Compile-time support for thread-level speculation

Una de las principales preocupaciones de las ciencias de la computación es el estudio de las capacidades paralelas tanto de programas como de los procesadores que los ejecutan. Existen varias razones que hacen muy deseable el desarrollo de técnicas que paralelicen automáticamente el código. Entre ellas se encuentran el inmenso número de programas secuenciales existentes ya escritos, la complejidad de los lenguajes de programación paralelos, y los conocimientos que se requieren para paralelizar un código. Sin embargo, los actuales mecanismos de paralelización automática implementados en los compiladores comerciales no son capaces de paralelizar la mayoría de los bucles en un código [1], debido a la dependencias de datos que existen entre ellos [2]. Por lo tanto, se hace necesaria la búsqueda de nuevas técnicas, como la paralelización especulativa [3-5], que saquen beneficio de las potenciales capacidades paralelas del hardware y arquitecturas multiprocesador actuales. Sin embargo, ésta y otras técnicas requieren la intervención manual de programadores experimentados. Antes de ofrecer soluciones alternativas, se han evaluado las capacidades de paralelización de los compiladores comerciales, exponiendo las limitaciones de los mecanismos de paralelización automática que implementan. El estudio revela que estos mecanismos de paralelización automática sólo alcanzan un 19% de speedup en promedio para los benchmarks del SPEC CPU2006 [6], siendo este un resultado significativamente inferior al obtenido por técnicas de paralelización especulativa [7]. Sin embargo, la paralelización especulativa requiere una extensa modificación manual del código por parte de programadores. Esta Tesis aborda este problema definiendo una nueva cláusula OpenMP [8], llamada ¿speculative¿, que permite señalar qué variables pueden llevar a una violación de dependencia. Además, esta Tesis también propone un sistema en tiempo de compilación que, usando la información sobre los accesos a las variables que proporcionan las cláusulas OpenMP, añade automáticamente todo el código necesario para gestionar la ejecución especulativa de un programa. Esto libera al programador de modificar el código manualmente, evitando posibles errores y una tediosa tarea. El código generado por nuestro sistema enlaza con la librería de ejecución especulativamente paralela desarrollada por Estebanez, García-Yagüez, Llanos y Gonzalez-Escribano [9,10].Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos

FPGA-based Query Acceleration for Non-relational Databases

Database management systems are an integral part of today’s everyday life. Trends like smart applications, the internet of things, and business and social networks require applications to deal efficiently with data in various data models close to the underlying domain. Therefore, non-relational database systems provide a wide variety of database models, like graphs and documents. However, current non-relational database systems face performance challenges due to the end of Dennard scaling and therefore performance scaling of CPUs. In the meanwhile, FPGAs have gained traction as accelerators for data management. Our goal is to tackle the performance challenges of non-relational database systems with FPGA acceleration and, at the same time, address design challenges of FPGA acceleration itself. Therefore, we split this thesis up into two main lines of work: graph processing and flexible data processing. Because of the lacking benchmark practices for graph processing accelerators, we propose GraphSim. GraphSim is able to reproduce runtimes of these accelerators based on a memory access model of the approach. Through this simulation environment, we extract three performance-critical accelerator properties: asynchronous graph processing, compressed graph data structure, and multi-channel memory. Since these accelerator properties have not been combined in one system, we propose GraphScale. GraphScale is the first scalable, asynchronous graph processing accelerator working on a compressed graph and outperforms all state-of-the-art graph processing accelerators. Focusing on accelerator flexibility, we propose PipeJSON as the first FPGA-based JSON parser for arbitrary JSON documents. PipeJSON is able to achieve parsing at line-speed, outperforming the fastest, vectorized parsers for CPUs. Lastly, we propose the subgraph query processing accelerator GraphMatch which outperforms state-of-the-art CPU systems for subgraph query processing and is able to flexibly switch queries during runtime in a matter of clock cycles

An experimental study and evaluation of a new architecture for clinical decision support - integrating the openEHR specifications for the Electronic Health Record with Bayesian Networks

Healthcare informatics still lacks wide-scale adoption of intelligent decision support methods, despite continuous increases in computing power and methodological advances in scalable computation and machine learning, over recent decades. The potential has long been recognised, as evidenced in the literature of the domain, which is extensively reviewed. The thesis identifies and explores key barriers to adoption of clinical decision support, through computational experiments encompassing a number of technical platforms. Building on previous research, it implements and tests a novel platform architecture capable of processing and reasoning with clinical data. The key components of this platform are the now widely implemented openEHR electronic health record specifications and Bayesian Belief Networks. Substantial software implementations are used to explore the integration of these components, guided and supplemented by input from clinician experts and using clinical data models derived in hospital settings at Moorfields Eye Hospital. Data quality and quantity issues are highlighted. Insights thus gained are used to design and build a novel graph-based representation and processing model for the clinical data, based on the openEHR specifications. The approach can be implemented using diverse modern database and platform technologies. Computational experiments with the platform, using data from two clinical domains – a preliminary study with published thyroid metabolism data and a substantial study of cataract surgery – explore fundamental barriers that must be overcome in intelligent healthcare systems developments for clinical settings. These have often been neglected, or misunderstood as implementation procedures of secondary importance. The results confirm that the methods developed have the potential to overcome a number of these barriers. The findings lead to proposals for improvements to the openEHR specifications, in the context of machine learning applications, and in particular for integrating them with Bayesian Networks. The thesis concludes with a roadmap for future research, building on progress and findings to date

XML-Verarbeitung auf Grafikkarten

Die Extensible Markup Language (XML) ist ein weit verbreitetes, strukturiertes Format zur Datenspeicherung und zum Datenaustausch. Die Komplexität und die Menge der zu verarbeitenden XML-Daten hat in den vergangenen Jahren erheblich zugenommen, und sowohl in der Wissenschaft wie auch in der Wirtschaft wird an Verfahren zur schnellen Verarbeitung von XML gearbeitet. In einigen Arbeiten werden auch Verfahren zur parallelen Verarbeitung von XML mittels üblicher Mehrkern-CPUs entwickelt. Seit einigen Jahren gewinnt die parallele Verarbeitung allgemeiner Probleme mittels Grafikprozessoren (GPUs) an Bedeutung. GPUs ähneln in ihrer Architektur und Arbeitsweise dem bekannten Single Instruction Multiple Data-Prinzip. Daher sind GPUs besonders gut geeignet für die Verarbeitung massiv-datenparalleler Probleme wie etwa der Matrixmultiplikation oder der Bildverarbeitung. XML-Dokumente bzw. XML-Datenmodelle haben eine baumartige Struktur und folglich basieren viele der Verfahren zur Verarbeitung von XML auf dieser Baumstruktur. Solche Strukturen und Verfahren können jedoch wegen der Architektur von GPUs nicht trivial von CPUs auf GPUs übertragen werden, selbst wenn es sich um Verfahren zur parallelen Verarbeitung mittels Mehrkern-CPUs handelt. Daher existieren bisher kaum Arbeiten, die die parallele Verarbeitung von XML mittels GPUs untersuchen. In der vorliegenden Arbeit wird anhand einer konkreten XML-Anwendung, der Transformation von XML-Dokumenten mit XSLT, untersucht, inwieweit die Verarbeitung von XML an die massiv-parallele Architektur von GPUs angepasst werden kann. Dazu wird zunächst ein Konzept zur XSLT-Verarbeitung durch GPUs entworfen und danach werden die vier wichtigsten in diesem Konzept eingesetzten neuen Verfahren vorgestellt: ein schnelles Sortierverfahren für GPUs, ein Verfahren um GPUs als asynchrone Koprozessoren für die CPU einsetzen zu können, ein Verfahren um XPath-Ausdrücke parallel auszuwerten und schließlich ein Verfahren zur XSLT-Verarbeitung mittels GPUs. In umfangreichen Tests wird gezeigt, dass mit den hier dargelegten Verfahren die Nutzung von GPUs für die XPath- und XSLT-Verarbeitung in vielen Fällen zu einer Leistungssteigerung gegenüber der Verarbeitung durch Mehrkern-CPUs führt. Es ist demnach möglich, GPUs auch zur Verarbeitung von XML gewinnbringend einzusetzen

A software architecture for electro-mobility services: a milestone for sustainable remote vehicle capabilities

To face the tough competition, changing markets and technologies in automotive industry, automakers have to be highly innovative. In the previous decades, innovations were electronics and IT-driven, which increased exponentially the complexity of vehicle’s internal network. Furthermore, the growing expectations and preferences of customers oblige these manufacturers to adapt their business models and to also propose mobility-based services. One other hand, there is also an increasing pressure from regulators to significantly reduce the environmental footprint in transportation and mobility, down to zero in the foreseeable future. This dissertation investigates an architecture for communication and data exchange within a complex and heterogeneous ecosystem. This communication takes place between various third-party entities on one side, and between these entities and the infrastructure on the other. The proposed solution reduces considerably the complexity of vehicle communication and within the parties involved in the ODX life cycle. In such an heterogeneous environment, a particular attention is paid to the protection of confidential and private data. Confidential data here refers to the OEM’s know-how which is enclosed in vehicle projects. The data delivered by a car during a vehicle communication session might contain private data from customers. Our solution ensures that every entity of this ecosystem has access only to data it has the right to. We designed our solution to be non-technological-coupling so that it can be implemented in any platform to benefit from the best environment suited for each task. We also proposed a data model for vehicle projects, which improves query time during a vehicle diagnostic session. The scalability and the backwards compatibility were also taken into account during the design phase of our solution. We proposed the necessary algorithms and the workflow to perform an efficient vehicle diagnostic with considerably lower latency and substantially better complexity time and space than current solutions. To prove the practicality of our design, we presented a prototypical implementation of our design. Then, we analyzed the results of a series of tests we performed on several vehicle models and projects. We also evaluated the prototype against quality attributes in software engineering

Content based dissemination of XML data

Ph.DDOCTOR OF PHILOSOPH

State Management for Efficient Event Pattern Detection

Event Stream Processing (ESP) Systeme überwachen kontinuierliche Datenströme, um benutzerdefinierte Queries auszuwerten. Die Herausforderung besteht darin, dass die Queryverarbeitung zustandsbehaftet ist und die Anzahl von Teilübereinstimmungen mit der Größe der verarbeiteten Events exponentiell anwächst. Die Dynamik von Streams und die Notwendigkeit, entfernte Daten zu integrieren, erschweren die Zustandsverwaltung. Erstens liefern heterogene Eventquellen Streams mit unvorhersehbaren Eingaberaten und Queryselektivitäten. Während Spitzenzeiten ist eine erschöpfende Verarbeitung unmöglich, und die Systeme müssen auf eine Best-Effort-Verarbeitung zurückgreifen. Zweitens erfordern Queries möglicherweise externe Daten, um ein bestimmtes Event für eine Query auszuwählen. Solche Abhängigkeiten sind problematisch: Das Abrufen der Daten unterbricht die Stream-Verarbeitung. Ohne eine Eventauswahl auf Grundlage externer Daten wird das Wachstum von Teilübereinstimmungen verstärkt. In dieser Dissertation stelle ich Strategien für optimiertes Zustandsmanagement von ESP Systemen vor. Zuerst ermögliche ich eine Best-Effort-Verarbeitung mittels Load Shedding. Dabei werden sowohl Eingabeeevents als auch Teilübereinstimmungen systematisch verworfen, um eine Latenzschwelle mit minimalem Qualitätsverlust zu garantieren. Zweitens integriere ich externe Daten, indem ich das Abrufen dieser von der Verwendung in der Queryverarbeitung entkoppele. Mit einem effizienten Caching-Mechanismus vermeide ich Unterbrechungen durch Übertragungslatenzen. Dazu werden externe Daten basierend auf ihrer erwarteten Verwendung vorab abgerufen und mittels Lazy Evaluation bei der Eventauswahl berücksichtigt. Dabei wird ein Kostenmodell verwendet, um zu bestimmen, wann welche externen Daten abgerufen und wie lange sie im Cache aufbewahrt werden sollen. Ich habe die Effektivität und Effizienz der vorgeschlagenen Strategien anhand von synthetischen und realen Daten ausgewertet und unter Beweis gestellt.Event stream processing systems continuously evaluate queries over event streams to detect user-specified patterns with low latency. However, the challenge is that query processing is stateful and it maintains partial matches that grow exponentially in the size of processed events. State management is complicated by the dynamicity of streams and the need to integrate remote data. First, heterogeneous event sources yield dynamic streams with unpredictable input rates, data distributions, and query selectivities. During peak times, exhaustive processing is unreasonable, and systems shall resort to best-effort processing. Second, queries may require remote data to select a specific event for a pattern. Such dependencies are problematic: Fetching the remote data interrupts the stream processing. Yet, without event selection based on remote data, the growth of partial matches is amplified. In this dissertation, I present strategies for optimised state management in event pattern detection. First, I enable best-effort processing with load shedding that discards both input events and partial matches. I carefully select the shedding elements to satisfy a latency bound while striving for a minimal loss in result quality. Second, to efficiently integrate remote data, I decouple the fetching of remote data from its use in query evaluation by a caching mechanism. To this end, I hide the transmission latency by prefetching remote data based on anticipated use and by lazy evaluation that postpones the event selection based on remote data to avoid interruptions. A cost model is used to determine when to fetch which remote data items and how long to keep them in the cache. I evaluated the above techniques with queries over synthetic and real-world data. I show that the load shedding technique significantly improves the recall of pattern detection over baseline approaches, while the technique for remote data integration significantly reduces the pattern detection latency

Structural Summaries as a Core Technology for Efficient XML Retrieval

The Extensible Markup Language (XML) is extremely popular as a generic markup language for text documents with an explicit hierarchical structure. The different types of XML data found in today’s document repositories, digital libraries, intranets and on the web range from flat text with little meaningful structure to be queried, over truly semistructured data with a rich and often irregular structure, to rather rigidly structured documents with little text that would also fit a relational database system (RDBS). Not surprisingly, various ways of storing and retrieving XML data have been investigated, including native XML systems, relational engines based on RDBSs, and hybrid combinations thereof. Over the years a number of native XML indexing techniques have emerged, the most important ones being structure indices and labelling schemes. Structure indices represent the document schema (i.e., the hierarchy of nested tags that occur in the documents) in a compact central data structure so that structural query constraints (e.g., path or tree patterns) can be efficiently matched without accessing the documents. Labelling schemes specify ways to assign unique identifiers, or labels, to the document nodes so that specific relations (e.g., parent/child) between individual nodes can be inferred from their labels alone in a decentralized manner, again without accessing the documents themselves. Since both structure indices and labelling schemes provide compact approximate views on the document structure, we collectively refer to them as structural summaries. This work presents new structural summaries that enable highly efficient and scalable XML retrieval in native, relational and hybrid systems. The key contribution of our approach is threefold. (1) We introduce BIRD, a very efficient and expressive labelling scheme for XML, and the CADG, a combined text and structure index, and combine them as two complementary building blocks of the same XML retrieval system. (2) We propose a purely relational variant of BIRD and the CADG, called RCADG, that is extremely fast and scales up to large document collections. (3) We present the RCADG Cache, a hybrid system that enhances the RCADG with incremental query evaluation based on cached results of earlier queries. The RCADG Cache exploits schema information in the RCADG to detect cached query results that can supply some or all matches to a new query with little or no computational and I/O effort. A main-memory cache index ensures that reusable query results are quickly retrieved even in a huge cache. Our work shows that structural summaries significantly improve the efficiency and scalability of XML retrieval systems in several ways. Former relational approaches have largely ignored structural summaries. The RCADG shows that these native indexing techniques are equally effective for XML retrieval in RDBSs. BIRD, unlike some other labelling schemes, achieves high retrieval performance with a fairly modest storage overhead. To the best of our knowledge, the RCADG Cache is the only approach to take advantage of structural summaries for effectively detecting query containment or overlap. Moreover, no other XML cache we know of exploits intermediate results that are produced as a by-product during the evaluation from scratch. These are valuable cache contents that increase the effectiveness of the cache at no extra computational cost. Extensive experiments quantify the practical benefit of all of the proposed techniques, which amounts to a performance gain of several orders of magnitude compared to various other approaches

Efficient similarity computations on parallel machines using data shaping

Similarity computation is a fundamental operation in all forms of data. Big Data is, typically, characterized by attributes such as volume, velocity, variety, veracity, etc. In general, Big Data variety appears as structured, semi-structured or unstructured forms. The volume of Big Data in general, and semi-structured data in particular, is increasing at a phenomenal rate. Big Data phenomenon is posing new set of challenges to similarity computation problems occurring in semi-structured data. Technology and processor architecture trends suggest very strongly that future processors shall have ten\u27s of thousands of cores (hardware threads). Another crucial trend is that ratio between on-chip and off-chip memory to core counts is decreasing. State-of-the-art parallel computing platforms such as General Purpose Graphics Processors (GPUs) and MICs are promising for high performance as well high throughput computing. However, processing semi-structured component of Big Data efficiently using parallel computing systems (e.g. GPUs) is challenging. Reason being most of the emerging platforms (e.g. GPUs) are organized as Single Instruction Multiple Thread/Data machines which are highly structured, where several cores (streaming processors) operate in lock-step manner, or they require a high degree of task-level parallelism. We argue that effective and efficient solutions to key similarity computation problems need to operate in a synergistic manner with the underlying computing hardware. Moreover, semi-structured form input data needs to be shaped or reorganized with the goal to exploit the enormous computing power of \textit{state-of-the-art} highly threaded architectures such as GPUs. For example, shaping input data (via encoding) with minimal data-dependence can facilitate flexible and concurrent computations on high throughput accelerators/co-processors such as GPU, MIC, etc. We consider various instances of traditional and futuristic problems occurring in intersection of semi-structured data and data analytics. Preprocessing is an operation common at initial stages of data processing pipelines. Typically, the preprocessing involves operations such as data extraction, data selection, etc. In context of semi-structured data, twig filtering is used in identifying (and extracting) data of interest. Duplicate detection and record linkage operations are useful in preprocessing tasks such as data cleaning, data fusion, and also useful in data mining, etc., in order to find similar tree objects. Likewise, tree edit is a fundamental metric used in context of tree problems; and similarity computation between trees another key problem in context of Big Data. This dissertation makes a case for platform-centric data shaping as a potent mechanism to tackle the data- and architecture-borne issues in context of semi-structured data processing on GPU and GPU-like parallel architecture machines. In this dissertation, we propose several data shaping techniques for tree matching problems occurring in semi-structured data. We experiment with real world datasets. The experimental results obtained reveal that the proposed platform-centric data shaping approach is effective for computing similarities between tree objects using GPGPUs. The techniques proposed result in performance gains up to three orders of magnitude, subject to problem and platform