DSM-PM2 adequacy for distributed constraint programming

As Redes de alta velocidade e o melhoramento rápido da performance dos microprocessadores fazem das redes de computadores um veículo apelativo para computação paralela. Não é preciso hardware especial para usar computadores paralelos e o sistema resultante é extensível e facilmente alterável. A programação por restrições é um paradigma de programação em que as relações entre as variáveis pode ser representada por restrições. As restrições diferem das primitivas comuns das outras linguagens de programação porque, ao contrário destas, não específica uma sequência de passos a executar mas antes a definição das propriedades para encontrar as soluções de um problema específico. As bibliotecas de programação por restrições são úteis visto elas não requerem que os programadores tenham que aprender novos skills para uma nova linguagem mas antes proporcionam ferramentas de programação declarativa para uso em sistemas convencionais. A tecnologia de Memoria Partilhada Distribuída (Distributed Shared Memory) apresenta-se como uma ferramenta para uso em aplicações distribuídas em que a informação individual partilhada pode ser acedida diretamente. Nos sistemas que suportam esta tecnologia os dados movem-se entre as memórias principais dos diversos nós de um cluster. Esta tecnologia poupa o programador às preocupações de passagem de mensagens onde ele teria que ter muito trabalho de controlo do comportamento do sistema distribuído. Propomos uma arquitetura orientada para a distribuição de Programação por Restrições que tenha os mecanismos da propagação e da procura local como base sobre um ambiente CC-NUMA distribuído usando memória partilhada distribuída. Os principais objetivos desta dissertação podem ser sumarizados em: - Desenvolver um sistema resolvedor de restrições, baseado no sistema AJ ACS [3], usando a linguagem ”C', linguagem nativa da biblioteca de desenvolvimento paralelo experimentada: O PM2 [4] - Adaptar, experimentar e avaliar a adequação deste sistema resolvedor de restrições usando DSM-PM2 [1] a um ambiente distribuído assente numa arquitetura CC-NUMA; /ABSTRACT - High-speed networks and rapidly improving microprocessor performance make networks of workstations an increasingly appealing vehicle for parallel computing. No special hardware is required to use this solution as a parallel computer, and the resulting system can be easily maintained, extended and upgraded. Constraint programming is a programming paradigm where relations between variables can be stated in the form of constraints. Constraints differ from the common primitives of other programming languages in that they do not specify a step or sequence of steps to execute but rather the properties of a solution to be found. Constraint programming libraries are useful as they do not require the developers to acquire skills for a new language, providing instead declarative programming tools for use within conventional systems. Distributed Shared Memory presents itself as a tool for parallel application in which individual shared data items can be accessed directly. In systems that support Distributed Shared Memory, data moves between main memories of different nodes. The Distributed Shared Memory spares the programmer the concerns of massage passing, where he would have to put allot of effort to control the distributed system behavior. We propose an architecture aimed for Distributed Constraint Programming Solving that relies on propagation and local search over a CC-NUMA distributed environment using Distributed Shared Memory. The main objectives of this thesis can be summarized as: - Develop a Constraint Solving System, based on the AJ ACS [3] system, in the C language, the native language of the experimented Parallel library - PM2 [4]; - Adapt, experiment and evaluate the developed constraint solving system distributed suitability by using DSM-PM2 [1] over a CC-NUMA architecture distributed environment

Processing of an iceberg query on distributed and centralized databases

Master'sMASTER OF SCIENC

Neural network methods in analysing and modelling time varying processes

Statistical data analysis is applied in many fields in order to gain understanding to the complex behaviour of the system or process under interest. For this goal, observations are collected from the process, and models are built in an effort to capture the essential structure from the observed data. In many applications, e.g. process control and pattern recognition, the modeled process is time-dependent, and thus modeling the temporal context is essential. In this thesis, neural network methods in statistical data analysis and especially in temporal sequence processing (TSP) are considered. Neural networks are a class of statistical models, applicable in many tasks from data exploration to regression and classification. Neural networks suitable for TSP can model time dependent phenomena, typically by utilizing delay lines or recurrent connections within the network. Recurrent Self-Organizing Map (RSOM) is an unsupervised neural network model capable of processing pattern sequences. The application of the RSOM with local models in temporal sequence prediction is presented. The RSOM is applied to divide the input pattern sequences into clusters, and local models are estimated corresponding to these clusters. In case studies, time series prediction problems are considered. Prediction results gained from the RSOM model show better performance than the model with conventional Self-Organizing Map. The RSOM can capture temporal context from the pattern sequence, which is useful in the presented prediction tasks. As another application, a neural network model for optimizing a Web cache is proposed. Web caches store recently requested Web objects, and are typically shared by many clients. A caching policy decides which objects are removed when the storage space is full. In the proposed approach a model predicts the value of each cache object by utilizing features extracted from the object. Only syntactic features are used, which enables efficient estimation and application of the model. The caching policy can be optimized based on the predicted values and a cost model designed according to the objectives of the caching. In a case study, different stages and decisions made during the data analysis and model building are presented. The results gained suggest that the proposed approach is useful in the application.reviewe

Proceedings of the Workshop on Space Telerobotics, volume 1

These proceedings report the results of a workshop on space telerobotics, which was held at the Jet Propulsion Laboratory, January 20-22, 1987. Sponsored by the NASA Office of Aeronautics and Space Technology (OAST), the Workshop reflected NASA's interest in developing new telerobotics technology for automating the space systems planned for the 1990s and beyond. The workshop provided a window into NASA telerobotics research, allowing leading researchers in telerobotics to exchange ideas on manipulation, control, system architectures, artificial intelligence, and machine sensing. One of the objectives was to identify important unsolved problems of current interest. The workshop consisted of surveys, tutorials, and contributed papers of both theoretical and practical interest. Several sessions were held on the themes of sensing and perception, control execution, operator interface, planning and reasoning, and system architecture

A static, transaction based design methodology for hard real-time systems

This thesis is concerned with the design and implementation stages of the development lifecycle of a class of systems known as hard real-time systems. Many of the existing methodologies are appropriate for meeting the functional requirements of this class of systems. However, it is proposed that these methodologies are not entirely appropriate for meeting the non-functional requirement of deadlines for work within these real-time systems. After discussing the concept of real-time systems and their characteristic requirements, this thesis proposes the use of a general transaction model of execution for the implementation of the system. Whereas traditional methodologies consider the system from the flow of data or control in the system, we consider the system from the viewpoint of the role of each shared data entity. A control dependency is implied between otherwise independent processes that make use of a shared data entity; our viewpoint is known as the data dependency viewpoint. This implied control dependency between independent processes, necessary to preserve the consistency of the entity in the face of concurrent access, is ignored during the design stages of other methodologies. In considering the role of each data entity, it is possible to generate other viewpoints, such as the dataflow through the processes, automatically. This however, is not considered in the work. This thesis describes a staged methodology for taking the requirements specification for a system and generating a design and implementation for that system. The methodology is intended to be more than a set of vague guidelines for implementation; a more rigid approach to the design and implementation stages is sought. The methodology begins by decomposing the system into more manageable units of processing. These units are known as tasks with a very low degree of coupling and high degree of cohesion. Following the system decomposition, the data dependency viewpoint is constructed; a descriptive notation and CASE tool support this viewpoint. From this viewpoint, implementation issues such as generating control flow; task and data allocation and hard real-time scheduling concerns, are addressed. A complete runtime environment to support the transaction model is described. This environment is hierarchical and can be adapted to many distributed implementations. Finally, the stages of the methodology are applied to a large example, a Ship Control System. Starting with a specification of the requirements, the methodology is applied to generate a design and implementation of the system

A method to detect and represent temporal patterns from time series data and its application for analysis of physiological data streams

In critical care, complex systems and sensors continuously monitor patients??? physiological features such as heart rate, respiratory rate thus generating significant amounts of data every second. This results to more than 2 million records generated per patient in an hour. It???s an immense challenge for anyone trying to utilize this data when making critical decisions about patient care. Temporal abstraction and data mining are two research fields that have tried to synthesize time oriented data to detect hidden relationships that may exist in the data. Various researchers have looked at techniques for generating abstractions from clinical data. However, the variety and speed of data streams generated often overwhelms current systems which are not designed to handle such data. Other attempts have been to understand the complexity in time series data utilizing mining techniques, however, existing models are not designed to detect temporal relationships that might exist in time series data (Inibhunu & McGregor, 2016). To address this challenge, this thesis has proposed a method that extends the existing knowledge discovery frameworks to include components for detecting and representing temporal relationships in time series data. The developed method is instantiated within the knowledge discovery component of Artemis, a cloud based platform for processing physiological data streams. This is a unique approach that utilizes pattern recognition principles to facilitate functions for; (a) temporal representation of time series data with abstractions, (b) temporal pattern generation and quantification (c) frequent patterns identification and (d) building a classification system. This method is applied to a neonatal intensive care case study with a motivating problem that discovery of specific patterns from patient data could be crucial for making improved decisions within patient care. Another application is in chronic care to detect temporal relationships in ambulatory patient data before occurrence of an adverse event. The research premise is that discovery of hidden relationships and patterns in data would be valuable in building a classification system that automatically characterize physiological data streams. Such characterization could aid in detection of new normal and abnormal behaviors in patients who may have life threatening conditions

Reports of planetary geology program, 1977-1978

A compilation of abstracts of reports which summarizes work conducted by Planetary Geology Principal Investigators and their associates is presented. Full reports of these abstracts were presented to the annual meeting of Planetary Geology Principal Investigators and their associates at the Universtiy of Arizona, Tucson, Arizona, May 31, June 1 and 2, 1978

Effizientes Programmiermodell für OpenMP auf einem Cluster-basierten Many-Core-System

Da die Komplexität „System-on-Chip“ (SoC) auch weiterhin zunimmt, wird man die Herausforderungen aufgrund der Konvergenz der Software- und Hardwareentwicklung nicht ignorieren können. Dies gilt auch für den Umgang mit dem hierarchischen Design, in dem die Prozessorkerne in Clustern oder sogenannten „Tiles“ angeordnet werden, um mittels eines schnellen lokalen Speicherzugriffs eine geringe Latenz und eine hohe Bandbreite der lokalen Kommunikation zu gewährleisten. Aus der Sicht eines Programmierers ist es wünschenswert, sich diese Eigenheiten der Hardware zunutze zu machen und sie bei der Ausgestaltung der abstrakten Parallel-Programmierung gewissenhaft und zielführend zu berücksichtigen. Diese Dissertation überwindet viele Engpässe in Bezug auf die Skalierbarkeit Cluster-basierter Many-Core-Systeme und führt das Programmiermodell OpenMP zur Vereinfachung der Anwendungsentwicklung ein. OpenMP abstrahiert von der Sichtweise des Programmierers – und es werden Richtlinien eingeführt, mit denen Schleifen in Programmsequenzen eingeteilt werden, als Basis für die parallele Programmierung. In dieser Arbeit wird das OpenMP-Modell bespielhaft in einem konkreten Cluster-basierten Many-Core-System umgesetzt; dem Intel Single-Chip Cloud Computer (SCC). Es wird eine schlanke und hoch-optimierte Laufzeitschicht für die Ausführung von OpenMP sowie ein Speichermodell vorgestellt. Auf Basis dieser Laufzeitschicht wird der parallele Code automatisch von einem nativen Backend-Compiler (GCC 4.6) erzeugt, der mit der Laufzeitbibliothek verknüpft ist. Im Rahmen der Arbeit wird auf einen effizienten Designansatz für die OpenMP-Programmierung eingegangen, wobei der Intel SCC als Beispiel für Cluster-basierte Systeme zum Einsatz kommt. In nicht-Cache-kohärenten Systemen dient die SCC OpenMP Laufzeitbibliothek primär dazu, die folgenden Herausforderungen zu bewältigen: 1. Die Ausführung von unmodifizierten, bestehenden OpenMP Programmen auf solchen Systemen. 2. Die Portierung des OpenMP-Speichermodells auf den SCC. 3. Die Synchronisation der parallelen Threads, auf die ein beträchtlicher Anteil der Ausführungszeit einer Anwendung entfällt. Eine Reihe weiterer Beispiele, basierend auf verschiedenen gebräuchlichen Kernen und realen Anwendungen, untermauert die Tauglichkeit von OpenMP – und eine Reihe von Experimenten zeigt, wie dieses Modell zu einer deutlichen Beschleunigung (bis zu 48-fach) in verschiedenen parallelen Anwendungen führt.As the complexity of systems-on-chip (SoCs) continues to increase, it is no longer possible to ignore the challenges caused by the convergence of software and hardware development. This involves attempts to deal with the hierarchical design – in which several cores are grouped in clusters or tiles – to ensure low-latency, high-bandwidth local communication by relying on fast local memories. From a programmer’s perspec- tive, it is desirable to make use of these peculiarities of the hardware, which must be clearly and carefully taken into account when designing the support for high-level parallel programming models. This dissertation overcomes many scalability bottlenecks in cluster-based many-core systems and introduces the OpenMP programming model as a means of simplifying application development. OpenMP represents an abstraction of the programmer’s view by providing abundant directives that decompose loops in sequential programs and lead to parallel programs. In this work, the full OpenMP model is implemented on a specific instance of a cluster-based many-core system: the Intel Single-chip Cloud Computer (SCC). In this thesis, a lightweight and highly optimized runtime layer for OpenMP execution and memory model by generating the parallel code that is automatically compiled by native back-end compiler (GCC 4.6) that linked with the runtime library. In this dissertation, I will address an efficient design approach of the OpenMP pro- gramming model for the Intel SCC as an example for cluster-based systems. The SCC OpenMP runtime library is designed to cope with three main challenges in a non-cache coherent system: 1. Executing unmodified legacy OpenMP programs on such system. 2. Landing OpenMP memory model on the SCC. 3. Synchronization in the work of parallel threads accounts for a sizeable fraction of an application’s execution time. Furthermore, the effectiveness of OpenMP is demonstrated on a set of widely used kernels and real-world applications. An extensive set of experiments shows how this model achieves significant parallel speedups up to 48x in several applications