is a rather new and uncharted field in computational science. The rapidly increasing speed of networks even outperforms the average increase of processor speed: Processor speeds double on average each 18 month whereas network bandwidths double every 9 months. Due to this development of local and wide area networks Grid computing will certainly play a key role in the future of parallel computing.<br /> <br /> This type of distributed computing, however, distinguishes from the traditional parallel computing in many ways since it has to deal with many problems not occurring in classical parallel computing. Those problems are for example heterogeneity, authentication and slow networks to mention only a few. Some of those problems, e.g. the allocation of distributed resources along with the providing of information about these resources to the application have been already attacked by the Globus software.<br /> <br /> Unfortunately, as far as we know, hardly any application or middle-ware software takes advantage of this information, since most parallelizing algorithms for finite differencing codes are implicitly designed for single supercomputer or cluster execution. We show that although it is possible to apply classical parallelizing algorithms in a Grid environment, in most cases the observed efficiency of the executed code is very poor.<br /> <br /> In this work we are closing this gap. In our thesis, we will<br /> - show that an execution of classical parallel codes in Grid environments is possible but very slow<br /> - analyze this situation of bad performance, nail down bottlenecks in communication, remove unnecessary overhead and other reasons for low performance<br /> - develop new and advanced algorithms for parallelisation that are aware of a Grid environment in order to generelize the traditional parallelization schemes<br /> - implement and test these new methods, replace and compare with the classical ones - introduce dynamic strategies that automatically adapt the running code to the nature of the underlying Grid environment.<br /> <br /> The higher the performance one can achieve for a single application by manual tuning for a Grid environment, the lower the chance that those changes are widely applicable to other programs. In our analysis as well as in our implementation we tried to keep the balance between high performance and generality. None of our changes directly affect code on the application level which makes our algorithms applicable to a whole class of real world applications.<br /> <br /> The implementation of our work is done within the Cactus framework using the Globus toolkit, since we think that these are the most reliable and advanced programming frameworks for supporting computations in Grid environments. On the other hand, however, we tried to be as general as possible, i.e. all methods and algorithms discussed in this thesis are independent of Cactus or Globus.

High Performance Computing (HPC) has become an indispensable tool for the scientific community to perform simulations on models whose complexity would exceed the limits of a standard computer. An unfortunate trend concerning HPC systems is that their power consumption under high-demanding workloads increases. To counter this trend, hardware vendors have implemented power saving mechanisms in recent years, which has increased the variability in power demands of single nodes. These capabilities provide an opportunity to increase the energy efficiency of HPC applications. To utilize these hardware power saving mechanisms efficiently, their overhead must be analyzed. Furthermore, applications have to be examined for performance and energy efficiency issues, which can give hints for optimizations. This requires an infrastructure that is able to capture both, performance and power consumption information concurrently. The mechanisms that such an infrastructure would inherently support could further be used to implement a tool that is able to do both, measuring and tuning of energy efficiency. This thesis targets all steps in this process by making the following contributions: First, I provide a broad overview on different related fields. I list common performance measurement tools, power measurement infrastructures, hardware power saving capabilities, and tuning tools. Second, I lay out a model that can be used to define and describe energy efficiency tuning on program region scale. This model includes hardware and software dependent parameters. Hardware parameters include the runtime overhead and delay for switching power saving mechanisms as well as a contemplation of their scopes and the possible influence on application performance. Thus, in a third step, I present methods to evaluate common power saving mechanisms and list findings for different x86 processors. Software parameters include their performance and power consumption characteristics as well as the influence of power-saving mechanisms on these. To capture software parameters, an infrastructure for measuring performance and power consumption is necessary. With minor additions, the same infrastructure can later be used to tune software and hardware parameters. Thus, I lay out the structure for such an infrastructure and describe common components that are required for measuring and tuning. Based on that, I implement adequate interfaces that extend the functionality of contemporary performance measurement tools. Furthermore, I use these interfaces to conflate performance and power measurements and further process the gathered information for tuning. I conclude this work by demonstrating that the infrastructure can be used to manipulate power-saving mechanisms of contemporary x86 processors and increase the energy efficiency of HPC applications.

High Performance Computing (HPC) has become an indispensable tool for the scientific community to perform simulations on models whose complexity would exceed the limits of a standard computer. An unfortunate trend concerning HPC systems is that their power consumption under high-demanding workloads increases. To counter this trend, hardware vendors have implemented power saving mechanisms in recent years, which has increased the variability in power demands of single nodes. These capabilities provide an opportunity to increase the energy efficiency of HPC applications. To utilize these hardware power saving mechanisms efficiently, their overhead must be analyzed. Furthermore, applications have to be examined for performance and energy efficiency issues, which can give hints for optimizations. This requires an infrastructure that is able to capture both, performance and power consumption information concurrently. The mechanisms that such an infrastructure would inherently support could further be used to implement a tool that is able to do both, measuring and tuning of energy efficiency. This thesis targets all steps in this process by making the following contributions: First, I provide a broad overview on different related fields. I list common performance measurement tools, power measurement infrastructures, hardware power saving capabilities, and tuning tools. Second, I lay out a model that can be used to define and describe energy efficiency tuning on program region scale. This model includes hardware and software dependent parameters. Hardware parameters include the runtime overhead and delay for switching power saving mechanisms as well as a contemplation of their scopes and the possible influence on application performance. Thus, in a third step, I present methods to evaluate common power saving mechanisms and list findings for different x86 processors. Software parameters include their performance and power consumption characteristics as well as the influence of power-saving mechanisms on these. To capture software parameters, an infrastructure for measuring performance and power consumption is necessary. With minor additions, the same infrastructure can later be used to tune software and hardware parameters. Thus, I lay out the structure for such an infrastructure and describe common components that are required for measuring and tuning. Based on that, I implement adequate interfaces that extend the functionality of contemporary performance measurement tools. Furthermore, I use these interfaces to conflate performance and power measurements and further process the gathered information for tuning. I conclude this work by demonstrating that the infrastructure can be used to manipulate power-saving mechanisms of contemporary x86 processors and increase the energy efficiency of HPC applications.

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology.

Die Recurrence Quantification Analysis (RQA) ist eine Methode aus der nicht-linearen Zeitreihenanalyse. Im Mittelpunkt dieser Methode steht die Auswertung des Inhalts sogenannter Rekurrenzmatrizen. Bestehende Berechnungsansätze zur Durchführung der RQA können entweder nur Zeitreihen bis zu einer bestimmten Länge verarbeiten oder benötigen viel Zeit zur Analyse von sehr langen Zeitreihen. Diese Dissertation stellt die sogenannte skalierbare Rekurrenzanalyse (SRA) vor. Sie ist ein neuartiger Berechnungsansatz, der eine gegebene Rekurrenzmatrix in mehrere Submatrizen unterteilt. Jede Submatrix wird von einem Berechnungsgerät in massiv-paralleler Art und Weise untersucht. Dieser Ansatz wird unter Verwendung der OpenCL-Schnittstelle umgesetzt. Anhand mehrerer Experimente wird demonstriert, dass SRA massive Leistungssteigerungen im Vergleich zu existierenden Berechnungsansätzen insbesondere durch den Einsatz von Grafikkarten ermöglicht. Die Dissertation enthält eine ausführliche Evaluation, die den Einfluss der Anwendung mehrerer Datenbankkonzepte, wie z.B. die Repräsentation der Eingangsdaten, auf die RQA-Verarbeitungskette analysiert. Es wird untersucht, inwiefern unterschiedliche Ausprägungen dieser Konzepte Einfluss auf die Effizienz der Analyse auf verschiedenen Berechnungsgeräten haben. Abschließend wird ein automatischer Optimierungsansatz vorgestellt, der performante RQA-Implementierungen für ein gegebenes Analyseszenario in Kombination mit einer Hardware-Plattform dynamisch bestimmt. Neben anderen Aspekten werden drastische Effizienzgewinne durch den Einsatz des Optimierungsansatzes aufgezeigt. / Recurrence quantification analysis (RQA) is a method from nonlinear time series analysis. It relies on the identification of line structures within so-called recurrence matrices and comprises a set of scalar measures. Existing computing approaches to RQA are either not capable of processing recurrence matrices exceeding a certain size or suffer from long runtimes considering time series that contain hundreds of thousands of data points. This thesis introduces scalable recurrence analysis (SRA), which is an alternative computing approach that subdivides a recurrence matrix into multiple sub matrices. Each sub matrix is processed individually in a massively parallel manner by a single compute device. This is implemented exemplarily using the OpenCL framework. It is shown that this approach delivers considerable performance improvements in comparison to state-of-the-art RQA software by exploiting the computing capabilities of many-core hardware architectures, in particular graphics cards. The usage of OpenCL allows to execute identical SRA implementations on a variety of hardware platforms having different architectural properties. An extensive evaluation analyses the impact of applying concepts from database technology, such memory storage layouts, to the RQA processing pipeline. It is investigated how different realisations of these concepts affect the performance of the computations on different types of compute devices. Finally, an approach based on automatic performance tuning is introduced that automatically selects well-performing RQA implementations for a given analytical scenario on specific computing hardware. Among others, it is demonstrated that the customised auto-tuning approach allows to considerably increase the efficiency of the processing by adapting the implementation selection.

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology.

Mit der wachsenden Popularität von GitHub, dem größten Online-Anbieter von Programm-Quellcode und der größten Kollaborationsplattform der Welt, hat es sich zu einer Big-Data-Ressource entfaltet, die eine Vielfalt von Open-Source-Repositorien (OSR) anbietet. Gegenwärtig gibt es auf GitHub mehr als eine Million Organisationen, darunter solche wie Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly und viele mehr. GitHub verfügt über eine umfassende REST API, die es Forschern ermöglicht, wertvolle Informationen über die Entwicklungszyklen von Software und Forschung abzurufen. Unsere Arbeit verfolgt zwei Hauptziele: (I) ein automatisches OSR-Kategorisierungssystem für Data Science Teams und Softwareentwickler zu ermöglichen, das Entdeckbarkeit, Technologietransfer und Koexistenz fördert. (II) Visuelle Daten-Exploration und thematisch strukturierte Navigation innerhalb von GitHub-Organisationen für reproduzierbare Kooperationsforschung und Web-Applikationen zu etablieren. Um Mehrwert aus Big Data zu generieren, ist die Speicherung und Verarbeitung der Datensemantik und Metadaten essenziell. Ferner ist die Wahl eines geeigneten Text Mining (TM) Modells von Bedeutung. Die dynamische Kalibrierung der Metadaten-Konfigurationen, TM Modelle (VSM, GVSM, LSA), Clustering-Methoden und Clustering-Qualitätsindizes wird als "Smart Clusterization" abgekürzt. Data-Driven Documents (D3) und Three.js (3D) sind JavaScript-Bibliotheken, um dynamische, interaktive Datenvisualisierung zu erzeugen. Beide Techniken erlauben Visuelles Data Mining (VDM) in Webbrowsern, und werden als D3-3D abgekürzt. Latent Semantic Analysis (LSA) misst semantische Information durch Kontingenzanalyse des Textkorpus. Ihre Eigenschaften und Anwendbarkeit für Big-Data-Analytik werden demonstriert. "Smart clusterization", kombiniert mit den dynamischen VDM-Möglichkeiten von D3-3D, wird unter dem Begriff "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA" zusammengefasst. / With the growing popularity of GitHub, the largest host of source code and collaboration platform in the world, it has evolved to a Big Data resource offering a variety of Open Source repositories (OSR). At present, there are more than one million organizations on GitHub, among them Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly and many more. GitHub provides an extensive REST API, which enables scientists to retrieve valuable information about the software and research development life cycles. Our research pursues two main objectives: (I) provide an automatic OSR categorization system for data science teams and software developers promoting discoverability, technology transfer and coexistence; (II) establish visual data exploration and topic driven navigation of GitHub organizations for collaborative reproducible research and web deployment. To transform Big Data into value, in other words into Smart Data, storing and processing of the data semantics and metadata is essential. Further, the choice of an adequate text mining (TM) model is important. The dynamic calibration of metadata configurations, TM models (VSM, GVSM, LSA), clustering methods and clustering quality indices will be shortened as "smart clusterization". Data-Driven Documents (D3) and Three.js (3D) are JavaScript libraries for producing dynamic, interactive data visualizations, featuring hardware acceleration for rendering complex 2D or 3D computer animations of large data sets. Both techniques enable visual data mining (VDM) in web browsers, and will be abbreviated as D3-3D. Latent Semantic Analysis (LSA) measures semantic information through co-occurrence analysis in the text corpus. Its properties and applicability for Big Data analytics will be demonstrated. "Smart clusterization" combined with the dynamic VDM capabilities of D3-3D will be summarized under the term "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA".