Many universities and research laboratories have developed low cost clusters, built from Commodity-Off-The-Shelf (COTS) components and running mostly free software. Research has shown that these types of systems are well-equipped to handle many problems requiring parallel processing. The primary components of clusters are hardware, networking, and system software. An important system software consideration for clusters is the choice of the message passing library. MPI (Message Passing Interface) has arguably become the most widely used message passing library on clusters and other parallel architectures, due in part to its existence as a standard. As a standard, MPI is open for anyone to implement, as long as the rules of the standard are followed. For this reason, a number of proprietary and freely available implementations have been developed. Of the freely available implementations, two have become increasingly popular: LAM (Local Area Multicomputer) and MPICH (MPI Chameleon). This thesis compares the performance of LAM and MPICH in an effort to provide performance data and analysis of the current releases of each to the cluster computing community. Specifically, the accomplishments of this thesis are: comparative testing of the High Performance Linpack benchmark (HPL); comparative testing of su3_rmd, an MPI application used in physics research; and a series of bandwidth comparisons involving eight MPI point-to-point communication constructs. All research was performed on a partition of the Wyeast SMP Cluster in the High Performance Computing Laboratory at Portland State University. We generate a vast amount of data, and show that LAM and MPICH perform similarly on many experiments, with LAM outperforming MPICH in the bandwidth tests and on a large problem size for su3_rmd. These findings, along with the findings of other research comparing the two libraries, suggest that LAM performs better than MPICH in the cluster environment. This conclusion may seem surprising, as MPICH has received more attention than LAM from MPI researchers. However, the two architectures are very different. LAM was originally designed for the cluster and networked workstation environments, while MPICH was designed to be portable across many different types of parallel architectures.

[EN] The field of audio signal processing has undergone a major development in recent years. Both the consumer and professional marketplaces continue to show growth in audio applications such as immersive audio schemes that offer optimal listening experience, intelligent noise reduction in cars or improvements in audio teleconferencing or hearing aids. The development of these applications has a common interest in increasing or improving the number of discrete audio channels, the quality of the audio or the sophistication of the algorithms. This often gives rise to problems of high computational cost, even when using common signal processing algorithms, mainly due to the application of these algorithms to multiple signals with real-time requirements. The field of High Performance Computing (HPC) based on low cost hardware elements is the bridge needed between the computing problems and the real multimedia signals and systems that lead to user's applications. In this sense, the present thesis goes a step further in the development of these systems by using the computational power of General Purpose Graphics Processing Units (GPGPUs) to exploit the inherent parallelism of signal processing for multichannel audio applications. The increase of the computational capacity of the processing devices has been historically linked to the number of transistors in a chip. However, nowadays the improvements in the computational capacity are mainly given by increasing the number of processing units and using parallel processing. The Graphics Processing Units (GPUs), which have now thousands of computing cores, are a representative example. The GPUs were traditionally used to graphic or image processing, but new releases in the GPU programming environments such as CUDA have allowed the use of GPUS for general processing applications. Hence, the use of GPUs is being extended to a wide variety of intensive-computation applications among which audio processing is included. However, the data transactions between the CPU and the GPU and viceversa have questioned the viability of the use of GPUs for audio applications in which real-time interaction between microphones and loudspeakers is required. This is the case of the adaptive filtering applications, where an efficient use of parallel computation in not straightforward. For these reasons, up to the beginning of this thesis, very few publications had dealt with the GPU implementation of real-time acoustic applications based on adaptive filtering. Therefore, this thesis aims to demonstrate that GPUs are totally valid tools to carry out audio applications based on adaptive filtering that require high computational resources. To this end, different adaptive applications in the field of audio processing are studied and performed using GPUs. This manuscript also analyzes and solves possible limitations in each GPU-based implementation both from the acoustic point of view as from the computational point of view. / [ES] El campo de procesado de señales de audio ha experimentado un desarrollo importante en los últimos años. Tanto el mercado de consumo como el profesional siguen mostrando un crecimiento en aplicaciones de audio, tales como: los sistemas de audio inmersivo que ofrecen una experiencia de sonido óptima, los sistemas inteligentes de reducción de ruido en coches o las mejoras en sistemas de teleconferencia o en audífonos. El desarrollo de estas aplicaciones tiene un propósito común de aumentar o mejorar el número de canales de audio, la propia calidad del audio o la sofisticación de los algoritmos. Estas mejoras suelen dar lugar a sistemas de alto coste computacional, incluso usando algoritmos comunes de procesado de señal. Esto se debe principalmente a que los algoritmos se suelen aplicar a sistemas multicanales con requerimientos de procesamiento en tiempo real. El campo de la Computación de Alto Rendimiento basado en elementos hardware de bajo coste es el puente necesario entre los problemas de computación y los sistemas multimedia que dan lugar a aplicaciones de usuario. En este sentido, la presente tesis va un paso más allá en el desarrollo de estos sistemas mediante el uso de la potencia de cálculo de las Unidades de Procesamiento Gráfico (GPU) en aplicaciones de propósito general. Con ello, aprovechamos la inherente capacidad de paralelización que poseen las GPU para procesar señales de audio y obtener aplicaciones de audio multicanal. El aumento de la capacidad computacional de los dispositivos de procesado ha estado vinculado históricamente al número de transistores que había en un chip. Sin embargo, hoy en día, las mejoras en la capacidad computacional se dan principalmente por el aumento del número de unidades de procesado y su uso para el procesado en paralelo. Las GPUs son un ejemplo muy representativo. Hoy en día, las GPUs poseen hasta miles de núcleos de computación. Tradicionalmente, las GPUs se han utilizado para el procesado de gráficos o imágenes. Sin embargo, la aparición de entornos sencillos de programación GPU, como por ejemplo CUDA, han permitido el uso de las GPU para aplicaciones de procesado general. De ese modo, el uso de las GPU se ha extendido a una amplia variedad de aplicaciones que requieren cálculo intensivo. Entre esta gama de aplicaciones, se incluye el procesado de señales de audio. No obstante, las transferencias de datos entre la CPU y la GPU y viceversa pusieron en duda la viabilidad de las GPUs para aplicaciones de audio en las que se requiere una interacción en tiempo real entre micrófonos y altavoces. Este es el caso de las aplicaciones basadas en filtrado adaptativo, donde el uso eficiente de la computación en paralelo no es sencillo. Por estas razones, hasta el comienzo de esta tesis, había muy pocas publicaciones que utilizaran la GPU para implementaciones en tiempo real de aplicaciones acústicas basadas en filtrado adaptativo. A pesar de todo, esta tesis pretende demostrar que las GPU son herramientas totalmente válidas para llevar a cabo aplicaciones de audio basadas en filtrado adaptativo que requieran elevados recursos computacionales. Con este fin, la presente tesis ha estudiado y desarrollado varias aplicaciones adaptativas de procesado de audio utilizando una GPU como procesador. Además, también analiza y resuelve las posibles limitaciones de cada aplicación tanto desde el punto de vista acústico como desde el punto de vista computacional. / [CA] El camp del processament de senyals d'àudio ha experimentat un desenvolupament important als últims anys. Tant el mercat de consum com el professional segueixen mostrant un creixement en aplicacions d'àudio, com ara: els sistemes d'àudio immersiu que ofereixen una experiència de so òptima, els sistemes intel·ligents de reducció de soroll en els cotxes o les millores en sistemes de teleconferència o en audiòfons. El desenvolupament d'aquestes aplicacions té un propòsit comú d'augmentar o millorar el nombre de canals d'àudio, la pròpia qualitat de l'àudio o la sofisticació dels algorismes que s'utilitzen. Això, sovint dóna lloc a sistemes d'alt cost computacional, fins i tot quan es fan servir algorismes comuns de processat de senyal. Això es deu principalment al fet que els algorismes se solen aplicar a sistemes multicanals amb requeriments de processat en temps real. El camp de la Computació d'Alt Rendiment basat en elements hardware de baix cost és el pont necessari entre els problemes de computació i els sistemes multimèdia que donen lloc a aplicacions d'usuari. En aquest sentit, aquesta tesi va un pas més enllà en el desenvolupament d'aquests sistemes mitjançant l'ús de la potència de càlcul de les Unitats de Processament Gràfic (GPU) en aplicacions de propòsit general. Amb això, s'aprofita la inherent capacitat de paral·lelització que posseeixen les GPUs per processar senyals d'àudio i obtenir aplicacions d'àudio multicanal. L'augment de la capacitat computacional dels dispositius de processat ha estat històricament vinculada al nombre de transistors que hi havia en un xip. No obstant, avui en dia, les millores en la capacitat computacional es donen principalment per l'augment del nombre d'unitats de processat i el seu ús per al processament en paral·lel. Un exemple molt representatiu són les GPU, que avui en dia posseeixen milers de nuclis de computació. Tradicionalment, les GPUs s'han utilitzat per al processat de gràfics o imatges. No obstant, l'aparició d'entorns senzills de programació de la GPU com és CUDA, han permès l'ús de les GPUs per a aplicacions de processat general. D'aquesta manera, l'ús de les GPUs s'ha estès a una àmplia varietat d'aplicacions que requereixen càlcul intensiu. Entre aquesta gamma d'aplicacions, s'inclou el processat de senyals d'àudio. No obstant, les transferències de dades entre la CPU i la GPU i viceversa van posar en dubte la viabilitat de les GPUs per a aplicacions d'àudio en què es requereix la interacció en temps real de micròfons i altaveus. Aquest és el cas de les aplicacions basades en filtrat adaptatiu, on l'ús eficient de la computació en paral·lel no és senzilla. Per aquestes raons, fins al començament d'aquesta tesi, hi havia molt poques publicacions que utilitzessin la GPU per implementar en temps real aplicacions acústiques basades en filtrat adaptatiu. Malgrat tot, aquesta tesi pretén demostrar que les GPU són eines totalment vàlides per dur a terme aplicacions d'àudio basades en filtrat adaptatiu que requereixen alts recursos computacionals. Amb aquesta finalitat, en la present tesi s'han estudiat i desenvolupat diverses aplicacions adaptatives de processament d'àudio utilitzant una GPU com a processador. A més, aquest manuscrit també analitza i resol les possibles limitacions de cada aplicació, tant des del punt de vista acústic, com des del punt de vista computacional. / Lorente Giner, J. (2015). Adaptive signal processing for multichannel sound using high performance computing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/58427

The problem of efficient parallelization of 2D Ising spin systems requires realistic algorithmic design and implementation based on an understanding of issues from computer science and statistical physics. In this work, we not only consider fundamental parallel computing issues but also ensure that the major constraints and criteria of 2D Ising spin systems are incorporated into our study. This realism in both parallel computation and statistical physics has rarely been reflected in previous research for this problem. In this thesis,we designed and implemented a variety of parallel algorithms for both sweep spin selection and random spin selection. We analyzed our parallel algorithms on a portable and general parallel machine model, namely the LogP model. We were able to obtain rigorous theoretical run-times on LogP for all the parallel algorithms. Moreover, a guiding equation was derived for choosing data layouts (blocked vs. stripped) for sweep spin selection. In regards to random spin selection, we were able to develop parallel algorithms with efficient communication schemes. We analyzed randomness of our schemes using statistical methods and provided comparisons between the different schemes. Furthermore, algorithms were implemented and performance data gathered and analyzed in order to determine further design issues and validate theoretical analysis. / Master of Science

Graphs, as simple yet powerful data structures, play a pivotal role in modeling and analyzing relationships among real-world entities. In the data representation and analysis landscape, graph data structures have established themselves as a fundamental paradigm for modeling and understanding complex relationships in various domains. The intrinsic domain independence, expressiveness, and the wide variety of analysis options based on graph theory have gained significant attention in both research and industry. In recent years, companies have increasingly leveraged graph technology to represent, store, query, and analyze graph-shaped data. This has been notably impactful in uncovering hidden patterns and predicting relationships within diverse domains such as social networks, Internet of Things (IoT), biological systems, and medical networks. However, the dynamic nature of most real-world graphs is often neglected in existing approaches, which might lead to inaccurate analytical results or an incomplete understanding of evolving patterns within the graph over time. Temporal graphs, in contrast, are a particular type of graphs that maintain changing structures and properties over time. They have gained significant attention in various domains, from financial networks over micromobility networks to supply chains and biological networks. A majority of these real-world networks are not static but rather exhibit high dynamics, which are rarely considered in data models, query languages, and analyses, although analytical questions often require an evaluation of the network's evolution. This doctoral thesis addresses this critical gap by presenting a comprehensive study on analyzing and exploring temporal property graphs. It focuses on scalability and proposes novel methodologies to enhance accuracy and comprehensiveness in analyzing evolving graph patterns over time. It also offers insights into real-time querying, addressing various challenges that emerge when the time dimension is treated as an integral part of the graph. This thesis introduces the Temporal Property Graph Model (TPGM), a sophisticated data model designed for bitemporal modeling of vertices and edges, as well as logical abstractions of subgraphs and graph collections. The reference implementation of this model, namely Gradoop, is a graph dataflow system explicitly designed for scalable and distributed analysis of static and temporal property graphs. Gradoop empowers analysts to construct comprehensive and flexible temporal graph processing workflows through a declarative analytical language. The system supports various analytical temporal graph operators, such as snapshot retrieval, temporal graph pattern matching, time-dependent grouping, and temporal metrics such as degree evolution. The thesis provides an in-depth analysis of the data model, system architecture, and implementation details of Gradoop and its operators. Comprehensive performance evaluations have been conducted on large real-world and synthetic temporal graphs, providing valuable insights into the system's capabilities and efficiency. Furthermore, this thesis demonstrates the flexibility of the temporal graph model and its operators through a practical use case based on a call center network. In this scenario, a TPGM operator pipeline is developed to answer a complex and time-dependent analytical question. We also showcase the Temporal Graph Explorer (TGE), a web-based user interface designed to explore temporal graphs, leveraging Gradoop as a backend. The TGE empowers users to delve into temporal graph dynamics by enabling the retrieval of snapshots from the graph's past states, computing differences between these snapshots, and providing temporal summaries of graphs. This functionality allows for a comprehensive understanding of graph evolution through diverse visualizations. Real-world temporal graph data from bicycle rentals highlight the system's flexibility and configurability of the selected temporal operators. The impact of graph changes on its characteristics can also be explored by examining centrality measures over time. Centrality measures, encompassing both node and graph metrics, quantify the characteristics of individual nodes or the entire graph. In the dynamic context of temporal graphs, where the graph changes over time, node and graph metrics also undergo implicit changes. This thesis tackles the challenge of adapting static node and graph metrics to temporal graphs. It proposes temporal extensions for selected degree-dependent metrics and aggregations, emphasizing the importance of including the time dimension in the metrics. This thesis demonstrates that a metric conventionally representing a scalar value for static graphs results in a time series when applied to temporal graphs. It introduces a baseline algorithm for calculating the degree evolution of vertices within a temporal graph, and its practical implementation in Gradoop is presented. The scalability of this algorithm is evaluated using both real-world and synthetic datasets, providing valuable insights into its performance across diverse scenarios. Such time series data can also be captured from the application scenario as properties of nodes and edges, such as sensor readings in the IoT domain. In light of this, we showcase significant advancements, including an extended version of the TPGM that supports time series data in temporal graphs. Additionally, we introduce a temporal graph query language based on Oracle's language PGQL to facilitate seamless querying of time-oriented graph structures. Furthermore, we present a novel continuous graph-based event detection approach to support scenarios involving more time-sensitive use cases. The increasing frequency of graph changes and the need to react quickly to emerging patterns leads to the need for a unified declarative graph query language that can evaluate queries on graph streams. To address the increasing importance of real-time data analysis and management, the thesis introduces the syntax and semantics of Seraph, a Cypher-based language that supports native streaming features within property graph query languages. The semantics of Seraph combine stream processing with property graphs and time-varying relations, treating time as a first-class citizen. Real-world industrial use cases demonstrate the usage of Seraph for graph-based continuous queries. After evaluating lessons learned from the development and research on Gradoop, a dissertation summary and an outlook on future work are given in a final section. This doctoral thesis comprehensively examines scalable analysis and exploration techniques for temporal property graphs, focusing on Gradoop and its system architecture, data model, operators, and performance evaluations. It also explores the evolution of node and graph metrics and the theoretical foundation of a real-time query language, contributing to the advancement of temporal graph analysis in various domains.:1 Introduction 2 Background and Related Work 3 The TPGM and Gradoop 4 Gradoop Application Examples 5 Evolution of Degree Metrics 6 The Fusion of Graph and Time-Series Data 7 Seraph: Continuous Queries on Property Graph Streams 8 Lessons Learned from Gradoop 9 Conclusion and Outlook Bibliography

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The way in which application systems and software are built has changed dramatically over the past few years. This is mainly due to advances in hardware technology, programming languages, as well as the requirement to build better software application systems in less time. The importance of mondial (worldwide) communication between systems is also growing exponentially. People are using network-based applications daily, communicating not only locally, but also globally. The Internet, the global network, therefore plays a significant role in the development of new software. Distributed object computing is one of the computing paradigms that promise to solve the need to develop clienVserver application systems, communicating over heterogeneous environments. This study, of limited scope, concentrates on one crucial element without which distributed object computing cannot be implemented. This element is the communication software, also called middleware, which allows objects situated on different hardware platforms to communicate over a network. Two of the most important middleware standards for distributed object computing today are the Common Object Request Broker Architecture (CORBA) from the Object Management Group, and the Distributed Component Object Model (DCOM) from Microsoft Corporation. Each of these standards is implemented in commercially available products, allowing distributed objects to communicate over heterogeneous networks. In studying each of the middleware standards, a formal way of comparing CORBA and DCOM is presented, namely meta-modelling. For each of these two distributed object infrastructures (middleware), meta-models are constructed. Based on this uniform and unbiased approach, a comparison of the two distributed object infrastructures is then performed. The results are given as a set of tables in which the differences and similarities of each distributed object infrastructure are exhibited. By adopting this approach, errors caused by misunderstanding or misinterpretation are minimised. Consequently, an accurate and unbiased comparison between CORBA and DCOM is made possible, which constitutes the main aim of this dissertation. / Computing / M. Sc. (Computer Science)

Thesis (MSc)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: Cloud computing is seen as one of top 10 disruptive changes in IT for the next decade by leading research analysts. Consequently, enterprises are starting to investigate the effect it will have on the strategic direction of their businesses and technology stacks. Because of the disruptive nature of the paradigm shift introduced by it, as well as the strategic impact thereof, it is necessary that a structured approach with regard to risk, value and operational cost is followed with the decision on its relevance, as well as the selection of a platform if needed. The purpose of this thesis is to provide a reference model and its associating framework that can be used to evaluate private cloud management platforms, as well as the technologies associated with it. / AFRIKAANSE OPSOMMING: Wolk berekening word deur vooraanstaande navorsing ontleders as een van die top 10 ontwrigtende veranderings vir IT in die volgende dekade beskou. Gevolglik begin korporatiewe ondernemings met ondersoeke om te bepaal wat die invloed daarvan op hulle strategiese rigting en tegnologië gaan wees. Die ontwrigtende aard van die paradigma skuif, asook die strategiese impak daarvan, noodsaak ’n gestruktureerde ondersoek na die toepaslikheid en keuse van ’n platform, indien nodig, met betrekking tot risiko, waarde en operasionele koste. Die doel van hierdie tesis is om ’n verwysings model, en ’n raamwerk wat dit implementeer, saam te stel wat dan gebruik kan word om privaat wolk berekening platforms te evalueer.

International Telemetering Conference Proceedings / October 26-29, 1998 / Town & Country Resort Hotel and Convention Center, San Diego, California / The necessity to acquire and analyze data dates back to the beginning of science itself. Long ago, a scientist may have run experiments and noted the results on a piece of paper. These notes became the data. The method was crude, but effective. As experiments got more complex, the need for better methodologies arose. Scientists began using computers to gather, analyze, and store the data. This method worked well for most types of data acquisition. As the amount of data being collected increased, larger computers, faster processors, and faster storage devices were used in order to keep up with the demand. This method was more refined, but still did not meet the needs of the scientific community. Requirements began to change in the data acquisition arena. More people wanted access to the data in real time. Companies producing large data acquisition systems began to move toward a network-based solution. This architecture featured a specialized computer called the server, which contained all of the data acquisition hardware. The server handled requests from multiple clients and handled the data flow to the network, data displays, and the archive medium. While this solution worked well to satisfy most requirements, it fell short in meeting others. The ability to have multiple computers working together across a local or wide area network (LAN or WAN) was not addressed. In addition, this architecture inherently had a single point of failure. If the server machine went down, all data from all sources was lost. Today, we see that the requirements for data acquisition systems include features only dreamed of five years ago. These new systems are linked around the world by wide area networks. They may include code to command satellites or handle 250 Mbps download rates. They must produce data for dozens of users at once, be customizable by the end user, and they must run on personal computers (PCs)! Systems like these cannot work using the traditional client/server model of the past. The data acquisition industry demands systems with far more features than were traditionally available. These systems must provide more reliability and interoperability, and be available at a fraction of the cost. To this end, we must use commercial-off-the-shelf (COTS) computers that operate faster than the mainframe computers of only a decade ago. These computers must run software that is smart, reliable, scalable, and easy to use. All of these requirements can be met by a network of PCs running the Windows NT operating system.