Dissertations / Theses: 'Hybrid Computing'

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Grandin, Henrik. "Hybrid Quaternary/Binary Computing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-305243.

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Due to scaling limitations and the end of Dennard scaling, energy efficiency has become a primary design goal. Approximate computing aims to solve this problem by sacrificing accuracy for efficiency. This project have evaluated a dual mode architecture that gives accuracy when needed and energy efficiency when it is possible. By combining binary and quaternary logic, up to half of the logic could be deactivated whenever absolute correctness is not critical. This project also proposes a hybrid mode, which aims to increase the number of cases where a more energy efficient representation could be used. By combining the two modes through using the more stable binary logic for the bits with higher significance, while at the same time using the energy efficient quaternary logic for the bits with the least significance, energy can still be saved while at the same time drastically limiting the potential size of the error. Simulations have shown that the hybrid mode not only succeeds in decreasing the maximum size of errors, but also reduces the total number of errors, compared to the quaternary mode. Results show increased energy efficiency of 10% without altering the perceived result at all. If we allow for some degradation of the result, the energy efficiency would improve with almost 40%.

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Lee, Joo Hong. "Hybrid Parallel Computing Strategies for Scientific Computing Applications." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28882.

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Multi-core, multi-processor, and Graphics Processing Unit (GPU) computer architectures pose significant challenges with respect to the efficient exploitation of parallelism for large-scale, scientific computing simulations. For example, a simulation of the human tonsil at the cellular level involves the computation of the motion and interaction of millions of cells over extended periods of time. Also, the simulation of Radiative Heat Transfer (RHT) effects by the Photon Monte Carlo (PMC) method is an extremely computationally demanding problem. The PMC method is example of the Monte Carlo simulation method—an approach extensively used in wide of application areas. Although the basic algorithmic framework of these Monte Carlo methods is simple, they can be extremely computationally intensive. Therefore, an efficient parallel realization of these simulations depends on a careful analysis of the nature these problems and the development of an appropriate software framework. The overarching goal of this dissertation is develop and understand what the appropriate parallel programming model should be to exploit these disparate architectures, both from the metric of efficiency, as well as from a software engineering perspective. In this dissertation we examine these issues through a performance study of PathSim2, a software framework for the simulation of large-scale biological systems, using two different parallel architectures’ distributed and shared memory. First, a message-passing implementation of a multiple germinal center simulation by PathSim2 is developed and analyzed for distributed memory architectures. Second, a germinal center simulation is implemented on shared memory architecture with two parallelization strategies based on Pthreads and OpenMP. Finally, we present work targeting a complete hybrid, parallel computing architecture. With this work we develop and analyze a software framework for generic Monte Carlo simulations implemented on multiple, distributed memory nodes consisting of a multi-core architecture with attached GPUs. This simulation framework is divided into two asynchronous parts: (a) a threaded, GPU-accelerated pseudo-random number generator (or producer), and (b) a multi-threaded Monte Carlo application (or consumer). The advantage of this approach is that this software framework can be directly used within any Monte Carlo application code, without requiring application-specific programming of the GPU. We examine this approach through a performance study of the simulation of RHT effects by the PMC method on a hybrid computing architecture. We present a theoretical analysis of our proposed approach, discuss methods to optimize performance based on this analysis, and compare this analysis to experimental results obtained from simulations run on two different hybrid, parallel computing architectures.
Ph. D.

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Holm, Marcus. "Scientific computing on hybrid architectures." Licentiate thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-200242.

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Modern computer architectures, with multicore CPUs and GPUs or other accelerators, make stronger demands than ever on writers of scientific code. As a rule of thumb, the fastest, most efficient program consists of labor-intensive code written by expert programmers for a certain application on a particular computer. This thesis deals with several algorithmic and technical approaches towards effectively satisfying the demand for high-performance parallel programming without incurring such a high cost in expert programmer time. Effective programming is accomplished by writing performance-portable code where performance-critical functionality is provided either by external software or at least a balance between maintainability/generality and efficiency.
UPMARC
eSSENCE

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Abraham, Ajith 1968. "Hybrid soft computing : architecture optimization and applications." Monash University, Gippsland School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8676.

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Bentz, Jonathan Lee. "Hybrid programming in high performance scientific computing." [Ames, Iowa : Iowa State University], 2006.

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Wei, Jian. "Hybrid mobile computing for connected autonomous vehicles." Thesis, Aston University, 2018. http://publications.aston.ac.uk/37533/.

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With increasing urbanization and the number of cars on road, there are many global issues on modern transport systems. Autonomous driving and connected vehicles are the most promising technologies to tackle these issues. The so-called integrated technology connected autonomous vehicles (CAV) can provide a wide range of safety applications for safer, greener and more efficient intelligent transport systems (ITS). As computing is an extreme component for CAV systems, various mobile computing models including mobile local computing, mobile edge computing and mobile cloud computing are proposed. However it is believed that none of these models fits all CAV applications, which have highly diverse quality of service (QoS) requirements such as communication delay, data rate, accuracy, reliability and/or computing latency. In this thesis, we are motivated to propose a hybrid mobile computing model with objective of overcoming limitations of individual models and maximizing the performances for CAV applications. In proposed hybrid mobile computing model three basic computing models and/or their combinations are chosen and applied to different CAV applications, which include mobile local computing, mobile edge computing and mobile cloud computing. Different computing models and their combinations are selected according to the QoS requirements of the CAV applications. Following the idea, we first investigate the job offloading and allocation of computing and communication resources at the local hosts and external computing centers with QoS aware and resource awareness. Distributed admission control and resource allocation algorithms are proposed including two baseline non-cooperative algorithms and a matching theory based cooperative algorithm. Experiment results demonstrate the feasibility of the hybrid mobile computing model and show large improvement on the service quality and capacity over existing individual computing models. The matching algorithm also largely outperforms the baseline non-cooperative algorithms. In addition, two specific use cases of the hybrid mobile computing for CAV applications are investigated: object detection with mobile local computing where only local computing resources are used, and movie recommendation with mobile cloud computing where remote cloud resources are used. For object detection, we focus on the challenges of detecting vehicles, pedestrians and cyclists in driving environment and propose three methods to an existing CNN based object detector. Large detection performance improvement is obtained over the KITTI benchmark test dataset. For movie recommendation we propose two recommendation models based on a general framework of integrating machine learning and collaborative filtering approach. The experiment results on Netflix movie dataset show that our models are very effective for cold start items recommendation.

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Calatrava, Arroyo Amanda. "High Performance Scientific Computing over Hybrid Cloud Platforms." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/75265.

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Scientific applications generally require large computational requirements, memory and data management for their execution. Such applications have traditionally used high-performance resources, such as shared memory supercomputers, clusters of PCs with distributed memory, or resources from Grid infrastructures on which the application needs to be adapted to run successfully. In recent years, the advent of virtualization techniques, together with the emergence of Cloud Computing, has caused a major shift in the way these applications are executed. However, the execution management of scientific applications on high performance elastic platforms is not a trivial task. In this doctoral thesis, Elastic Cloud Computing Cluster (EC3) has been developed. EC3 is an open-source tool able to execute high performance scientific applications by creating self-managed cost-efficient virtual hybrid elastic clusters on top of IaaS Clouds. These self-managed clusters have the capability to adapt the size of the cluster, i.e. the number of nodes, to the workload, thus creating the illusion of a real cluster without requiring an investment beyond the actual usage. They can be fully customized and migrated from one provider to another, in an automatically and transparent process for the users and jobs running in the cluster. EC3 can also deploy hybrid clusters across on-premises and public Cloud resources, where on-premises resources are supplemented with public Cloud resources to accelerate the execution process. Different instance types and the use of spot instances combined with on-demand resources are also cluster configurations supported by EC3. Moreover, using spot instances, together with checkpointing techniques, the tool can significantly reduce the total cost of executions while introducing automatic fault tolerance. EC3 is conceived to facilitate the use of virtual clusters to users, that might not have an extensive knowledge about these technologies, but they can benefit from them. Thus, the tool offers two different interfaces for its users, a web interface where EC3 is exposed as a service for non-experienced users and a powerful command line interface. Moreover, this thesis explores the field of light-weight virtualization using containers as an alternative to the traditional virtualization solution based on virtual machines. This study analyzes the suitable scenario for the use of containers and proposes an architecture for the deployment of elastic virtual clusters based on this technology. Finally, to demonstrate the functionality and advantages of the tools developed during this thesis, this document includes several use cases covering different scenarios and fields of knowledge, such as structural analysis of buildings, astrophysics or biodiversity.
Las aplicaciones científicas generalmente precisan grandes requisitos de cómputo, memoria y gestión de datos para su ejecución. Este tipo de aplicaciones tradicionalmente ha empleado recursos de altas prestaciones, como supercomputadores de memoria compartida, clústers de PCs de memoria distribuida, o recursos provenientes de infraestructuras Grid, sobre los que se adaptaba la aplicación para que se ejecutara satisfactoriamente. El auge que han tenido las técnicas de virtualización en los últimos años, propiciando la aparición de la computación en la nube (Cloud Computing), ha provocado un importante cambio en la forma de ejecutar este tipo de aplicaciones. Sin embargo, la gestión de la ejecución de aplicaciones científicas sobre plataformas de computación elásticas de altas prestaciones no es una tarea trivial. En esta tesis doctoral se ha desarrollado Elastic Cloud Computing Cluster (EC3), una herramienta de código abierto capaz de llevar a cabo la ejecución de aplicaciones científicas de altas prestaciones creando para ello clústers virtuales, híbridos y elásticos, autogestionados y eficientes en cuanto a costes, sobre plataformas Cloud de tipo Infraestructura como Servicio (IaaS). Estos clústers autogestionados tienen la capacidad de adaptar su tamaño, es decir, el número de nodos, a la carga de trabajo, creando así la ilusión de un clúster real sin requerir una inversión por encima del uso actual. Además, son completamente configurables y pueden ser migrados de un proveedor a otro de manera automática y transparente a los usuarios y trabajos en ejecución en el cluster. EC3 también permite desplegar clústers híbridos sobre recursos Cloud públicos y privados, donde los recursos privados son complementados con recursos Cloud públicos para acelerar el proceso de ejecución. Otras configuraciones híbridas, como el empleo de diferentes tipos de instancias y el uso de instancias puntuales combinado con instancias bajo demanda son también soportadas por EC3. Además, el uso de instancias puntuales junto con técnicas de checkpointing permite a EC3 reducir significantemente el coste total de las ejecuciones a la vez que proporciona tolerancia a fallos. EC3 está concebido para facilitar el uso de clústers virtuales a los usuarios, que, aunque no tengan un conocimiento extenso sobre este tipo de tecnologías, pueden beneficiarse fácilmente de ellas. Por ello, la herramienta ofrece dos interfaces diferentes a sus usuarios, una interfaz web donde se expone EC3 como servicio para usuarios no experimentados y una potente interfaz de línea de comandos. Además, esta tesis doctoral se adentra en el campo de la virtualización ligera, mediante el uso de contenedores como alternativa a la solución tradicional de virtualización basada en máquinas virtuales. Este estudio analiza el escenario propicio para el uso de contenedores y propone una arquitectura para el despliegue de clusters virtuales elásticos basados en esta tecnología. Finalmente, para demostrar la funcionalidad y ventajas de las herramientas desarrolladas durante esta tesis, esta memoria recoge varios casos de uso que abarcan diferentes escenarios y campos de conocimiento, como estudios estructurales de edificios, astrofísica o biodiversidad.
Les aplicacions científiques generalment precisen grans requisits de còmput, de memòria i de gestió de dades per a la seua execució. Este tipus d'aplicacions tradicionalment hi ha empleat recursos d'altes prestacions, com supercomputadors de memòria compartida, clústers de PCs de memòria distribuïda, o recursos provinents d'infraestructures Grid, sobre els quals s'adaptava l'aplicació perquè s'executara satisfactòriament. L'auge que han tingut les tècniques de virtualitzaciò en els últims anys, propiciant l'aparició de la computació en el núvol (Cloud Computing), ha provocat un important canvi en la forma d'executar este tipus d'aplicacions. No obstant això, la gestió de l'execució d'aplicacions científiques sobre plataformes de computació elàstiques d'altes prestacions no és una tasca trivial. En esta tesi doctoral s'ha desenvolupat Elastic Cloud Computing Cluster (EC3), una ferramenta de codi lliure capaç de dur a terme l'execució d'aplicacions científiques d'altes prestacions creant per a això clústers virtuals, híbrids i elàstics, autogestionats i eficients quant a costos, sobre plataformes Cloud de tipus Infraestructura com a Servici (IaaS). Estos clústers autogestionats tenen la capacitat d'adaptar la seua grandària, es dir, el nombre de nodes, a la càrrega de treball, creant així la il·lusió d'un cluster real sense requerir una inversió per damunt de l'ús actual. A més, són completament configurables i poden ser migrats d'un proveïdor a un altre de forma automàtica i transparent als usuaris i treballs en execució en el cluster. EC3 també permet desplegar clústers híbrids sobre recursos Cloud públics i privats, on els recursos privats són complementats amb recursos Cloud públics per a accelerar el procés d'execució. Altres configuracions híbrides, com l'us de diferents tipus d'instàncies i l'ús d'instàncies puntuals combinat amb instàncies baix demanda són també suportades per EC3. A més, l'ús d'instàncies puntuals junt amb tècniques de checkpointing permet a EC3 reduir significantment el cost total de les execucions al mateix temps que proporciona tolerància a fallades. EC3e stà concebut per a facilitar l'ús de clústers virtuals als usuaris, que, encara que no tinguen un coneixement extensiu sobre este tipus de tecnologies, poden beneficiar-se fàcilment d'elles. Per això, la ferramenta oferix dos interfícies diferents dels seus usuaris, una interfície web on s'exposa EC3 com a servici per a usuaris no experimentats i una potent interfície de línia d'ordres. A més, esta tesi doctoral s'endinsa en el camp de la virtualitzaciò lleugera, per mitjà de l'ús de contenidors com a alternativa a la solució tradicional de virtualitzaciò basada en màquines virtuals. Este estudi analitza l'escenari propici per a l'ús de contenidors i proposa una arquitectura per al desplegament de clusters virtuals elàstics basats en esta tecnologia. Finalment, per a demostrar la funcionalitat i avantatges de les ferramentes desenrotllades durant esta tesi, esta memòria arreplega diversos casos d'ús que comprenen diferents escenaris i camps de coneixement, com a estudis estructurals d'edificis, astrofísica o biodiversitat.
Calatrava Arroyo, A. (2016). High Performance Scientific Computing over Hybrid Cloud Platforms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/75265
TESIS

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Lukášová, Pavlína. "Cloud Computing jako nástroj BCM." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-75556.

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This thesis deals with possible interconnections between two concepts playing a big role in contemporary business and IT world. These concepts are Business Continuity Management and Cloud Computing. In the scope of this thesis there are certain areas identified where both concepts are complement, where Cloud Computing brings new opportunities for Business Continuity Management and where could possible problems arise during particular implementation. From the BCM perspective the impact lies on IT services, from the Cloud Computing perspective the thesis deals especially with security aspects. The thesis is also aimed at the characteristics of higher education and basic differences from commercial sphere. Based on defined differences and identified interconnections between BCM and Cloud Computing, the thesis argues for usage of suitable Cloud Computing solution for higher education regarding Business Continuity improvement. The multi-criterion comparison of several Infrastructure-as-a-Service solutions stems from this analysis focusing on technical, financial, and Business Continuity aspects. The result from this comparison together with conclusions from previous chapters serve as an input for subsequent practical proposal of Cloud Computing solution and its verification against Business Continuity improvement in specific conditions on University of Economics in Prague. The proposal is also represented by strategic map.

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Luo, Hao, and 罗浩. "Hybrid flowshop scheduling with job interdependences using evolutionary computing approaches." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B47849551.

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This research deals with production scheduling of manufacturing systems that predominantly consist of hybrid flowshops. Hybrid Flowshop Scheduling (HFS) problems are common in metal working industries. Their solution has significant inferences on company performance in a globally competitive market in terms of production cycle time, delivery dates, warehouse and work-in-process inventory management. HFS problems have attracted considerable research efforts on examining their scientific complexity and practical solution algorithms. In conventional HFS systems, an individual job goes through the flowshop with its own processing route, which has no influence on other jobs. However, in many metal working HFS systems, jobs have interdependent relationships during the process. This thesis focuses on addressing two classes of HFS problems with job interdependence that have been motivated by real-life industrial problems observed from our collaborating companies. The first class of HFS problems with job interdependence are faced by manufacturers of typically standard metal components where jobs are organized in families according to their machine settings and tools. Family setup times arise when a machine shifts from processing one job family to another. This problem is compounded by the challenges that the formation of job families is different in different stages and only a limited number of jobs can be processed within one setup. This class of problems is defined as HFS with family setup and inconsistent family formation. The second class of HFS problems with job interdependence is typically faced in a production process consisting of divergent operations where a single input item is converted into multiple output items. Two important challenges have been investigated. One is that one product can be produced following different process routes. The other is that the total inventory capacity is very limited in the company in the sense that the inventory spaces are commonly shared by raw materials, work-in-process items and finished products. This class of problems is defined as HFS with divergent production and common inventory. The aim is to analyze the general characteristics of HFS with job interdependence and develop effective and practical methodologies that can tackle real-world constraints and reduce the scheduling effort in daily production. This research has made the following contributions: (1) A V-A-X structural classification has been proposed to represent the divergent (V), convergent (A) and mixed (X) job interdependent relations during the production. (2) A genetic algorithm based approach and a particle swarm optimization based approach have been developed to solve two classes of HFS problems with job interdependence, respectively. The computational results based on actual production data have shown that the proposed solutions are robust, efficient and advantageous for solving the practical problems. (3) A waiting factor approach and delay timetable approach have been developed to extend the solutions space of two classes of HFS problems by inserting intentional idle times into original schedules. The computational results have indicated that better schedules can be obtained in the extended solution spaces.
published_or_final_version
Industrial and Manufacturing Systems Engineering
Doctoral
Doctor of Philosophy

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Roy, Kuntal. "Hybrid spintronics and straintronics: An ultra-low-energy computing paradigm." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/381.

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The primary obstacle to continued downscaling of charge-based electronic devices in accordance with Moore's law is the excessive energy dissipation that takes place in the device during switching of bits. Unlike charge-based devices, spin-based devices are switched by flipping spins without moving charge in space. Although some energy is still dissipated in flipping spins, it can be considerably less than the energy associated with current flow in charge-based devices. Unfortunately, this advantage will be squandered if the method adopted to switch the spin is so energy-inefficient that the energy dissipated in the switching circuit far exceeds the energy dissipated inside the system. Regrettably, this is often the case, e.g., switching spins with a magnetic field or with spin-transfer-torque mechanism. In this dissertation, it is shown theoretically that the magnetization of two-phase multiferroic single-domain nanomagnets can be switched very energy-efficiently, more so than any device currently extant, leading possibly to new magnetic logic and memory systems which might be an important contributor to Beyond-Moore's-Law technology. A multiferroic composite structure consists of a layer of piezoelectric material in intimate contact with a magnetostrictive layer. When a tiny voltage of few millivolts is applied across the structure, it generates strain in the piezoelectric layer and the strain is transferred to the magnetostrictive nanomagnet. This strain generates magnetostrictive anisotropy in the nanomagnet and thus rotates its direction of magnetization, resulting in magnetization reversal or 'bit-flip'. It is shown after detailed analysis that full 180 degree switching of magnetization can occur in the "symmetric" potential landscape of the magnetostrictive nanomagnet, even in the presence of room-temperature thermal fluctuations, which differs from the general perception on binary switching. With proper choice of materials, the energy dissipated in the bit-flip can be made as low as one attoJoule at room-temperature. Also, sub-nanosecond switching delay can be achieved so that the device is adequately fast for general-purpose computing. The above idea, explored in this dissertation, has the potential to produce an extremely low-power, yet high-density and high-speed, non-volatile magnetic logic and memory system. Such processors would be well suited for embedded applications, e.g., implantable medical devices that could run on energy harvested from the patient's body motion.

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Saenger, Pierre. "Optimisation et gestion d'énergie d'un système hybride électrique embarqué." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCD061/document.

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Les aéronefs « plus électriques » permettent de réduire la masse à embarquer, les émissions de gaz à effet de serre et la consommation de carburant. Leur développement nécessite d'adapter leur architecture électrique et leur capacité de production et de stockage d'énergie.Les systèmes de production et de stockage doivent être bien dimensionnés pour correspondre aux besoins énergétiques du véhicule. Une hybridation électrique entre un pack constitué de batteries lithium-ion et un pack de supercondensateurs peuvent répondre, respectivement, aux demandes en énergie et en puissance de la charge.Différentes architectures électriques permettant cette hybridation sont prises en compte. Ce travail de recherche traite du dimensionnement optimal de ces systèmes de stockage à bord d’un hélicoptère.Dans cette application particulière, la masse globale de l'ensemble du système de stockage doit être minimisé. Un outil de dimensionnement optimal est développé pour atteindre cet objectif en agissant sur la fréquence de coupure d’un filtre passe-bas. A partir du profil de mission le plus contraignant en énergie et en puissance demandées, cette approche fréquentielle permet la répartition de la demande de puissance entre nos deux systèmes de stockage et, donc, leur caractérisation.Les résultats d'optimisation obtenus par architecture électrique avec la méthode du recuit simulée, sont présentés et évalués dans toute la gamme de températures. Egalement, une adaptation de la stratégie de gestion de l'énergie est présentée pour tenir compte de l'influence de la température sur les performances de la batterie
The "more electric" aircrafts reduce the embedded weight, greenhouse gas emissions and fuel consumption. Their development requires to adapt their electrical architecture and their energy production and storage capacity.Production and storage systems must be well dimensioned to match thevehicle energy requirements. An electric hybridization integrating alithium-ion battery pack and a supercapacitor pack can respectively respond to the energy and power demands of the load.Different electrical hybridization architectures will be studied. This research project deals with the optimal designs of these storage systems on board a helicopter.In this particular application, the overall mass of the entire storagetank system must be minimized. An optimal sizing tool is developed toachieve this objective by acting on the cut-off frequency of alow-pass filter. This frequency approach, based on the most demanding mission profile in terms of energy and power, allows the allocation of power demand between our two storage systems and, therefore, their characterization.Optimization results obtained by electrical architecture using thesimulated annealing method are presented and evaluated over the entiretemperature range. An adaptation of the energy managementstrategy is also presented to evaluate the influence of temperature on battery performance

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Xing, Jin. "IHC3: an integrated hybrid cloud computing cyberinfrastructure for GIS/RS research." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107847.

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With the advancement of technologies, earth observation data could be obtained with finer spatial and spectral resolution. However, the increasing volume and complexity of those high resolution data presents new challenges in geographic information system (GIS) and remote sensing (RS) research, such as heterogeneous distributed data handling, efficient parallel data processing algorithms, and easy manageability of the underlying cyberinfrastructure, new collaboration model and lower computation costs. Geospatial cloud computing is leveraged in GIS/RS research to address the challenges of heterogeneous distributed data and its processing. Although the early experience has proven it is a great success to utilize cloud computing in GIS/RS research, the manageability of the cyberinfrastructure cannot be neglected. To be manageable, I argue that cloud computing must handle domain specific problems in GIS/RS, manage privacy of data, ease of use, and be inexpensive. In thesis I propose IHC3—integrated hybrid cloud computing cyberinfrastructure for advanced scalability and easy manageability in GIS/RS cyberinfrastructure research. IHC3 is designed to seamlessly integrate the computing resource of local hardware with public cloud providers, and it can dynamically adjust the boundary of private and public cloud with respect to the variable workload. A set of functionalities to simplify the image data processing, analysis, and visualization in GIS/RS research are also implemented in IHC3. I use MODIS data re-projection experiment with IHC3 to evaluate IHC3's performance, and compare the execution time and computation costs with single desktop, private cloud and Amazon EC2. The experiment proves that IHC3 is an effective platform for GIS/RS research, and it can offload the onerous system administration work from GIS/RS scientists, providing them with a tool for enhancing their research.
Avec l'avancement des technologies, des données d'observation de la terre pourrait être obtenu avec une résolution spatiale et spectrale plus fine. Toutefois, le volume et la complexité croissantes de ces données à haute résolution présente de nouveaux défis en matière de système d'information géographique (SIG) et télédétection (RS) de recherche, tels que la manipulation des données distribuées hétérogènes, parallèle efficace des algorithmes de traitement de données, la gestion facile des cyberinfrastructure sous-jacente, nouveau modèle de collaboration et de coût de calcul inférieur. Géospatiales cloud computing ont un effet de levier en matière de SIG / RS de recherche pour relever les défis de données hétérogènes distribués et son traitement. Bien que les premières expériences ont prouvé qu'il est un grand succès d'utiliser le cloud computing dans les SIG / RS recherche, la gestion de l'cyberinfrastructure ne peut pas être négligée. Pour être gérable, je soutiens que le cloud computing doit traiter les problèmes domaine spécifique en matière de SIG / RS, de gérer la confidentialité des données, la facilité d'utilisation, et d'être peu coûteux. Ainsi, dans cet article je propose IHC3 intégrée cyberinfrastructure hybrides de cloud computing pour l'évolutivité de pointe et maniabilité facile dans la recherche cyberinfrastructure SIG / RS. IHC3 s'intègre de façon transparente les ressources de calcul du local de matériel avec les fournisseurs de cloud public, et il pourrait ajuster dynamiquement la limite de cloud privé et public à l'égard de la charge de travail variable. Un ensemble de fonctionnalités pour simplifier le traitement des données d'image, l'analyse et la visualisation de SIG / RS de recherche sont également mis en œuvre dans IHC3. Je utilise des données MODIS re-projection expérimenter avec IHC3 pour évaluer la performance IHC3, et de comparer les temps d'exécution et les coûts de calcul avec le bureau unique, nuage privé et Amazon EC2. L'expérience prouve que IHC3 est une plateforme efficace pour la SIG / RS de recherche, et il peut décharger le travail du système onéreux administration de SIG / RS scientifiques, en leur fournissant un outil pour l'amélioration de leurs recherches.

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Hekail, Zeyad O. I. Al. "A hybrid method for computing the scattered fields from complex structures /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487760357824131.

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Spruth, Wilhelm G. "Enterprise Computing." Universitätsbibliothek Leipzig, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-126859.

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Das vorliegende Buch entstand aus einer zweisemestrigen Vorlesung „Enterprise Computing“, die wir gemeinsam über viele Jahre als Teil des Bachelor- oder Master-Studienganges an der Universität Leipzig gehalten haben. Das Buch führt ein in die Welt des Mainframe und soll dem Leser einen einführenden Überblick geben. Band 1 ist der Einführung in z/OS gewidmet, während sich Band 2 mit der Internet Integration beschäftigt. Ergänzend werden in Band 3 praktische Übungen unter z/OS dargestellt.

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Dittes, Andrew L. "A hybrid-parallel implementation of a strongly-correlated systems simulation." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1212174983.

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Smith, Michael Shawn. "Performance Analysis of Hybrid CPU/GPU Environments." PDXScholar, 2010. https://pdxscholar.library.pdx.edu/open_access_etds/300.

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We present two metrics to assist the performance analyst to gain a unified view of application performance in a hybrid environment: GPU Computation Percentage and GPU Load Balance. We analyze the metrics using a matrix multiplication benchmark suite and a real scientific application. We also extend an experiment management system to support GPU performance data and to calculate and store our GPU Computation Percentage and GPU Load Balance metrics.

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Bambini, Alberto. "Combining Active Learning and Mathematical Programming: a hybrid approach for Transprecision Computing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19664/.

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This paper explores the possibility of applying a hybrid approach between Active Learning and Mathematical Programming to Transprecision Computing. This would entail embedding a machine learning model trained by means of an Active Learning approach into an optimization model to automatically and intelligently tweak the representation of floating-point numerical data. This project aims to lower the energetic expenditure of every single intermediate computation in a given program, while also avoiding errors that are systematically introduced when manipulating variables using this technique, and ensure that they do not exceed a maximum acceptable error rate decided prior.

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Bai, Kang Jun. "Moving Toward Intelligence: A Hybrid Neural Computing Architecture for Machine Intelligence Applications." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103711.

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Rapid advances in machine learning have made information analysis more efficient than ever before. However, to extract valuable information from trillion bytes of data for learning and decision-making, general-purpose computing systems or cloud infrastructures are often deployed to train a large-scale neural network, resulting in a colossal amount of resources in use while themselves exposing other significant security issues. Among potential approaches, the neuromorphic architecture, which is not only amenable to low-cost implementation, but can also deployed with in-memory computing strategy, has been recognized as important methods to accelerate machine intelligence applications. In this dissertation, theoretical and practical properties of a hybrid neural computing architecture are introduced, which utilizes a dynamic reservoir having the short-term memory to enable the historical learning capability with the potential to classify non-separable functions. The hybrid neural computing architecture integrates both spatial and temporal processing structures, sidestepping the limitations introduced by the vanishing gradient. To be specific, this is made possible through four critical features: (i) a feature extractor built based upon the in-memory computing strategy, (ii) a high-dimensional mapping with the Mackey-Glass neural activation, (iii) a delay-dynamic system with historical learning capability, and (iv) a unique learning mechanism by only updating readout weights. To support the integration of neuromorphic architecture and deep learning strategies, the first generation of delay-feedback reservoir network has been successfully fabricated in 2017, better yet, the spatial-temporal hybrid neural network with an improved delay-feedback reservoir network has been successfully fabricated in 2020. To demonstrate the effectiveness and performance across diverse machine intelligence applications, the introduced network structures are evaluated through (i) time series prediction, (ii) image classification, (iii) speech recognition, (iv) modulation symbol detection, (v) radio fingerprint identification, and (vi) clinical disease identification.
Doctor of Philosophy
Deep learning strategies are the cutting-edge of artificial intelligence, in which the artificial neural networks are trained to extract key features or finding similarities from raw sensory information. This is made possible through multiple processing layers with a colossal amount of neurons, in a similar way to humans. Deep learning strategies run on von Neumann computers are deployed worldwide. However, in today's data-driven society, the use of general-purpose computing systems and cloud infrastructures can no longer offer a timely response while themselves exposing other significant security issues. Arose with the introduction of neuromorphic architecture, application-specific integrated circuit chips have paved the way for machine intelligence applications in recently years. The major contributions in this dissertation include designing and fabricating a new class of hybrid neural computing architecture and implementing various deep learning strategies to diverse machine intelligence applications. The resulting hybrid neural computing architecture offers an alternative solution to accelerate the neural computations required for sophisticated machine intelligence applications with a simple system-level design, and therefore, opening the door to low-power system-on-chip design for future intelligence computing, what is more, providing prominent design solutions and performance improvements for internet of things applications.

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Hwang, Suk Hyun. "Optimization of the Photovoltaic Time-series Analysis Process Through Hybrid Distributed Computing." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1587128073434884.

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Fashami, Mohammad Salehi. "MULTIFERROIC NANOMAGNETIC LOGIC: HYBRID SPINTRONICS-STRAINTRONIC PARADIGM FOR ULTRA-LOW ENERGY COMPUTING." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3520.

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Excessive energy dissipation in CMOS devices during switching is the primary threat to continued downscaling of computing devices in accordance with Moore’s law. In the quest for alternatives to traditional transistor based electronics, nanomagnet-based computing [1, 2] is emerging as an attractive alternative since: (i) nanomagnets are intrinsically more energy-efficient than transistors due to the correlated switching of spins [3], and (ii) unlike transistors, magnets have no leakage and hence have no standby power dissipation. However, large energy dissipation in the clocking circuit appears to be a barrier to the realization of ultra low power logic devices with such nanomagnets. To alleviate this issue, we propose the use of a hybrid spintronics-straintronics or straintronic nanomagnetic logic (SML) paradigm. This uses a piezoelectric layer elastically coupled to an elliptically shaped magnetostrictive nanomagnetic layer for both logic [4-6] and memory [7-8] and other information processing [9-10] applications that could potentially be 2-3 orders of magnitude more energy efficient than current CMOS based devices. This dissertation focuses on studying the feasibility, performance and reliability of such nanomagnetic logic circuits by simulating the nanoscale magnetization dynamics of dipole coupled nanomagnets clocked by stress. Specifically, the topics addressed are: 1. Theoretical study of multiferroic nanomagnetic arrays laid out in specific geometric patterns to implement a “logic wire” for unidirectional information propagation and a universal logic gate [4-6]. 2. Monte Carlo simulations of the magnetization trajectories in a simple system of dipole coupled nanomagnets and NAND gate described by the Landau-Lifshitz-Gilbert (LLG) equations simulated in the presence of random thermal noise to understand the dynamics switching error [11, 12] in such devices. 3. Arriving at a lower bound for energy dissipation as a function of switching error [13] for a practical nanomagnetic logic scheme. 4. Clocking of nanomagnetic logic with surface acoustic waves (SAW) to drastically decrease the lithographic burden needed to contact each multiferroic nanomagnet while maintaining pipelined information processing. 5. Nanomagnets with four (or higher states) implemented with shape engineering. Two types of magnet that encode four states: (i) diamond, and (ii) concave nanomagnets are studied for coherence of the switching process.

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Li, Hao. "Banking theory based distributed resource management and scheduling for hybrid cloud computing." Thesis, University of Huddersfield, 2013. http://eprints.hud.ac.uk/id/eprint/23544/.

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Cloud computing is a computing model in which the network offers a dynamically scalable service based on virtualized resources. The resources in the cloud environment are heterogeneous and geographically distributed. The user does not need to know how to manage those who support the cloud computing infrastructure. From the view of cloud computing, all hardware, software and networks are resources. All of the resources are dynamically scalable on demand. It can offer a complete service for the user even when these service resources are geographically distributed. The user pays for only what they use (pay-per-use). Meanwhile, the transaction environment will decide how to manage resource usage and cost, because all of the transactions have to follow the rule of the market. How to manage and schedule resources effectively becomes a very important part of cloud computing, and how to setup a new framework to offer a reliable, safe and executable service are very important issues. The approach herein is a new contribution to cloud computing. It not only proposes a hybrid cloud computing model based on banking theory to manage transactions among all participants in the hybrid cloud computing environment, but also proposes a "Cloud Bank" framework to support all the related issues. There are some of technology and theory been used to offer contributions as below: 1. This thesis presents an Optimal Deposit-loan Ratio Theory to adjust the pricing between the resource provider and resource consumer to realize both benefit maximization and cloud service optimization for all participants. 2. It also offers a new pricing schema using Centralized Synchronous Algorithm and Distributed Price Adjustment Algorithm to control all lifecycles and dynamically price all resources. 3. Normally, commercial banks apply four factors mitigation and to predict the risk: Probability of Default, Loss Given Default, Exposure at Default and Maturity. This thesis applies Probability of Default model of credit risk to forecast the safety supply of the resource. The Logistic Regression Model been used to control some factors in resource allocation. At the same time, the thesis uses Multivariate Statistical analysis to predict risk. 4. The Cloud Bank model applies an improved Pareto Optimality Algorithm to build its own scheduling system. 5. In order to archive the above purpose, this thesis proposes a new QoS-based SLA-CBSAL to describe all the physical resource and the processing of thread. In order to support all the related algorithms and theories, the thesis uses the CloudSim simulation tools give a test result to support some of the Cloud Bank management strategies and algorithms. The experiment shows us that the Cloud Bank Model is a new possible solution for hybrid cloud computing. For future research direction, the author will focus on building real hybrid cloud computing and simulate actual user behaviour in a real environment, and continue to modify and improve the feasibility and effectiveness of the project. For the risk mitigation and prediction, the risks can be divided into the four categories: credit risk, liquidity risk, operational risk, and other risks. Although this thesis uses credit risk and liquidity risk research, in a real trading environment operational risks and other risks exist. Only through improvements to the designation of all risk types of analysis and strategy can our Cloud Bank be considered relatively complete.

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Rudström, Åsa. "Co-Construction of Hybrid Spaces." Doctoral thesis, Stockholm University, Department of Computer and Systems Sciences (together with KTH), 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-748.

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When computational systems become increasingly mobile and ubiquitous, digital information and the use of computational systems may increasingly be immersed into the physical and social world of objects, people and practices. However, the digital, physical and social materials that make up these hybrid spaces have different characteristics and are hard to understand for users. In addition, users are themselves part in constructing and re-constructing the hybrid spaces.

The main question addressed in this thesis is whether making aspects of the digitally mediated hybrid spaces observable and accessible provides support to users. The observability may provide support for the specific task at hand or help in building an understanding for what the system does and how, an understanding that is needed to explain system output and to cope with service breakdowns. The fundament of the approach is to empower users of computational systems to actively make sense of the system themselves.

Two prototype services are described, Socifer and MobiTip. Their common denominator was to make digitally mediated parts of the hybrid spaces observable to users. Without disqualifying other kinds of information, the work focussed on digitally mediated social trails of other users.

Building on experience from the prototype work and an investigation into in seamful design, observability and awareness, I have investigated the effects of making a computational system’s social context observable to users in a way that

- is separated from the service’s main functionality in the interface, allowing it to become peripheral and non-obtrusive;

- uses simple models and little interpretation;

- to some extent opens up the service to allow for user appropriation of both service content and functionality; and

- is informative rather than proactive in order to empower the user rather than acting on the user’s behalf.

By designing systems that fulfil these criteria I claim that the user will be supported in performing the task at hand, with or without the service, and that with service use, the user will become more and more aware of the possibilities and limitations of the underlying technology. In addition, the digitally mediated hybrid spaces where physical, social and digital contexts meet constitute application domains in themselves, domains that users may enjoy exploring.

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Monteiro, André Frederico Guilhoto. "HPC management and engineering in the hybrid cloud." Doctoral thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/15737.

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Doutoramento em Informática
The evolution and maturation of Cloud Computing created an opportunity for the emergence of new Cloud applications. High-performance Computing, a complex problem solving class, arises as a new business consumer by taking advantage of the Cloud premises and leaving the expensive datacenter management and difficult grid development. Standing on an advanced maturing phase, today’s Cloud discarded many of its drawbacks, becoming more and more efficient and widespread. Performance enhancements, prices drops due to massification and customizable services on demand triggered an emphasized attention from other markets. HPC, regardless of being a very well established field, traditionally has a narrow frontier concerning its deployment and runs on dedicated datacenters or large grid computing. The problem with common placement is mainly the initial cost and the inability to fully use resources which not all research labs can afford. The main objective of this work was to investigate new technical solutions to allow the deployment of HPC applications on the Cloud, with particular emphasis on the private on-premise resources – the lower end of the chain which reduces costs. The work includes many experiments and analysis to identify obstacles and technology limitations. The feasibility of the objective was tested with new modeling, architecture and several applications migration. The final application integrates a simplified incorporation of both public and private Cloud resources, as well as HPC applications scheduling, deployment and management. It uses a well-defined user role strategy, based on federated authentication and a seamless procedure to daily usage with balanced low cost and performance.
O desenvolvimento e maturação da Computação em Nuvem abriu a janela de oportunidade para o surgimento de novas aplicações na Nuvem. A Computação de Alta Performance, uma classe dedicada à resolução de problemas complexos, surge como um novo consumidor no Mercado ao aproveitar as vantagens inerentes à Nuvem e deixando o dispendioso centro de computação tradicional e o difícil desenvolvimento em grelha. Situando-se num avançado estado de maturação, a Nuvem de hoje deixou para trás muitas das suas limitações, tornando-se cada vez mais eficiente e disseminada. Melhoramentos de performance, baixa de preços devido à massificação e serviços personalizados a pedido despoletaram uma atenção inusitada de outros mercados. A CAP, independentemente de ser uma área extremamente bem estabelecida, tradicionalmente tem uma fronteira estreita em relação à sua implementação. É executada em centros de computação dedicados ou computação em grelha de larga escala. O maior problema com o tipo de instalação habitual é o custo inicial e o não aproveitamento dos recursos a tempo inteiro, fator que nem todos os laboratórios de investigação conseguem suportar. O objetivo principal deste trabalho foi investigar novas soluções técnicas para permitir o lançamento de aplicações CAP na Nuvem, com particular ênfase nos recursos privados existentes, a parte peculiar e final da cadeia onde se pode reduzir custos. O trabalho inclui várias experiências e análises para identificar obstáculos e limitações tecnológicas. A viabilidade e praticabilidade do objetivo foi testada com inovação em modelos, arquitetura e migração de várias aplicações. A aplicação final integra uma agregação de recursos de Nuvens, públicas e privadas, assim como escalonamento, lançamento e gestão de aplicações CAP. É usada uma estratégia de perfil de utilizador baseada em autenticação federada, assim como procedimentos transparentes para a utilização diária com um equilibrado custo e performance.

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Peyrounette, Myriam. "Towards brain-scale modelling of the human cerebral blood flow : hybrid approach and high performance computing." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/19543/1/Peyrounette.pdf.

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The brain microcirculation plays a key role in cerebral physiology and neuronal activation. In the case of degenerative diseases such as Alzheimer’s, severe deterioration of the microvascular networks (e.g. vascular occlusions) limit blood flow, thus oxygen and nutrients supply, to the cortex, eventually resulting in neurons death. In addition to functional neuroimaging, modelling is a valuable tool to investigate the impact of structural variations of the microvasculature on blood flow and mass transfers. In the brain microcirculation, the capillary bed contains the smallest vessels (1-10 μm in diameter) and presents a mesh-like structure embedded in the cerebral tissue. This is the main place of molecular exchange between blood and neurons. The capillary bed is fed and drained by larger arteriolar and venular tree-like vessels (10-100 μm in diameter). For the last decades, standard network approaches have significantly advanced our understanding of blood flow, mass transport and regulation mechanisms in the human brain microcirculation. By averaging flow equations over the vascular cross-sections, such approaches yield a one-dimensional model that involves much fewer variables compared to a full three-dimensional resolution of the flow. However, because of the high density of capillaries, such approaches are still computationally limited to relatively small volumes (<100 mm3). This constraint prevents applications at clinically relevant scales, since standard imaging techniques only yield much larger volumes (∼100 cm3), with a resolution of 1-10 mm3. To get around this computational cost, we present a hybrid approach for blood flow modelling where the capillaries are replaced by a continuous medium. This substitution makes sense since the capillary bed is dense and space-filling over a cut-off length of ∼50 μm. In this continuum, blood flow is characterized by effective properties (e.g. permeability) at the scale of a much larger representative volume. Furthermore, the domain is discretized on a coarse grid using the finite volume method, inducing an important computational gain. The arteriolar and venular trees cannot be homogenized because of their quasi-fractal structure, thus the network approach is used to model blood flow in the larger vessels. The main difficulty of the hybrid approach is to develop a proper coupling model at the points where arteriolar or venular vessels are connected to the continuum. Indeed, high pressure gradients build up at capillary-scale in the vicinity of the coupling points, and must be properly described at the continuum-scale. Such multiscale coupling has never been discussed in the context of brain microcirculation. Taking inspiration from the Peaceman “well model” developed for petroleum engineering, our coupling model relies on to use analytical solutions of the pressure field in the neighbourhood of the coupling points. The resulting equations yield a single linear system to solve for both the network part and the continuum (strong coupling). The accuracy of the hybrid model is evaluated by comparison with a classical network approach, for both very simple synthetic architectures involving no more than two couplings, and more complex ones, with anatomical arteriolar and venular trees displaying a large number of couplings. We show that the present approach is very accurate, since relative pressure errors are lower than 6 %. This lays the goundwork for introducing additional levels of complexity in the future (e.g. non uniform hematocrit). In the perspective of large-scale simulations and extension to mass transport, the hybrid approach has been implemented in a C++ code designed for High Performance Computing. It has been fully parallelized using Message Passing Interface standards and specialized libraries (e.g. PETSc). Since the present work is part of a larger project involving several collaborators, special care has been taken in developing efficient coding strategies.

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Johnson, Buxton L. Sr. "HYBRID PARALLELIZATION OF THE NASA GEMINI ELECTROMAGNETIC MODELING TOOL." UKnowledge, 2017. http://uknowledge.uky.edu/ece_etds/99.

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Understanding, predicting, and controlling electromagnetic field interactions on and between complex RF platforms requires high fidelity computational electromagnetic (CEM) simulation. The primary CEM tool within NASA is GEMINI, an integral equation based method-of-moments (MoM) code for frequency domain electromagnetic modeling. However, GEMINI is currently limited in the size and complexity of problems that can be effectively handled. To extend GEMINI’S CEM capabilities beyond those currently available, primary research is devoted to integrating the MFDlib library developed at the University of Kentucky with GEMINI for efficient filling, factorization, and solution of large electromagnetic problems formulated using integral equation methods. A secondary research project involves the hybrid parallelization of GEMINI for the efficient speedup of the impedance matrix filling process. This thesis discusses the research, development, and testing of the secondary research project on the High Performance Computing DLX Linux supercomputer cluster. Initial testing of GEMINI’s existing MPI parallelization establishes the benchmark for speedup and reveals performance issues subsequently solved by the NASA CEM Lab. Implementation of hybrid parallelization incorporates GEMINI’s existing course level MPI parallelization with Open MP fine level parallel threading. Simple and nested Open MP threading are compared. Final testing documents the improvements realized by hybrid parallelization.

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Ribeiro, Tiago Filipe Rodrigues. "Developing and evaluating clopencl applications for heterogeneous clusters." Master's thesis, Instituto Politécnico de Bragança, Escola Superior de Tecnologia e Gestão, 2012. http://hdl.handle.net/10198/7948.

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In the last few years, the computing systems processing capabilities have increased significantly, changing from single-core to multi-core and even many-core systems. Accompanying this evolution, local networks have also become faster, with multi-gigabit technologies like Infiniband, Myrinet and 10G Ethernet. Parallel/distributed programming tools and standards, like POSIX Threads, OpenMP and MPI, have helped to explore these technologies and have been frequently combined, giving rise to Hybrid Programming Models. Recently, co-processors like GPUs and FPGAs, started to be used as accelerators, requiring specialized frameworks (like CUDA for NVIDIA GPUs). Presented with so much heterogeneity, the industry formulated the OpenCL specification, as a standard to explore heterogeneous systems. However, in the context of cluster computing, one problem surfaces: OpenCL only enables a developer to use the devices that are present in the local machine. With many processor devices scattered across cluster nodes (CPUs, GPUs and other co-processors), it then became important to enable software developers to take full advantage of the full cluster device set. This dissertation demonstrates and evaluates an OpenCL extension, named clOpenCL, which supports the simple deployment and efficient running of OpenCL-based parallel applications that may span several cluster nodes, thus expanding the original single-node OpenCL model. The main contributions are that clOpenCL i) offers a transparent approach to the porting of traditional OpenCL applications to cluster environments and ii) provides significant performance increases over classical (non-)hybrid parallel approaches. Nos últimos anos, a capacidade de processamento dos sistemas de computação aumentou significativamente, passando de CPUs com um núcleo para CPUs multi-núcleo. Acompanhando esta evolução, as redes locais também se tornaram mais rápidas, com tecnologias multi-gigabit como a Infiniband, Myrinet e 10G Ethernet. Ferramentas e standards paralelos/distribuídos, como POSIX Threads, OpenMP e MPI, ajudaram a explorar esses sistemas, e têm sido frequentemente combinados dando origem a Modelos de Programação Híbrida. Mais recentemente, co-processadores como GPUs e FPGAs, começaram a ser utilizados como aceleradores, exigindo frameworks especializadas (como o CUDA para GPUs NVIDIA). Deparada com tanta heterogeneidade, a indústria formulou a especificação OpenCL, como sendo um standard para exploração de sistemas heterogéneos. No entanto, no contexto da computação em cluster, um problema surge: o OpenCL só permite ao desenvolvedor utilizar dispositivos presentes na máquina local. Com tantos dispositivos de processamento espalhados pelos nós de um cluster (CPUs, GPUs e outros co-processadores), tornou-se assim importante habilitar os desenvolvedores de software, a tirarem o máximo proveito do conjunto total de dispositivos do cluster. Esta dissertação demonstra e avalia uma extensão OpenCL, chamada clOpenCL, que suporta a implementação simples e execução eficiente de aplicações paralelas baseadas em OpenCL que podem estender-se por vários nós do cluster, expandindo assim o modelo original de um único nó do OpenCL. As principais contribuições referem-se a que o clOpenCL i) oferece uma abordagem transparente à portabilidade de aplicações OpenCL tradicionais para ambientes cluster e ii) proporciona aumentos significativos de desempenho sobre abordagens paralelas clássicas (não) híbridas.

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Malík, Tomáš. "Adopce Cloud computing ve firemním sektoru." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-124676.

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This work is focused on the newly emerging field of Cloud computing and view of adoption of this technology in various industrial sectors. The first section explains the concept of cloud computing, its characteristics and individual models. The following part is a brief analysis of the size and development of the current market for cloud services. Next section focuses on the creation of industry categories acording to area of their business, together with their description. The actual categories are examined in part four, along with their expenditures on IT and state of Cloud's adoption, in addition with specific advantages and obstacles. Results from previous section are summarized in part five. In conclusion, the hypothesis is verified and the main findings summarized.

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Ostheimer, Julia. "Human-in-the-loop Computing : Design Principles for Machine Learning Algorithms of Hybrid Intelligence." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-94051.

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Artificial intelligence (AI) is revolutionizing contemporary industries and being applied in application domains ranging from recommendation systems to self-driving cars. In scenarios in which humans are interacting with an AI, inaccurate algorithms could lead to human mistreatment or even harmful events. Human-in-the-loop computing is a machine learning approach desiring hybrid intelligence, the combination of human and machine intelligence, to achieve accurate and interpretable results. This thesis applies human-in-the-loop computing in a Design Science Research project with a Swedish manufacturing company to make operational processes more efficient. The thesis aims to investigate emerging design principles useful for designing machine learning algorithms of hybrid intelligence. Hereby, the thesis has two key contributions: First, a theoretical framework is built that comprises general design knowledge originating from Information Systems (IS) research. Second, the analysis of empirical findings leads to the review of general IS design principles and to the formulation of useful design principles for human-in-the-loop computing. Whereas the principle of AI-readiness improves the likelihood of strategical AI success, the principle of hybrid intelligence shows how useful it can be to trigger a demand for human-in-the-loop computing in involved stakeholders. The principle of use case-marketing might help designers to promote the customer benefits of applying human-in-the-loop computing in a research setting. By utilizing the principle of power relationship and the principle of human-AI trust, designers can demonstrate the humans’ power over AI and build a trusting human-machine relationship. Future research is encouraged to extend and specify the formulated design principles and employ human-in-the-loop computing in different research settings. With regard to technological advancements in brain-machine interfaces, human-in-the-loop computing might even become much more critical in the future.

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Vepuri, Harish, and Mohsin Rahman. "IMPLICATIONS OF CLOUD COMPUTING IN IT ORGANIZATIONS." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-16989.

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Information technology (IT) in the recent decades has showcased immense evolution. This evolution has impacted not only the technological sector of the society but also the socio-political and economic sectors. One such archetype that has gained popular momentum in the recent years is cloud computing. Still in its developmental stages, cloud computing is the latest offspring of computing services and has invariably challenged the traditional aspects of computing services across the IT Organizations in the world. Thus, adopted by a number of developed as well as developing countries, cloud computing has played a crucial role in decreasing the overall cost pertinent to hardware, software and other services in organizations. However, issues such as data security and confidentiality, data availability, legal issues and unintelligent Service Level Agreements (SLAs) have questioned the reliability and credibility of cloud computing. This has resulted in lack of potential investments from major companies. The main aim of this research is to provide concrete reasons for the actively implementing cloud computing in IT Organizations. Qualitative approach and exploratory design are extensively used for data collection and analysis. Primary data is collected based on the views and opinions of IT experts and secondary data is collected from literary sources which invariably supports the primary data and maintains the credibility of the research.

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Wu, Lan. "Exploring Hybrid SPM-Cache Architectures to Improve Performance and Energy Efficiency for Real-time Computing." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3280.

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Real-time computing is not just fast computing but time-predictable computing. Many tasks in safety-critical embedded real-time systems have hard real-time characteristics. Failure to meet deadlines may result in the loss of life or in large damages. Known of Worst Case Execution Time (WCET) is important for reliability or correct functional behavior of the system. As multi-core processors are increasingly adopted in industry, it has become a great challenge to accurately bound the worst-case execution time (WCET) for real-time systems running on multi-core chips. This is particularly true because of the inter-thread interferences in accessing shared resources on multi-cores, such as shared L2 caches, which can significantly affect the performance but are very difficult to be estimate statically. We propose an approach to analyzing Worst Case Execution Time (WCET) for multi-core processors with shared L2 instruction caches by using a model checking based method. Our experiments indicate that compared to the static analysis technique based on extended ILP (Integer Linear Programming), our approach improves the tightness of WCET estimation more than 31.1% for the benchmarks we studied. However, due to the inherent complexity of multi-core timing analysis and the state explosion problem, the model checking based approach currently can only work with small real-time kernels for dual-core processors. At the same time, improving the average-case performance and energy efficiency has also been important for real-time systems. Recently, Hybrid SPM-Cache (HSC) architectures by combining caches and Scratch-Pad Memories (SPMs) have been increasingly used in commercial processors and research prototypes. Our research explores HSC architectures for real-time systems to reconcile time predictability, performance, and energy consumption. We study the energy dissipation of a number of hybrid on-chip memory architectures by combining both caches and Scratch-Pad Memories (SPM) without increasing the total on-chip memory size. Our experimental results indicate that with the equivalent total on-chip memory size, several hybrid SPM-Cache architectures are more energy-efficient than either pure software controlled SPMs or pure hardware-controlled caches. In particular, using the hybrid SPM-cache to store both instructions and data can achieve the best energy efficiency. However, the SPM allocation for the HSC architecture must be aware of the cache to harness the full potential of the HSC architecture. First, we propose and evaluate four SPM allocation strategies to reduce WCET for hybrid SPM-Caches with different complexities. These algorithms differ by whether or not they can cooperate with the cache or be aware of the WCET. Our evaluation shows that the cache aware and WCET-oriented SPM allocation can maximally reduce the WCET with minimum or even positive impact on the average-case execution time (ACET). Moreover, we explore four SPM allocation algorithms to maximize performance on the HSC architecture, including three heuristic-based algorithms, and an optimal algorithm based on model checking. Our experiments indicate that the Greedy Stack Distance based Allocation (GSDA) can run efficiently while achieving performance either the same as or close to the optimal results got by the Optimal Stack Distance based Allocation (OSDA). Last but not the least, we extend the two stack distance based allocation algorithms to GSDA-E and OSDA-E to minimize the energy consumption of the HSC architecture. Our experimental results show that the GSDA-E can also reduce the energy either the same as or close to the optimal results attained by the OSDA-E, while achieving performance close to the OSDA and GSDA.

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Mehdi, Malika. "PARALLEL HYBRID OPTIMIZATION METHODS FOR PERMUTATION BASED PROBLEMS." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00841962.

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La résolution efficace de problèmes d'optimisation a permutation de grande taille nécessite le développement de méthodes hybrides complexes combinant différentes classes d'algorithmes d'optimisation. L'hybridation des metaheuristiques avec les méthodes exactes arborescentes, tel que l'algorithme du branch-and-bound (B&B), engendre une nouvelle classe d'algorithmes plus efficace que ces deux classes de méthodes utilisées séparément. Le défi principal dans le développement de telles méthodes consiste a trouver des liens ou connections entre les stratégies de recherches divergentes utilisées dans les deux classes de méthodes. Les Algorithmes Genetiques (AGs) sont des metaheuristiques, a base de population, tr'es populaires bas'es sur des op'erateurs stochastiques inspirés de la théorie de l'évolution. Contrairement aux AGs et aux m'etaheuristiques généralement, les algorithmes de B&B sont basées sur l'énumération implicite de l'espace de recherche représente par le moyen d'un arbre, dit arbre de recherche. Notre approche d'hybridation consiste a définir un codage commun des solutions et de l'espace de recherche ainsi que des opérateurs de recherche ad'equats afin de permettre un couplage efficace de bas niveau entre les deux classes de méthodes AGs et B&B. La représentation de l'espace de recherche par le moyen d'arbres est traditionnellement utilis'ee dans les algorithmes de B&B. Dans cette thèse, cette représentation a été adaptée aux metaheuristiques. L'encodage des permutations au moyen de nombres naturels faisant référence a l'ordre d'énumération lexicographique des permutations dans l'arbre du B&B, est proposé comme une nouvelle manière de représenter l'espace de recherche des problèmes 'a permutations dans les metaheuristiques. Cette méthode de codage est basée sur les propriétés mathématiques des permutations, 'a savoir les codes de Lehmer et les tables d'inversions ainsi que les système d'énumération factoriels. Des fonctions de transformation permettant le passage entre les deux représentations (permutations et nombres) ainsi que des opérateurs de recherche adaptes au codage, sont définis pour les problèmes 'a permutations généralisés. Cette représentation, désormais commune aux metaheuristiques et aux algorithmes de B&B, nous a permis de concevoir des stratégies d'hybridation et de collaboration efficaces entre les AGs et le B&B. En effet, deux approches d'hybridation entre les AGs et les algorithmes de B&B (HGABB et COBBIGA) bas'es sur cette représentation commune ont été proposées dans cette thèse. Pour validation, une implémentation a été réalisée pour le problème d'affectation quadratique 'a trois dimension (Q3AP). Afin de résoudre de larges instances de ce problème, nous avons aussi propose une parallélisation pour les deux algorithme hybrides, basée sur des techniques de décomposition d'espace (décomposition par intervalle) utilisées auparavant pour la parallélisation des algorithmes de B&B. Du point de vue implémentation, afin de faciliter de futurs conceptions et implémentations de méthodes hybrides combinant metaheuristiques et méthodes exacte arborescentes, nous avons développe une plateforme d'hybridation intégrée au logiciel pour metaheuristiques, ParadisEO. La nouvelle plateforme a été utilisée pour réaliser des expérimentations intensives sur la grille de calcul Grid'5000.

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Turkedjiev, Emil. "Hybrid neural network analysis of short-term financial shares trading." Thesis, Northumbria University, 2017. http://nrl.northumbria.ac.uk/36122/.

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Recent advances in machine intelligence, particularly Artificial Neural Networks (ANNs) and Particle Swarm Optimisation (PSO), have introduced conceptually advanced technologies that can be utilised for financial market share trading analysis. The primary goal of the present research is to model short-term daily trading in Financial Times Stock Exchange 100 Index (FTSE 100) shares to make forecasts with certain levels of confidence and associated risk. The hypothesis to be tested is that financial shares time series contain significant non-linearity and that ANN, either separately or in conjunction with PSO, could be utilised effectively. Validation of the proposed model shows that nonlinear models are likely to be better choices than traditional linear regression for short-term trading. Some periodicity and trend lines were apparent in short- and long-term trading. Experiments showed that a model using an ANN with the Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) model features performed significantly better than analysis in the time domain. Mathematical analysis of the PSO algorithm from a systemic point of view along with stability analysis was performed to determine the choice of parameters, and a possible proportional, integral and derivative (PID) algorithm extension was recommended. The proposed extension was found to perform better than traditional PSO. Furthermore, a chaotic local search operator and exponentially varying inertia weight factor algorithm considering constraints were proposed that gave better ability to converge to a high quality solution without oscillations. A hybrid example combining an ANN with the PSO forecasting regression model significantly outperformed the original ANN and PSO approaches in accuracy and computational complexity. The evaluation of statistical confidence for the models gave good results, which is encouraging for further experimentation considering model cross-validation for generalisation to show how accurately the predictive models perform in practice.

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Wang, Kaibo. "Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU Hybrid Computing Environments." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1447685368.

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Massetto, Francisco Isidro. "Hybrid MPI - uma implementação MPI para ambientes distribuídos híbridos." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-08012008-100937/.

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O crescente desenvolvimento de aplicações de alto desempenho é uma realidade presente nos dias atuais. Entretanto, a diversidade de arquiteturas de máquinas, incluindo monoprocessadores e multiprocessadores, clusters com ou sem máquina front-end, variedade de sistemas operacionais e implementações da biblioteca MPI tem aumentado cada dia mais. Tendo em vista este cenário, bibliotecas que proporcionem a integração de diversas implementações MPI, sistemas operacionais e arquiteturas de máquinas são necessárias. Esta tese apresenta o HyMPI, uma implementação da biblioteca MPI voltada para integração, em um mesmo ambiente distribuído de alto desempenho, nós com diferentes arquiteturas, clusters com ou sem máquina front-end, sistemas operacionais e implementações MPI. HyMPI oferece um conjunto de primitivas compatíveis com a especificação MPI, incluindo comunicação ponto a ponto, operações coletivas, inicio e termino, além de outras primitivas utilitárias.
The increasing develpment of high performance applications is a reality on current days. However, the diversity of computer architectures, including mono and multiprocessor machines, clusters with or without front-end node, the variety of operating systems and MPI implementations has growth increasingly. Focused on this scenario, programming libraries that allows integration of several MPI implementations, operating systems and computer architectures are needed. This thesis introduces HyMPI, a MPI implementation aiming integratino, on a distributed high performance system nodes with different architectures, clusters with or without front-end machine, operating systems and MPI implementations. HyMPI offers a set of primitives based on MPI specification, including point-to-point communication, collective operations, startup and finalization and some other utility functions.

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Ali, Ayaz. "Analysis of key security and privacy concerns and viable solutions in Cloud computing." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-90806.

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Cloud Security and Privacy is the most concerned area in the development of newly advance technological domains like cloud computing, the cloud of things, the Internet of Things. However, with the growing popularity and diverse nature of these technologies, security and privacy are becoming intricate matters and affect the adoption of cloud computing. Many small and large enterprises are in conflict while migrating towards cloud technology and this is because of no proper cloud adoption policy guideline, generic solutions for system migration issues, systematic models to analyze security and privacy performance provided by different cloud models. Our proposed literature review focuses on the problems and identifies solutions in the category of security and privacy. A comprehensive analysis of various identified techniques published during 2010 – 2018 has been presented. We have reviewed 51 studies including research papers and systematic literature reviews on the factors of security and privacy. After analyzing, the papers have been classified into 5 major categories to get an appropriate solution for our required objectives of this study. This work will facilitate the researchers and as well the companies to select appropriate guideline while adopting cloud services.

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Alexandru, Mihai. "Efficient large electromagnetic simulation based on hybrid TLM and modal approach on grid computing and supercomputer." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2012. http://tel.archives-ouvertes.fr/tel-00797061.

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Dans le contexte des Sciences de l'Information et de la Technologie, un des challenges est de créer des systèmes de plus en plus petits embarquant de plus en plus d'intelligence au niveau matériel et logiciel avec des architectures communicantes de plus en plus complexes. Ceci nécessite des méthodologies robustes de conception afin de réduire le cycle de développement et la phase de prototypage. Ainsi, la conception et l'optimisation de la couche physique de communication est primordiale. La complexité de ces systèmes rend difficile leur optimisation notamment à cause de l'explosion du nombre des paramètres inconnus. Les méthodes et outils développés ces dernières années seront à terme inadéquats pour traiter les problèmes qui nous attendent. Par exemple, la propagation des ondes dans une cabine d'avion à partir des capteurs ou même d'une antenne, vers le poste de pilotage est grandement affectée par la présence de la structure métallique des sièges à l'intérieur de la cabine, voir les passagers. Il faut, donc, absolument prendre en compte cette perturbation pour prédire correctement le bilan de puissance entre l'antenne et un possible récepteur. Ces travaux de recherche portent sur les aspects théoriques et de mise en oeuvre pratique afin de proposer des outils informatiques pour le calcul rigoureux de la réflexion des champs électromagnétiques à l'intérieur de très grandes structures . Ce calcul implique la solution numérique de très grands systèmes inaccessibles par des ressources traditionnelles. La solution sera basée sur une grille de calcul et un supercalculateur. La modélisation électromagnétique des structures surdimensionnées par plusieurs méthodes numériques utilisant des nouvelles ressources informatiques, hardware et software, pour dérouler des calculs performants, représente le but de ce travail. La modélisation numérique est basée sur une approche hybride qui combine la méthode Transmission-Line Matrix (TLM) et l'approche modale. La TLM est appliquée aux volumes homogènes, tandis que l'approche modale est utilisée pour décrire les structures planaires complexes. Afin d'accélérer la simulation, une implémentation parallèle de l'algorithme TLM dans le contexte du paradigme de calcul distribué est proposé. Le sous-domaine de la structure qui est discrétisé avec la TLM est divisé en plusieurs parties appelées tâches, chacune étant calculée en parallèle par des processeurs différents. Pour accomplir le travail, les tâches communiquent entre elles au cours de la simulation par une librairie d'échange de messages. Une extension de l'approche modale avec plusieurs modes différents a été développée par l'augmentation de la complexité des structures planaires. Les résultats démontrent les avantages de la grille de calcul combinée avec l'approche hybride pour résoudre des grandes structures électriques, en faisant correspondre la taille du problème avec le nombre de ressources de calcul utilisées. L'étude met en évidence le rôle du schéma de parallélisation, cluster versus grille, par rapport à la taille du problème et à sa répartition. En outre, un modèle de prédiction a été développé pour déterminer les performances du calcul sur la grille, basé sur une approche hybride qui combine une prédiction issue d'un historique d'expériences avec une prédiction dérivée du profil de l'application. Les valeurs prédites sont en bon accord avec les valeurs mesurées. L'analyse des performances de simulation a permis d'extraire des règles pratiques pour l'estimation des ressources nécessaires pour un problème donné. En utilisant tous ces outils, la propagation du champ électromagnétique à l'intérieur d'une structure surdimensionnée complexe, telle qu'une cabine d'avion, a été effectuée sur la grille et également sur le supercalculateur. Les avantages et les inconvénients des deux environnements sont discutés.

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Sheen, Sean Kai. "Astro - A Low-Cost, Low-Power Cluster for CPU-GPU Hybrid Computing using the Jetson TK1." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1567.

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With the rising costs of large scale distributed systems many researchers have began looking at utilizing low power architectures for clusters. In this paper, we describe our Astro cluster, which consists of 46 NVIDIA Jetson TK1 nodes each equipped with an ARM Cortex A15 CPU, 192 core Kepler GPU, 2 GB of RAM, and 16 GB of flash storage. The cluster has a number of advantages when compared to conventional clusters including lower power usage, ambient cooling, shared memory between the CPU and GPU, and affordability. The cluster is built using commodity hardware and can be setup for relatively low costs while providing up to 190 single precision GFLOPS of computing power per node due to its combined GPU/CPU architecture. The cluster currently uses one 48-port Gigabit Ethernet switch and runs Linux for Tegra, a modified version of Ubuntu provided by NVIDIA as its operating system. Common file systems such as PVFS, Ceph, and NFS are supported by the cluster and benchmarks such as HPL, LAPACK, and LAMMPS are used to evaluate the system. At peak performance, the cluster is able to produce 328 GFLOPS of double precision and a peak of 810W using the LINPACK benchmark placing the cluster at 324th place on the Green500. Single precision benchmarks result in a peak performance of 6800 GFLOPs. The Astro cluster aims to be a proof-of-concept for future low power clusters that utilize a similar architecture. The cluster is installed with many of the same applications used by top supercomputers and is validated using the several standard supercomputing benchmarks. We show that with the rise of low-power CPUs and GPUs, and the need for lower server costs, this cluster provides insight into how ARM and CPU-GPU hybrid chips will perform in high-performance computing.

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Chen, Yung-Yu. "A Multi-Physics Software Framework on Hybrid Parallel Computing for High-Fidelity Solutions of Conservation Laws." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1313000975.

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39

Turnbull, Julian. "A novel approach to reduce the computation time for CFD : hybrid LES-RANS modelling on parallel computers." Thesis, Cranfield University, 2003. http://hdl.handle.net/1826/104.

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Large Eddy Simulation is a method of obtaining high accuracy computational results for modelling fluid flow. Unfortunately it is computationally expensive limiting it to users of large parallel machines. However, it may be that the use of LES leads to an over-resolution of the problem because the bulk of the computational domain could be adequately modelled using the Reynolds averaged approach. A study has been undertaken to assess the feasibility, both in accuracy and computational efficiency of using a parallel computer to solve both LES and RANS type turbulence models on the same domain for the problem flow over a circular cylinder at Reynolds number 3 900 To do this the domain has been created and then divided into two sub-domains, one for the LES model and one for the kappa - epsilon turbulence model. The hybrid model has been developed specifically for a parallel computing environment and the user is able to allocate modelling techniques to processors in a way which enables expansion of the model to any number of processors. Computational experimentation has shown that the combination of the Smagorinsky model can be used to capture the vortex shedding from the cylinder and the information successfully passed to the kappa - epsilon model for the dissipation of the vortices further downstream. The results have been compared to high accuracy LES results and with both kappa - epsilon and Smagorinsky LES computations on the same domain. The hybrid models developed compare well with the Smagorinsky model capturing the vortex shedding with the correct periodicity. Suggestions for future work have been made to develop this idea further, and to investigate the possibility of using the technology for the modelling of mixing and fast chemical reactions based on the more accurate prediction of the turbulence levels in the LES sub-domain.

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Khan, Zeeshan H. "Exploring Strategies that IT Leaders Use to Adopt Cloud Computing." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/3040.

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Information Technology (IT) leaders must leverage cloud computing to maintain competitive advantage. Evidence suggests that IT leaders who have leveraged cloud computing in small and medium sized organizations have saved an average of $1 million in IT services for their organizations. The purpose of this qualitative single case study was to explore strategies that IT leaders use to adopt cloud computing for their organizations. The target population consisted of 15 IT leaders who had experience with designing and deploying cloud computing solutions at their organization in Long Island, New York within the past 2 years. The conceptual framework of this research project was the disruptive innovation theory. Semistructured interviews were conducted and company documents were gathered. Data were inductively analyzed for emergent themes, then subjected to member checking to ensure the trustworthiness of findings. Four main themes emerged from the data: the essential elements for strategies to adopt cloud computing; most effective strategies; leadership essentials; and barriers, critical factors, and ineffective strategies affecting adoption of cloud computing. These findings may contribute to social change by providing insights to IT leaders in small and medium sized organizations to save money while gaining competitive advantage and ensure sustainable business growth that could enhance community standards of living.

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Polk, Nekerral. "Adoption of Cloud Computing Services in an Illinois-Based Insurance Company." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7058.

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The decision to adopt cloud computing services should involve business units of an insurance company as well as information technology (IT) because cloud computing services are viewed as both a technology offering and business alternative. The purpose of this qualitative exploratory case study was to explore the strategies used by IT architects of an Illinois-based insurance company when adopting cloud computing services. The theory supporting this study was the technology acceptance model. The study's population consisted of IT architects from an Illinois-based insurance company that have used strategies to adopt cloud computing services. This study's data collection included semistructured interviews and review of organizational documents. Member checking with each participant increased the validity of this study's findings. Four major themes emerged from this study: strategies to adopt cloud computing services, strategies to adopt cloud services models, strategies to adopt cloud computing models, and concerns affecting the strategies to adopt cloud computing services. The study findings may assist IT architects in developing effective strategies to adopt cloud computing services for their respective business unit. This study might serve as a foundation for positive social change by decreasing customer concerns regarding critical information being compromised when adopting cloud computing services.

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Tadonki, Claude. "High Performance Computing as a Combination of Machines and Methods and Programming." Habilitation à diriger des recherches, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00832930.

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High Performance Computing (HPC) aims at providing reasonably fast computing solutions to both scientific and real life technical problems. Many efforts have indeed been made on the way to powerful supercomputers, both generic and customized configurations. However, whatever their current and future breathtaking capabilities, supercomputers work by brute force and deterministic steps, while human mind works by few strokes of brilliance. Thus, in order to take a significant advantage of hardware advances, we need powerful methods to solve problems together with highly skillful programming efforts and relevant frameworks. The advent of multicore architectures is noteworthy in the HPC history, because it has brought the underlying concept of multiprocessing into common consideration and has changed the landscape of standard computing. At a larger scale, there is a keen desire to build or host frontline supercomputers. The yearly Top500 ranking nicely illustrates and orchestrates this supercomputers saga. For many years, computers have been falling in price while gaining processing power often strengthened by specialized accelerator units. We clearly see that what commonly springs up in mind when it comes to HPC is computer capability. However, this availability of increasingly fast computers has changed the rule of scientific discovery and has motivated the consideration of challenging applications. Thus, we are routinely at the door of large-scale problems, and most of time, the speed of calculation by itself is no longer sufficient. Indeed, the real concern of HPC users is the time-to-output. Thus, we need to study each important aspect in the critical path between inputs and outputs, and keep striving to reach the expected level of performance. This is the main concern of the viewpoints and the achievements reported in this book. The document is organized into five chapters articulated around our main contributions. The first chapter depicts the landscape of supercomputers, comments the need for tremendous processing speed, and analyze the main trends in supercomputing. The second chapter deals with solving large-scale combinatorial problems through a mixture of continuous and discrete optimization methods, we describe the main generic approaches and present an important framework on which we have been working so far. The third chapter is devoted to the topic accelerated computing, we discuss the motivations and the issues, and we describe three case studies from our contributions. In chapter four, we address the topic of energy minimization in a formal way and present our method based on a mathematical programming approach. Chapter five debates on hybrid supercomputing, we discuss technical issues with hierarchical shared memories and illustrate hybrid coding through a large-scale linear algebra implementation on a supercomputer.

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Hegde, Sridhar. "FUNCTIONAL ENHANCEMENT AND APPLICATIONS DEVELOPMENT FOR A HYBRID, HETEROGENEOUS SINGLE-CHIP MULTIPROCESSOR ARCHITECTURE." UKnowledge, 2004. http://uknowledge.uky.edu/gradschool_theses/252.

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Reconfigurable and dynamic computer architecture is an exciting area of research that is rapidly expanding to meet the requirements of compute intense real and non-real time applications in key areas such as cryptography, signal/radar processing and other areas. To meet the demands of such applications, a parallel single-chip heterogeneous Hybrid Data/Command Architecture (HDCA) has been proposed. This single-chip multiprocessor architecture system is reconfigurable at three levels: application, node and processor level. It is currently being developed and experimentally verified via a three phase prototyping process. A first phase prototype with very limited functionality has been developed. This initial prototype was used as a base to make further enhancements to improve functionality and performance resulting in a second phase virtual prototype, which is the subject of this thesis. In the work reported here, major contributions are in further enhancing the functionality of the system by adding additional processors, by making the system reconfigurable at the node level, by enhancing the ability of the system to fork to more than two processes and by designing some more complex real/non-real time applications which make use of and can be used to test and evaluate enhanced and new functionality added to the architecture. A working proof of concept of the architecture is achieved by Hardware Description Language (HDL) based development and use of a Virtual Prototype of the architecture. The Virtual Prototype was used to evaluate the architecture functionality and performance in executing several newly developed example applications. Recommendations are made to further improve the system functionality.

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Medhioub, Houssem. "Architectures et mécanismes de fédération dans les environnements cloud computing et cloud networking." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0009/document.

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Présenté dans la littérature comme une nouvelle technologie, le Cloud Computing est devenu incontournable dans la mise en place et la fourniture des services informatiques. Cette thèse s’inscrit dans le contexte de cette nouvelle technologie qui est en mesure de transformer la mise en place, la gestion et l’utilisation des systèmes d’information. L'adoption et la vulgarisation du Cloud ont été ralenties par la jeunesse même des concepts et l'hétérogénéité des solutions existantes. Cette difficulté d'adoption se manifeste par l'absence de standard, l'hétérogénéité des architectures et des API, le Vendor Lock-In imposé par les leaders du marché et des manques qui ralentissent la fédération. La motivation principale de la thèse est de simplifier l'adoption du cloud et la migration vers ses environnements et technologies. Notre objectif est de proposer des solutions d'interopérabilité et de fédération dans le Cloud. Le travail de recherche s’est aussi articulé autour de deux grands axes. Le premier concerne le rapprochement des réseaux du futur et des Clouds. Le deuxième axe concerne l'interopérabilité et la fédération entre solutions et services cloud. Une analyse de l’état de l’art sur le Cloud Computing et le Cloud Networking, a permis de confirmer des manques pressentis et de proposer deux architectures de fédération Cloud. La première architecture permet le rapprochement entre le Cloud Computing et le Cloud Networking. La seconde architecture facilite l'interopérabilité et le courtage de services Cloud. L'étude des deux architectures a fait ressortir deux composants primordiaux et essentiels pour assurer la fédération: une interface générique et un système d'échange de messages. Ces deux composants correspondent à deux contributions centrales de la thèse et reflètent l’ensemble des contributions (quatre au total) du travail de recherche
Presented in the literature as a new technology, Cloud Computing has become essential in the development and delivery of IT services. Given the innovative potential of Cloud, our thesis was conducted in the context of this promising technology. It was clear that the Cloud would change the way we develop, manage and use information systems. However, the adoption and popularization of Cloud were slow and difficult given the youth of the concepts and heterogeneity of the existing solutions. This difficulty in adoption is reflected by the lack of standard, the presence of heterogeneous architectures and APIs, the introduction of Vendor Lock-In imposed by the market leaders and the lack of cloud federation principles and facilitators. The main motivation for our PhD is to simplify the adoption of the cloud paradigm and the migration to cloud environments and technologies. Our goal has consequently been to improve interoperability and enable federation in the cloud. The thesis focused on two main areas. The first concerns the convergence of future networks and clouds and the second the improvement of federation and interoperability between heterogeneous cloud solutions and services. Based on our work in state of the art about Cloud Computing and Cloud Networking, we defined in this thesis two architectures for Cloud federation. The first architecture enables the merging (convergence) of Cloud Computing and Cloud Networking. The second architecture addresses interoperability between services and proposes cloud-brokering solutions. The study enabled the identification of two essential components for cloud federation, namely: a generic interface and a message exchange system. These two components have been two contributions of our thesis. The proposed federation architectures and these two components summarize the four major contributions of our work

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Siddiqui, Muhammad Anas. "Cloud Computing : Evaluation, as a platform for Scania Architecture." Thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-18896.

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Cloud computing has been given a great deal of attention during recent years. Almost all the technology market leaders and leading hosting service providers (like IBM, Microsoft and Verizon) have entered into the Cloud market as Cloud Providers. Cloud computing promises to provide highly available, secure, low cost, agile and highly scalable solution to the consumers. Scania is a global company and one of the world’s leading heavy vehicle manufacturers with 35,000+ employees. All the large organizations such as Scania, aim to constantly update themselves with the latest technology in order to meet their business requirements but, these organizations must always be convinced that there is a strong reason(s) to implement new technology. This research provides the method and criteria in relation to initiating Cloud computing. A number of Scania’s specific business requirements that it is possible to map to the Cloud are addressed in this thesis. The methodology of research is split in two parts. Firstly, the identification of business cases at Scania and their requirements with the Cloud and Secondly, the evaluation and comparison of the functionalities and capabilities of different vendors. The accumulated data is then compared and suitable vendors, according to those business requirements are suggested. This thesis also shares the experience of moving on premise applications to the Cloud. These are Scania specific applications which are currently being hosted in-house. The research also addresses the possibilities of portability between the Cloud providers. Although there is no standardization in relation to Cloud computing, some initiatives such as OpenStack are available and its current position and some application and data migration tools are also discussed. The thesis concludes with a general discussion, recommendations in relation to adapting Cloud computing and selecting the Cloud provider. This recommendation applies to every organization including Scania.

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46

Dab, Boutheina. "Optimization of routing and wireless resource allocation in hybrid data center networks." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1068/document.

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L’arrivée de la prochaine technologie 5G va permettre la connectivité des billions de terminaux mobiles et donc une énorme augmentation du trafic de données. A cet égard, les fournisseurs des services Cloud doivent posséder les infrastructures physiques capables de supporter cette explosion de trafic. Malheureusement, les architectures filaires conventionnelles des centres de données deviennent staturées et la congestion des équipements d’interconnexion est souvent atteinte. Dans cette thèse, nous explorons une approche récente qui consiste à augmenter le réseau filaire du centre de données avec l’infrastructure sans fil. En effet, nous exploitons une nouvelle technologie émergente, la technologie 60 GHz, qui assure un débit de l’ordre de 7 Gbits/s afin d’améliorer la QoS. Nous concevons une architecture hybride (filaire/sans fil) du réseau de centre de données basée sur : i) le modèle "Cisco’s Massively Scalable Data Center" (MSDC), et ii) le standard IEEE 802.11ad. Dans une telle architecture, les serveurs sont regroupés dans des racks, et sont interconnectés à travers un switch Ethernet, appelé top-of-rack (ToR) switch. Chaque ToR switch possède plusieurs antennes utilisées en parallèle sur différents canaux sans fil. L’objectif final consiste à minimiser la congestion du réseau filaire, en acheminant le maximum du trafic sur les canaux sans fil. Pour ce faire, cette thèse se focalise sur l’optimisation du routage et de l’allocation des canaux sans fil pour les communications inter-rack, au sein d’un centre de données hybride (HDCN). Ce problème étant NP-difficile, nous allons procéder en trois étapes. En premier lieu, on considère le cas des communications à un saut, où les racks sont placés dans le même rayon de transmission. Nous proposons un nouvel algorithme d’allocation des canaux sans fil dans les HDCN, qui permet d’acheminer le maximum des communications en sans-fil, tout en améliorant les performances réseau en termes de débit et délai. En second lieu, nous nous adressons au cas des communications à plusieurs sauts, où les racks ne sont pas dans le même rayon de transmission. Nous allons proposer une nouvelle approche optimale traitant conjointement le problème du routage et de l’allocation de canaux sans fils dans le HDCN, pour chaque communication, dans un mode online. En troisième étape, nous proposons un nouvel algorithme qui calcule conjointement le routage et l’allocation des canaux pour un ensemble des communications arrivant en mode batch (i.e., par lot). En utilisant le simulateur réseau QualNet, considérant toute la pile TCP/IP, les résultats obtenus montrent que nos propositions améliorent les performances comparées aux méthodes de l’état de l’art
The high proliferation of smart devices and online services allows billions of users to connect with network while deploying a vast range of applications. Particularly, with the advent of the future 5G technology, it is expected that a tremendous mobile and data traffic will be crossing Internet network. In this regard, Cloud service providers are urged to rethink their data center architectures in order to cope with this unprecedented traffic explosion. Unfortunately, the conventional wired infrastructures struggle to resist to such a traffic growth and become prone to serious congestion problems. Therefore, new innovative techniques are required. In this thesis, we investigate a recent promising approach that augments the wired Data Center Network (DCN) with wireless communications. Indeed, motivated by the feasibility of the new emerging 60 GHz technology, offering an impressive data rate (≈ 7 Gbps), we envision, a Hybrid (wireless/wired) DCN (HDCN) architecture. Our HDCN is based on i) Cisco’s Massively Scalable Data Center (MSDC) model and ii) IEEE 802.11ad standard. Servers in the HDCN are regrouped into racks, where each rack is equipped with a: i) Ethernet top-of-rack (ToR) switch and ii) set of wireless antennas. Our research aims to optimize the routing and the allocation of wireless resources for inter-rack communications in HDCN while enhancing network performance and minimizing congestion. The problem of routing and resource allocation in HDCN is NP-hard. To deal with this difficulty, we will tackle the problem into three stages. In the first stage, we consider only one-hop inter-rack communications in HDCN, where all communicating racks are in the same transmission range. We will propound a new wireless channel allocation approach in HDCN to hardness both wireless and wired interfaces for incoming flows while enhancing network throughput. In the second stage, we deal with the multi-hop communications in HDCN where communicating racks can not communicate in one single-hop wireless path. We propose a new approach to jointly route and allocate channels for each single communication flow, in an online way. Finally, in the third stage, we address the batched arrival of inter-rack communications to the HDCN so as to further optimize the usage of wireless and wired resources. For that end, we propose: i) a heuristic-based and ii) an approximate, solutions, to solve the joint batch routing and channel assignment. Based on extensive simulations conducted in QualNet simulator while considering the full protocol stack, the obtained results for both real workload and uniform traces, show that our proposals outperform the prominent related strategies

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Del, Piccolo Valentin. "Isolation réseau dans un environnement Cloud Public/Hybride." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066050/document.

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Le cloud computing est un modèle informatique donnant accès à un grand nombre de ressources de calcul et de stockage. Trois types de cloud existent, le cloud public, le cloud privé et le cloud hybride. Afin de proposer une solution cloud hybride, nous utilisons le protocole TRILL qui permet d'optimiser l'utilisation des ressources réseau d'une infrastructure. Cependant, TRILL ne permet pas d'interconnecter des data centers sans perdre l'indépendance de leur plan de contrôle. Pour modifier ce comportement, lequel implique la création d'un unique domaine de broadcast s'étendant sur tout le réseau, nous proposons, comme première contribution, une solution (MLTP) qui permet d'interconnecter des réseaux TRILL tout en les maintenant indépendants. Un autre élément manquant de TRILL est l'isolation des flux réseau. Notre seconde contribution consiste donc à trouver et implémenter une solution d'isolation des flux au sein de MLTP. Ce nouveau protocole (MLTP+VNT), permet d'avoir une solution de cloud hybride, mais elle possède deux désavantages. Le premier est la gestion des pannes. Certains éléments de MLTP+VNT, les Border RBridges (BRB), contiennent des informations nécessaires au routage inter-data center et lorsqu'ils tombent en panne, ces informations sont perdues. Pour éviter cela, nous avons, dans notre troisième contribution, modifié MLTP+VNT pour synchroniser les BRBs. Le second est l'obligation de n'utiliser que des réseaux MLTP+VNT pour réaliser un cloud hybride. Pour lever cette restriction, nous avons, dans notre quatrième contribution, conçu une passerelle entre un réseau TRILL, pour le cloud public, et un réseau OpenFlow, pour le cloud privé
Cloud computing uses infrastructure with a lot of computing and storage resources. There are three types of cloud: Public cloud, Private cloud, and Hybrid cloud. In order to provide a hybrid cloud solution, we used as a base the TRILL protocol which optimizes the use of the data center infrastructure. However, TRILL cannot interconnect data centers as doing so will merge the data centers networks and each data center will lose its independence. Our first contribution is to change this behavior and we develop MLTP which allows to interconnect TRILL or MLTP network without merging them. Another functionality missing from TRILL is network isolation. To fill this lack, in our second proposal we add to MLTP a solution called VNT and we then have a new protocol called MLTP+VNT. In this protocol, each user traffic is isolated from one another. Therefore, MLTP+VNT allows to have a hybrid cloud environment. Nevertheless, it has two shortcomings. The first one is its “single” point of failure. As a matter of fact, MLTP+VNT uses a new type of nodes called Border RBridges which contains inter-data centers routing information. If a Border RBridge fails, then the information it contained is lost. In order to prevent this loss, we implement a method to synchronize the Border RBridges in our third contribution. The second shortcoming is the obligation to use MLTP+VNT in each network to form the hybrid cloud. To lift this limitation, we design and develop, in our fourth contribution, a bridge between a MLTP+VNT network and an OpenFlow network. This way, our solution allows to create a hybrid cloud environment with the MLTP+VNT solution in the public cloud and OpenFlow in the public cloud

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Benin, Joseph Thomas. "Unified distribution of pseudonyms in hybrid ephemeral vehicular networks." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45910.

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This research devises a unified method for the distribution of pseudonyms in hybrid ephemeral vehicular networks (VNs), which are often referred to as vehicular ad hoc networks (VANETs), for the purposes of refill, intra-regional, and inter-regional movement. This work addresses a significant impediment to the use of pseudonyms, which has been almost universally accepted (and is on the verge of being standardized by the Institute for Electrical and Electronic Engineers (IEEE) and the Society for Automotive Engineers (SAE) as the best means to balance attribution and privacy to maximize the value of infrastructure deployment and citizen acceptability (i.e. use). The results include a pseudonym distribution protocol that maximizes ease of use while not compromising the security or privacy pseudonyms afford. These results contribute to the solution, in a scalable, adaptive, and bandwidth efficient manner, one of the remaining impediments to the adoption of VANETs. The new method shows improved performance compared to a baseline pseudonym distribution method that does not take these factors into consideration.

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Ediger, David. "Analyzing hybrid architectures for massively parallel graph analysis." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47659.

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The quantity of rich, semi-structured data generated by sensor networks, scientific simulation, business activity, and the Internet grows daily. The objective of this research is to investigate architectural requirements for emerging applications in massive graph analysis. Using emerging hybrid systems, we will map applications to architectures and close the loop between software and hardware design in this application space. Parallel algorithms and specialized machine architectures are necessary to handle the immense size and rate of change of today's graph data. To highlight the impact of this work, we describe a number of relevant application areas ranging from biology to business and cybersecurity. With several proposed architectures for massively parallel graph analysis, we investigate the interplay of hardware, algorithm, data, and programming model through real-world experiments and simulations. We demonstrate techniques for obtaining parallel scaling on multithreaded systems using graph algorithms that are orders of magnitude faster and larger than the state of the art. The outcome of this work is a proposed hybrid architecture for massive-scale analytics that leverages key aspects of data-parallel and highly multithreaded systems. In simulations, the hybrid systems incorporating a mix of multithreaded, shared memory systems and solid state disks performed up to twice as fast as either homogeneous system alone on graphs with as many as 18 trillion edges.

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Öhberg, Tomas. "Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU." Thesis, Linköpings universitet, Programvara och system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149605.

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The trend in computer architectures has for several years been heterogeneous systems consisting of a regular CPU and at least one additional, specialized processing unit, such as a GPU.The different characteristics of the processing units and the requirement of multiple tools and programming languages makes programming of such systems a challenging task. Although there exist tools for programming each processing unit, utilizing the full potential of a heterogeneous computer still requires specialized implementations involving multiple frameworks and hand-tuning of parameters.To fully exploit the performance of heterogeneous systems for a single computation, hybrid execution is needed, i.e. execution where the workload is distributed between multiple, heterogeneous processing units, working simultaneously on the computation. This thesis presents the implementation of a new hybrid execution backend in the algorithmic skeleton framework SkePU. The skeleton framework already gives programmers a user-friendly interface to algorithmic templates, executable on different hardware using OpenMP, CUDA and OpenCL. With this extension it is now also possible to divide the computational work of the skeletons between multiple processing units, such as between a CPU and a GPU. The results show an improvement in execution time with the hybrid execution implementation for all skeletons in SkePU. It is also shown that the new implementation results in a lower and more predictable execution time compared to a dynamic scheduling approach based on an earlier implementation of hybrid execution in SkePU.

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Dissertations / Theses on the topic 'Hybrid Computing'

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