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Bhardwaj, Divya Anshu. "Inverse Discrete Cosine Transform by Bit Parallel Implementation and Power Comparision." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2447.
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The goal of this project was to implement and compare Invere Discrete Cosine Transform using three methods i.e. by bit parallel, digit serial and bit serial. This application describes a one dimensional Discrete Cosine Transform by bit prallel method and has been implemented by 0.35 ìm technology. When implementing a design, there are several considerations like word length etc. were taken into account. The code was implemented using WHDL and some of the calculations were done in MATLAB. The VHDL code was the synthesized using Design Analyzer of Synopsis; power was calculated and the results were compared.
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Wei, Lan. "Implementation of Pipelined Bit-parallel Adders." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1943.
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Bit-parallel addition can be performed using a number of adder structures with different area and latency. However, the power consumption of different adder structures is not well studied. Further, the effect of pipelining adders to increase the throughput is not well studied. In this thesis four different adders are described, implemented in VHDL and compared after synthesis. The results give a general idea of the time-delay-power tradeoffs between the adder structures. Pipelining is shown to be a good technique for increasing the circuit speed.
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Vasata, Darlon. "Ferramenta de programação e processamento para execução de aplicações com grandes quantidades de dados em ambientes distribuídos." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-14122018-074952/.
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A temática envolvendo o processamento de grandes quantidades de dados é um tema amplamente discutido nos tempos atuais, envolvendo seus desafios e aplicabilidade. Neste trabalho é proposta uma ferramenta de programação para desenvolvimento e um ambiente de execução para aplicações com grandes quantidades de dados. O uso da ferramenta visa obter melhor desempenho de aplicações neste cenário, explorando o uso de recursos físicos como múltiplas linhas de execução em processadores com diversos núcleos e a programação distribuída, que utiliza múltiplos computadores interligados por uma rede de comunicação, de forma que estes operam conjuntamente em uma mesma aplicação, dividindo entre tais máquinas sua carga de processamento. A ferramenta proposta consiste na utilização de blocos de programação, de forma que tais blocos sejam compostos por tarefas, e sejam executados utilizando o modelo produtor consumidor, seguindo um fluxo de execução definido. A utilização da ferramenta permite que a divisão das tarefas entre as máquinas seja transparente ao usuário. Com a ferramenta, diversas funcionalidades podem ser utilizadas, como o uso de ciclos no fluxo de execução ou no adiantamento de tarefas, utilizando a estratégia de processamento especulativo. Os resultados do trabalho foram comparados a duas outras ferramentas de processamento de grandes quantidades de dados, Hadoop e que o uso da ferramenta proporciona aumento no desempenho das aplicações, principalmente quando executado em clusters homogêneos.The topic involving the processing of large amounts of data is widely discussed subject currently, about its challenges and applicability. This work proposes a programming tool for development and an execution environment for applications with large amounts of data. The use of the tool aims to achieve better performance of applications in this scenario, exploring the use of physical resources such as multiple lines of execution in multi-core processors and distributed programming, which uses multiple computers interconnected by a communication network, so that they operate jointly in the same application, dividing such processing among such machines. The proposed tool consists of the use of programming blocks, so that these blocks are composed of tasks, and the blocks are executed using the producer consumer model, following an execution flow. The use of the tool allows the division of tasks between the machines to be transparent to the user. With the tool, several functionalities can be used, such as cycles in the execution flow or task advancing using the strategy of speculative processing. The results were compared with two other frameworks, Hadoop and Spark. These results indicate that the use of the tool provides an increase in the performance of the applications, mostly when executed in homogeneous clusters.
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Cojocaru, Christian Carleton University Dissertation Engineering Electronics. "Computational RAM: implementation and bit-parallel architecture." Ottawa, 1995.
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Åslund, Anders. "Power Estimation of High Speed Bit-Parallel Adders." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2390.
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Fast addition is essential in many DSP algorithms. Various structures have been introduced to speed up the time critical carry propagation. For high throughput applications, however, it may be necessary to introduce pipelining. In this report the power consumption of four different adder structures, with varying word length and different number of pipeline cuts, is compared.
Out of the four adder structures compared, the Kogge-Stone parallel prefix adder proves to be the best choice most of the time. The Brent-Kung parallel prefix adder is also a good choice, but the maximal throughput does not reach as high as the maximal throughput of the Kogge-Stone parallel prefix adder.
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Bolotski, Michael 1965. "Abacus--a reconfigurable bit-parallel architecture for early vision." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10638.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 123-126).
by Michael Bolotski.
Ph.D.
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Bolotski, Michael. "Distributed bit-parallel architecture and algorithms for early vision." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29462.
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A new form of parallelism, distributed bit-parallelism, is introduced. A distributed bit-parallel organization distributes each bit of a data item to a different processor. Bit-parallelism allows computation that is sub-linear with word size for such operations as integer addition, arithmetic shifts, and data moves. The implications of bit-parallelism for system architecture are analyzed. An implementation of a bit-parallel architecture based on a mesh with bypass network is presented. The performance of bit-parallel algorithms on this architecture is analyzed and found to be several times faster than bit-serial algorithms. The application of the architecture to low level vision algorithms is discussed.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Le, Chin Aik. "An 8-bit inner product multiplier by parallel pipeline algorithm." Ohio : Ohio University, 1988. http://www.ohiolink.edu/etd/view.cgi?ohiou1182863777.
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Rising, Barry John Paul. "Hardware architectures for stochastic bit-stream neural networks : design and implementation." Thesis, Royal Holloway, University of London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326219.
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Shah, Milap. "Parallel Aes diffusion inter block diffusion at bit level and compression." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42449.
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Information is an intelligent data through which knowledgeable and usable things can be convicted or interpreted in a proper manner. With the advancement of technology, transmission of information over the network has come a trend. This information must be transmitted securely over the network. Data security was not a problem if a secure channel was provided for single transmission. It is a necessity to convert the information into an unintelligible form for transmitting it over an unsecured channel. Encryption is a technique through which original information can be converted into unintelligible form. As time has elapsed, various encryption algorithms are employed so that information can be transmitted securely over an unsecured channel. Unless an intruder accesses the encrypted text, he / she cannot gain any information from that text. But as the new algorithms are designed, all the algorithms are challenged and their cryptanalysis is available. In the year 1998, Advanced Encryption Standards (A (S)) were proposed and later it was widely accepted as the most secure encryption algorithm that can be used to encrypt the information so that it can be transmitted securely and unsecured. fixed to a new scheme called Parallel AЕS, was an employee who takes four blocks of 16 bytes at a time to generate four blocks of 16 bytes of text thus providing diffusion of blocks at exchange. than all sequential AЕs. All the algorithms are challenged and their cryptanalysis is available. In the year 1998, To make A morS more fixed to a new scheme called Parallel AЕS, was an employee who took four blocks of 16 bytes at a time to generate four blocks of 16 bytes of text, thus providing diffusion of blocks at exchange. By doing this parallel A stoodS stood to be much firmer than sequential AЕS. Advanced Encryption Standards (AЕS) was proposed and later it was widely accepted as the most secure encryption algorithm that can be used to encrypt the information so that it can be transmitted securely over an unsecured channel. To make A morS more fixed to a new scheme called Parallel AЕS, was an employee who took four blocks of 16 bytes at a time to generate four blocks of 16 bytes of text, thus providing diffusion of blocks at exchange. By doing this parallel A stoodS stood to be much firmer than sequential AЕS. Advanced Encryption Standards (AЕS) was proposed and later it was widely accepted as the most secure encryption algorithm that can be used to encrypt the information so that it can be transmitted securely over an unsecured channel. To make A morS more fixed to a new scheme called Parallel AЕS, was an employee who took four blocks of 16 bytes at a time to generate four blocks of 16 bytes of text, thus providing diffusion of blocks at exchange. By doing this parallel A stoodS stood to be much firmer than sequential AЕS. was an employee who took four blocks of 16 bytes at a time to generate four blocks of 16 bytes of text, thus providing diffusion of blocks at exchange. By doing this parallel A stoodS stood to be much firmer than sequential AЕS. was an employee who took four blocks of 16 bytes at a time to generate four blocks of 16 bytes of text, thus providing diffusion of blocks at exchange. By doing this parallel A stoodS stood to be much firmer than sequential AЕS.
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Rampaigul, I. "Parallel processing for digital transmission : Novel structures for high bit rate terminals." Thesis, University of Essex, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.354009.
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Korndorfer, Jonas Henrique Muller. "High performance trace replay event simulation of parallel programs behavior." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/149310.
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Sistemas modernos de alto desempenho compreendem milhares a milhões de unidades de processamento. O desenvolvimento de uma aplicação paralela escalável para tais sistemas depende de um mapeamento preciso da utilização recursos disponíveis. A identificação de recursos não utilizados e os gargalos de processamento requere uma boa análise desempenho. A observação de rastros de execução é uma das técnicas mais úteis para esse fim. Infelizmente, o rastreamento muitas vezes produz grandes arquivos de rastro, atingindo facilmente gigabytes de dados brutos. Portanto ferramentas para análise de desempenho baseadas em rastros precisam processar esses dados para uma forma legível e serem eficientes a fim de permitirem uma análise rápida e útil. A maioria das ferramentas existentes, tais como Vampir, Scalasca e TAU, focam no processamento de formatos de rastro com semântica associada, geralmente definidos para lidar com programas desenvolvidos com bibliotecas populares como OpenMP, MPI e CUDA. No entanto, nem todas aplicações paralelas utilizam essas bibliotecas e assim, algumas vezes, essas ferramentas podem não ser úteis. Felizmente existem outras ferramentas que apresentam uma abordagem mais dinâmica, utilizando um formato de arquivo de rastro aberto e sem semântica específica. Algumas dessas ferramentas são Paraver, Pajé e PajeNG. Por outro lado, ser genérico tem custo e assim tais ferramentas frequentemente apresentam baixo desempenho para o processamento de grandes rastros. O objetivo deste trabalho é apresentar otimizações feitas para o conjunto de ferramentas PajeNG. São apresentados o desenvolvimento de um estratégia de paralelização para o PajeNG e uma análise de desempenho para demonstrar nossos ganhos. O PajeNG original funciona sequencialmente, processando um único arquivo de rastro que contém todos os dados do programa rastreado. Desta forma, a escalabilidade da ferramenta fica muito limitada pela leitura dos dados. Nossa estratégia divide o arquivo em pedaços permitindo seu processamento em paralelo. O método desenvolvido para separar os rastros permite que cada pedaço execute em um fluxo de execução separado. Nossos experimentos foram executados em máquinas com acesso não uniforme à memória (NUMA).Aanálise de desempenho desenvolvida considera vários aspectos como localidade das threads, o número de fluxos, tipo de disco e também comparações entre os nós NUMA. Os resultados obtidos são muito promissores, escalando o PajeNG cerca de oito a onze vezes, dependendo da máquina.Modern high performance systems comprise thousands to millions of processing units. The development of a scalable parallel application for such systems depends on an accurate mapping of application processes on top of available resources. The identification of unused resources and potential processing bottlenecks requires good performance analysis. The trace-based observation of a parallel program execution is one of the most helpful techniques for such purpose. Unfortunately, tracing often produces large trace files, easily reaching the order of gigabytes of raw data. Therefore tracebased performance analysis tools have to process such data to a human readable way and also should be efficient to allow an useful analysis. Most of the existing tools such as Vampir, Scalasca, TAU have focus on the processing of trace formats with a fixed and well-defined semantic. The corresponding file format are usually proposed to handle applications developed using popular libraries like OpenMP, MPI, and CUDA. However, not all parallel applications use such libraries and so, sometimes, these tools cannot be useful. Fortunately, there are other tools that present a more dynamic approach by using an open trace file format without specific semantic. Some of these tools are the Paraver, Pajé and PajeNG. However the fact of being generic comes with a cost. These tools very frequently present low performance for the processing of large traces. The objective of this work is to present performance optimizations made in the PajeNG tool-set. This comprises the development of a parallelization strategy and a performance analysis to set our gains. The original PajeNG works sequentially by processing a single trace file with all data from the observed application. This way, the scalability of the tool is very limited by the reading of the trace file. Our strategy splits such file to process several pieces in parallel. The created method to split the traces allows the processing of each piece in each thread. The experiments were executed in non-uniform memory access (NUMA) machines. The performance analysis considers several aspects like threads locality, number of flows, disk type and also comparisons between the NUMA nodes. The obtained results are very promising, scaling up the PajeNG about eight to eleven times depending on the machine.
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Chan, C. F. "Low bit-rate speech coding : A parallel processing approach using digital signal processors." Thesis, University of Essex, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375652.
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Santos, Carlos Henrique da Silva. "Computação bio-inspirada e paralela para a analise de estruturas metamateriais em microondas e fotonica." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260809.
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Orientador: Hugo Enrique Hernandez FigueroaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-15T19:32:10Z (GMT). No. of bitstreams: 1 Santos_CarlosHenriquedaSilva_D.pdf: 6145760 bytes, checksum: b129064af29e8b7052b13e0b422b299c (MD5) Previous issue date: 2010
Resumo: Esse trabalho tem por objetivo desenvolver algoritmos bio-inspirados (Algoritmo Genético, Estratégia Evolutiva e Sistema Imunológico Artificial) eficientes (tempo de processamento e convergência), e flexíveis (multi-plataforma), para a otimização de dispositivos eletromagnéticos nos domínios da frequência e do tempo. Além disso, para satisfazer a demanda computacional requerida nessas otimizações, foram implementadas versões paralelas dos algoritmos bio-inspirados e do método das diferenças finitas no domínio tempo em três dimensões (FDTD 3D) para serem executados em cluster de computadores. A eficiência e flexibilidade dos algoritmos desenvolvidos foram confirmadas através da aplicação criteriosa de testes de desempenho disponíveis na literatura. Isto permitiu a proposta e simulação de novos e complexos dispositivos eletromagnéticos baseados em estruturas metamateriais
Abstract: The objectives of this work are to develop efficient (runtime and convergence) and flexible (multiplatform) bio-inspired algorithms (Genetic Algorithm, Evolution Strategy and Artificial Immune System), for the optimization of electromagnetic devices either in frequency and time domain. In addition, to satisfy the computational requirements for these optimizations a parallel version of these bio-inspired algorithms and a three-dimensional finite difference time domain (3D FDTD) were developed capable to be processed in cluster computers. The successful performance of these parallel resources was confirmed by their careful assessment through key tests available in the specialized literature. This permitted the proposal and simulation of novel and complex electromagnetic devices based on metamaterial structures
Doutorado
Telecomunicações e Telemática
Doutor em Engenharia Elétrica
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Hansson, Karakoca Josef. "Big Data Types : Internally Parallel in an Actor Language." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372248.
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Around year 2005 the hardware industry hit a power wall. It was no longer possible to drastically increasing computer performance through decreasing the transistors' size or increasing the clock-speed of the CPU. To ensure future development multi-core processors became the way to go. The Programming Languages Group at Uppsala University is developing a programming language called Encore that is developed to be scalable to future machines with a few hundred or even thousand processor cores. This thesis reports on the design and implementation of Big data types. Big data types are locally distributed data structures that allow internal parallelism in the actor model by using several actors in their implementations. Thus, rather than serializing all interaction these data structures are potentially as parallel as the number of actors used to construct them. The goal of Big data types is to provide a tool that makes it easier for an Encore programmer to create parallel and concurrent programs. As part of our evaluation, we have implemented a Mapreduce framework which showcase how of Big data types could be used in a more complex program.
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練偉森 and Wai-sum Lin. "Adaptive parallel rendering." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31221415.
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Ma, Chao. "Implementing Graph Based Parallel Computation of Big Taxi Trajectory Data." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1442683650.
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Xu, Lei. "Cellular distributed and parallel computing." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:88ffe124-c2fd-4144-86fe-47b35f4908bd.
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This thesis focuses on novel approaches to distributed and parallel computing that are inspired by the mechanism and functioning of biological cells. We refer to this concept as cellular distributed and parallel computing which focuses on three important principles: simplicity, parallelism, and locality. We first give a parallel polynomial-time solution to the constraint satisfaction problem (CSP) based on a theoretical model of cellular distributed and parallel computing, which is known as neural-like P systems (or neural-like membrane systems). We then design a class of simple neural-like P systems to solve the fundamental maximal independent set (MIS) selection problem efficiently in a distributed way, by drawing inspiration from the way that developing cells in the fruit fly become specialised. Building on the novel bio-inspired approach to distributed MIS selection, we propose a new simple randomised algorithm for another fundamental distributed computing problem: the distributed greedy colouring (GC) problem. We then propose an improved distributed MIS selection algorithm that incorporates for the first time another important feature of the biological system: adapting the probabilities used at each node based on local feedback from neighbouring nodes. The improved distributed MIS selection algorithm is again extended to solve the distributed greedy colouring problem. Both improved algorithms are simple and robust and work under very restrictive conditions, moreover, they both achieve state-of-the-art performance in terms of their worst-case time complexity and message complexity. Given any n-node graph with maximum degree Delta, the expected time complexity of our improved distributed MIS selection algorithm is O(log n) and the message complexity per node is O(1). The expected time complexity of our improved distributed greedy colouring algorithm is O(Delta + log n) and the message complexity per node is again O(1). Finally, we provide some experimental results to illustrate the time and message complexity of our proposed algorithms in practice. In particular, we show experimentally that the number of colours used by our distributed greedy colouring algorithms turns out to be optimal or near-optimal for many standard graph colouring benchmarks, so they provide effective simple heuristic approaches to computing a colouring with a small number of colours.
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Glendenning, Kurtis M. "Browser Based Visualization for Parameter Spaces of Big Data Using Client-Server Model." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1441203223.
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Wei, Jinliang. "Parallel Analysis of Aspect-Based Sentiment Summarization from Online Big-Data." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1505264/.
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Consumer's opinions and sentiments on products can reflect the performance of products in general or in various aspects. Analyzing these data is becoming feasible, considering the availability of immense data and the power of natural language processing. However, retailers have not taken full advantage of online comments. This work is dedicated to a solution for automatically analyzing and summarizing these valuable data at both product and category levels. In this research, a system was developed to retrieve and analyze extensive data from public online resources. A parallel framework was created to make this system extensible and efficient. In this framework, a star topological network was adopted in which each computing unit was assigned to retrieve a fraction of data and to assess sentiment. Finally, the preprocessed data were collected and summarized by the central machine which generates the final result that can be rendered through a web interface. The system was designed to have sound performance, robustness, manageability, extensibility, and accuracy.
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Silva, Jesús, Palma Hugo Hernández, Núẽz William Niebles, David Ovallos-Gazabon, and Noel Varela. "Parallel Algorithm for Reduction of Data Processing Time in Big Data." Institute of Physics Publishing, 2020. http://hdl.handle.net/10757/652134.
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Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.
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Arifuzzaman, S. M. "Parallel Mining and Analysis of Triangles and Communities in Big Networks." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/72281.
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A network (graph) is a powerful abstraction for interactions among entities in a system. Examples include various social, biological, collaboration, citation, and co-purchase networks. Real-world networks are often characterized by an abundance of triangles and the existence of well-structured communities. Thus, counting triangles and detecting communities in networks have become important algorithmic problems in network mining and analysis. In the era of big data, the network data emerged from numerous scientific disciplines are very large. Online social networks such as Twitter and Facebook have millions to billions of users. Such massive networks often do not fit in the main memory of a single machine, and the existing sequential methods might take a prohibitively large runtime. This motivates the need for scalable parallel algorithms for mining and analysis.
We design MPI-based distributed-memory parallel algorithms for counting triangles and detecting communities in big networks and present related analysis. The dissertation consists of four parts. In Part I, we devise parallel algorithms for counting and enumerating triangles. The first algorithm employs an overlapping partitioning scheme and novel load-balancing schemes leading to a fast algorithm. We also design a space-efficient algorithm using non-overlapping partitioning and an efficient communication scheme. This space efficiency allows the algorithm to work on even larger networks. We then present our third parallel algorithm based on dynamic load balancing. All these algorithms work on big networks, scale to a large number of processors, and demonstrate very good speedups. An important property, very related to triangles, of many real-world networks is high transitivity, which states that two nodes having common neighbors tend to become neighbors themselves. In Part II, we characterize networks by quantifying the number of common neighbors and demonstrate its relationship to community structure of networks. In Part III, we design parallel algorithms for detecting communities in big networks. We propose efficient load balancing and communication approaches, which lead to fast and scalable algorithms. Finally, in Part IV, we present scalable parallel algorithms for a useful graph preprocessing problem-- converting edge list to adjacency list. We present non-trivial parallelization with efficient HPC-based techniques leading to fast and space-efficient algorithms.Ph. D.
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Morgan, Kenneth J. "Design and Analysis of Four Architectures for FPGA-Based Cellular Computing." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/35578.
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The computational abilities of today's parallel supercomputers are often quite impressive, but these machines can be impractical for some researchers due to prohibitive costs and limited availability. These researchers might be better served by a more personal solution such as a "hardware acceleration" peripheral for a PC. FPGAs are the ideal device for the task: their configurability allows a problem to be translated directly into hardware, and their reconfigurability allows the same chip to be reprogrammed for a different problem.
Efficient FPGA computation of parallel problems calls for cellular computing, which uses an array of independent, locally connected processing elements, or cells, that compute a problem in parallel. The architecture of the computing cells determines the performance of the FPGA-based computer in terms of the cell density possible and the speedup over conventional single-processor computation.
This thesis presents the design and performance results of four computing-cell architectures. MULTIPLE performs all operations in one cycle, which takes the least amount of time but requires the most chip area. BIT performs all operations bit-serially, which takes a long time but allows a large cell density. The two other architectures, SINGLE and BOOTH, lie within these two extremes of the area/time spectrum.
The performance results show that MULTIPLE provides the greatest speedup over common calculation software, but its usefulness is limited by its small cell density. Thus, the best architecture for a particular problem depends on the number of computing cells required. The results also show that with further research, next-generation FPGAs can be expected to accelerate single-processor computations as much as 22,000 times.Master of Science
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Mannem, Narender Reddy. "Adaptive Data Rate Multicarrier Direct Sequence Spread Spectrum in Rayleigh Fading Channel." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1125782227.
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Cui, Henggang. "Exploiting Application Characteristics for Efficient System Support of Data-Parallel Machine Learning." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/908.
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Large scale machine learning has many characteristics that can be exploited in the system designs to improve its efficiency. This dissertation demonstrates that the characteristics of the ML computations can be exploited in the design and implementation of parameter server systems, to greatly improve the efficiency by an order of magnitude or more. We support this thesis statement with three case study systems, IterStore, GeePS, and MLtuner. IterStore is an optimized parameter server system design that exploits the repeated data access pattern characteristic of ML computations. The designed optimizations allow IterStore to reduce the total run time of our ML benchmarks by up to 50×. GeePS is a parameter server that is specialized for deep learning on distributed GPUs. By exploiting the layer-by-layer data access and computation pattern of deep learning, GeePS provides almost linear scalability from single-machine baselines (13× more training throughput with 16 machines), and also supports neural networks that do not fit in GPU memory. MLtuner is a system for automatically tuning the training tunables of ML tasks. It exploits the characteristic that the best tunable settings can often be decided quickly with just a short trial time. By making use of optimization-guided online trial-and-error, MLtuner can robustly find and re-tune tunable settings for a variety of machine learning applications, including image classification, video classification, and matrix factorization, and is over an order of magnitude faster than traditional hyperparameter tuning approaches.
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Wai, Siu-kit, and 衛兆傑. "Virtual links for multicomputers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B18038050.
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(Uncorrected OCR)
Abstract of Thesis entitled 'Virtual Links for Multicomputers' Submitted by
Siu Kit Wai
for the degree of
Master of Philosophy at Univsersity of Hong Kong in October 1996
In order to increase computation power, multiple autonomous computers or processors are connected to form a multicomputer. The performance boost is the result of exploiting in parallel the processing power available in individual processors. Parallel processing, however, requires the cooperation among the processors, which implies interprocessor communication. The efficiency of such communications is limited by the bandwidth and number of communication channels between directly connected processors.
Multiple processes on a processor share a few hardware communication links/channels to communication with processes executing on a different processor. Effective and efficient sharing of channels is important for the overall system performance; hence it is important that the sharing be properly managed. When the sharing is not provided by the hardware, it can be provided in software at system level. Without a managing component, processes need to be programmed to flight for and gain exclusive access to the communication links. This is usually not effective, error-prone, and could reduce the overall performance of processes executing in the processor.
Flexibility is a main advantage of providing a channel-sharing mechanism at system level. Parameters such as packet size, and configuration of the system can be customized and tuned to meet the communication characteristics of different applications.
In this project, we investigate how link sharing can be provided at system level. Our approach is based on idea of virtual links. The system is designed to be as transparent and easy to be used as possible. We will discuss how different
parameters and configurations affect the system functionality and performance. We also compare this software solution to other existing solutions including a hardware solution.
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Computer Science
Master
Master of Philosophy
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Grepl, Filip. "Aplikace pro řízení paralelního zpracování dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445490.
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This work deals with the design and implementation of a system for parallel execution of tasks in the Knowledge Technology Research Group. The goal is to create a web application that allows to control their processing and monitor runs of these tasks including the use of system resources. The work first analyzes the current method of parallel data processing and the shortcomings of this solution. Then the work describes the existing tools including the problems that their test deployment revealed. Based on this knowledge, the requirements for a new application are defined and the design of the entire system is created. After that the selected parts of implementation and the way of the whole system testing is described together with the comparison of the efficiency with the original system.
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Gadiraju, Krishna Karthik. "Benchmarking Performance for Migrating a Relational Application to a Parallel Implementation." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1409065914.
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朱啓祥 and Kai-cheung Chu. "Workload balancing in parallel video encoding." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31222481.
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Qu, Peng, and 瞿蓬. "Advances in parallel imaging reconstruction techniques." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36898582.
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邱祖淇 and Cho-ki Joe Yau. "Efficient solutions for the load distribution problem." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31222031.
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Tran, Viet-Trung. "Scalable data-management systems for Big Data." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00920432.
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Big Data can be characterized by 3 V's. * Big Volume refers to the unprecedented growth in the amount of data. * Big Velocity refers to the growth in the speed of moving data in and out management systems. * Big Variety refers to the growth in the number of different data formats. Managing Big Data requires fundamental changes in the architecture of data management systems. Data storage should continue being innovated in order to adapt to the growth of data. They need to be scalable while maintaining high performance regarding data accesses. This thesis focuses on building scalable data management systems for Big Data. Our first and second contributions address the challenge of providing efficient support for Big Volume of data in data-intensive high performance computing (HPC) environments. Particularly, we address the shortcoming of existing approaches to handle atomic, non-contiguous I/O operations in a scalable fashion. We propose and implement a versioning-based mechanism that can be leveraged to offer isolation for non-contiguous I/O without the need to perform expensive synchronizations. In the context of parallel array processing in HPC, we introduce Pyramid, a large-scale, array-oriented storage system. It revisits the physical organization of data in distributed storage systems for scalable performance. Pyramid favors multidimensional-aware data chunking, that closely matches the access patterns generated by applications. Pyramid also favors a distributed metadata management and a versioning concurrency control to eliminate synchronizations in concurrency. Our third contribution addresses Big Volume at the scale of the geographically distributed environments. We consider BlobSeer, a distributed versioning-oriented data management service, and we propose BlobSeer-WAN, an extension of BlobSeer optimized for such geographically distributed environments. BlobSeer-WAN takes into account the latency hierarchy by favoring locally metadata accesses. BlobSeer-WAN features asynchronous metadata replication and a vector-clock implementation for collision resolution. To cope with the Big Velocity characteristic of Big Data, our last contribution feautures DStore, an in-memory document-oriented store that scale vertically by leveraging large memory capability in multicore machines. DStore demonstrates fast and atomic complex transaction processing in data writing, while maintaining high throughput read access. DStore follows a single-threaded execution model to execute update transactions sequentially, while relying on a versioning concurrency control to enable a large number of simultaneous readers.
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Dezfouli, Mahya. "Barcoded DNA Sequencing for Parallel Protein Detection." Doctoral thesis, KTH, Genteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-159506.
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The work presented in this thesis describes methodologies developed for integration and accurate interpretation of barcoded DNA, to empower large-scale-omics analysis. The objectives mainly aim at enabling multiplexed proteomic measurements in high-throughput format through DNA barcoding and massive parallel sequencing. The thesis is based on four scientific papers that focus on three main criteria; (i) to prepare reagents for large-scale affinity-proteomics, (ii) to present technical advances in barcoding systems for parallel protein detection, and (iii) address challenges in complex sequencing data analysis. In the first part, bio-conjugation of antibodies is assessed at significantly downscaled reagent quantities. This allows for selection of affinity binders without restrictions to accessibility in large amounts and purity from amine-containing buffers or stabilizer materials (Paper I). This is followed by DNA barcoding of antibodies using minimal reagent quantities. The procedure additionally enables efficient purification of barcoded antibodies from free remaining DNA residues to improve sensitivity and accuracy of the subsequent measurements (Paper II). By utilizing a solid-phase approach on magnetic beads, a high-throughput set-up is ready to be facilitated by automation. Subsequently, the applicability of prepared bio-conjugates for parallel protein detection is demonstrated in different types of standard immunoassays (Papers I and II). As the second part, the method immuno-sequencing (I-Seq) is presented for DNAmediated protein detection using barcoded antibodies. I-Seq achieved the detection of clinically relevant proteins in human blood plasma by parallel DNA readout (Paper II). The methodology is further developed to track antibody-antigen interaction events on suspension bead arrays, while being encapsulated in barcoded emulsion droplets (Paper III). The method, denoted compartmentalized immuno-sequencing (cI-Seq), is potent to perform specific detections with paired antibodies and can provide information on details of joint recognition events. Recent progress in technical developments of DNA sequencing has increased the interest in large-scale studies to analyze higher number of samples in parallel. The third part of this thesis focuses on addressing challenges of large-scale sequencing analysis. Decoding of a huge DNA-barcoded data is presented, aiming at phase-defined sequence investigation of canine MHC loci in over 3000 samples (Paper IV). The analysis revealed new single nucleotide variations and a notable number of novel haplotypes for the 2nd exon of DLA DRB1. Taken together, this thesis demonstrates emerging applications of barcoded sequencing in protein and DNA detection. Improvements through the barcoding systems for assay parallelization, de-convolution of antigen-antibody interactions, sequence variant analysis, as well as large-scale data interpretation would aid biomedical studies to achieve a deeper understanding of biological processes. The future perspectives of the developed methodologies may therefore stem for advancing large-scale omics investigations, particularly in the promising field of DNA-mediated proteomics, for highly multiplex studies of numerous samples at a notably improved molecular resolution.QC 20150203
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Maier, Joshua. "PERFORMANCE STUDY OF SOW-AND-GROW: A NEW CLUSTERING ALGORITHM FOR BIG DATA." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2669.
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DBSCAN is a density-based clustering algorithm that is known for being able to cluster irregular shaped clusters and can handle noise points as well. For very large sets of data, however, this algorithm becomes inefficient because it must go through each and every point and look at its neighborhood in order to determine the clusters. Also, DBSCAN is hard to implement in parallel due to the structure of the data and its sequential data access. The Sow and Grow algorithm is a parallel, density-based clustering algorithm. It utilizes a concept of growing points in order to more efficiently find clusters as opposed to going through every point in the dataset in a sequential order. We create an initial seed set of variable size based on user input and a dynamic growing points vector to cluster the data. Our algorithm is designed for shared memory and can be run in parallel using threads. For our experiments, multiple datasets were used with a varying number of points and dimensions. We used this dataset to show the significant speedup the Sow-and-Grow algorithm produces as compared to other parallel, density-based clustering algorithms. On some datasets, Sow-and-Grow achieves a speedup of 8 times faster than another density-based algorithm. We also looked at how changing the number of seeds affects the results in terms of runtime and clusters discovered.
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馬家駒 and Ka-kui Ma. "Transparent process migration for parallel Java computing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226474.
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Stadtherr, Hans. "Work efficient parallel scheduling algorithms." [S.l. : s.n.], 1998. http://deposit.ddb.de/cgi-bin/dokserv?idn=962681369.
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Töben, Dirk. "Submanifolds with parallel focal structure." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=970666632.
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Song, Ge. "Méthodes parallèles pour le traitement des flux de données continus." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC059/document.
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Nous vivons dans un monde où une grande quantité de données est généré en continu. Par exemple, quand on fait une recherche sur Google, quand on achète quelque chose sur Amazon, quand on clique en ‘Aimer’ sur Facebook, quand on upload une image sur Instagram, et quand un capteur est activé, etc., de nouvelles données vont être généré. Les données sont différentes d’une simple information numérique, mais viennent dans de nombreux format. Cependant, les données prisent isolément n’ont aucun sens. Mais quand ces données sont reliées ensemble on peut en extraire de nouvelles informations. De plus, les données sont sensibles au temps. La façon la plus précise et efficace de représenter les données est de les exprimer en tant que flux de données. Si les données les plus récentes ne sont pas traitées rapidement, les résultats obtenus ne sont pas aussi utiles. Ainsi, un système parallèle et distribué pour traiter de grandes quantités de flux de données en temps réel est un problème de recherche important. Il offre aussi de bonne perspective d’application. Dans cette thèse nous étudions l’opération de jointure sur des flux de données, de manière parallèle et continue. Nous séparons ce problème en deux catégories. La première est la jointure en parallèle et continue guidée par les données. La second est la jointure en parallèle et continue guidée par les requêtesWe live in a world where a vast amount of data is being continuously generated. Data is coming in a variety of ways. For example, every time we do a search on Google, every time we purchase something on Amazon, every time we click a ‘like’ on Facebook, every time we upload an image on Instagram, every time a sensor is activated, etc., it will generate new data. Data is different than simple numerical information, it now comes in a variety of forms. However, isolated data is valueless. But when this huge amount of data is connected, it is very valuable to look for new insights. At the same time, data is time sensitive. The most accurate and effective way of describing data is to express it as a data stream. If the latest data is not promptly processed, the opportunity of having the most useful results will be missed.So a parallel and distributed system for processing large amount of data streams in real time has an important research value and a good application prospect. This thesis focuses on the study of parallel and continuous data stream Joins. We divide this problem into two categories. The first one is Data Driven Parallel and Continuous Join, and the second one is Query Driven Parallel and Continuous Join
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Fraideinberze, Antonio Canabrava. "Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17112017-154451/.
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Given a very large dataset of moderate-to-high dimensionality, how to mine useful patterns from it? In such cases, dimensionality reduction is essential to overcome the well-known curse of dimensionality. Although there exist algorithms to reduce the dimensionality of Big Data, unfortunately, they all fail to identify/eliminate non-linear correlations that may occur between the attributes. This MSc work tackles the problem by exploring concepts of the Fractal Theory and massive parallel processing to present Curl-Remover, a novel dimensionality reduction technique for very large datasets. Our contributions are: (a) Curl-Remover eliminates linear and non-linear attribute correlations as well as irrelevant attributes; (b) it is unsupervised and suits for analytical tasks in general not only classification; (c) it presents linear scale-up on both the data size and the number of machines used; (d) it does not require the user to guess the number of attributes to be removed, and; (e) it preserves the attributes semantics by performing feature selection, not feature extraction. We executed experiments on synthetic and real data spanning up to 1.1 billion points, and report that our proposed Curl-Remover outperformed two PCA-based algorithms from the state-of-the-art, being in average up to 8% more accurate.Dada uma grande base de dados de dimensionalidade moderada a alta, como identificar padrões úteis nos objetos de dados? Nesses casos, a redução de dimensionalidade é essencial para superar um fenômeno conhecido na literatura como a maldição da alta dimensionalidade. Embora existam algoritmos capazes de reduzir a dimensionalidade de conjuntos de dados na escala de Terabytes, infelizmente, todos falham em relação à identificação/eliminação de correlações não lineares entre os atributos. Este trabalho de Mestrado trata o problema explorando conceitos da Teoria de Fractais e processamento paralelo em massa para apresentar Curl-Remover, uma nova técnica de redução de dimensionalidade bem adequada ao pré-processamento de Big Data. Suas principais contribuições são: (a) Curl-Remover elimina correlações lineares e não lineares entre atributos, bem como atributos irrelevantes; (b) não depende de supervisão do usuário e é útil para tarefas analíticas em geral não apenas para a classificação; (c) apresenta escalabilidade linear tanto em relação ao número de objetos de dados quanto ao número de máquinas utilizadas; (d) não requer que o usuário sugira um número de atributos para serem removidos, e; (e) mantêm a semântica dos atributos por ser uma técnica de seleção de atributos, não de extração de atributos. Experimentos foram executados em conjuntos de dados sintéticos e reais contendo até 1,1 bilhões de pontos, e a nova técnica Curl-Remover apresentou desempenho superior comparada a dois algoritmos do estado da arte baseados em PCA, obtendo em média até 8% a mais em acurácia de resultados.
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Taljaard, Darren. "Investigating the parallels between disciplinary/bio-power and cyber-corporate empire." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1021023.
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Strong parallels exist between the formation of subjectivity through the disciplinary/bio-power technologies of the 18th/19th centuries – which Michel Foucault identified in his books Discipline and Punish: The Birth of the Prison, and The History of Sexuality Volume 1: The Will to Knowledge – and the 20th/21st century formation of subjectivity effected through Apple. Inc and Google. Inc operating systems and products. These systems and products similarly serve to canalize and ‗discipline‘ the pursuit and exchange of information, in a way that is constitutive of a new cyber-variant of disciplinary/bio-power subjectivity.
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Kindap, Nihal. "On An Architecture For A Parallel Finite Field Multiplier With Low Complexity Based On Composite Fields." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605347/index.pdf.
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In this thesis, a bit parallel architecture for a parallel finite field multiplier
with low complexity in composite fields GF((2n)m) with k = n ·m (k 32) is investigated. The architecture has lower complexity when the Karatsuba-Ofman algorithm is applied for certain k. Using particular primitive polynomials for composite fields improves the complexities. We demonstrated for the values m = 2, 4, 8 in details. This thesis is based on the paper &ldquo
A New Architecture for a Parallel Finite Field Multiplier with Low Complexity Based on Composite Fields &rdquo
by Christof Paar. The whole purpose of this thesis is to understand and present a detailed description of the results of the paper of Paar.
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朱展強 and Chin-keung Chu. "Parallel computation for time domain boundary element method." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31220678.
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Jackson, Scott Michael. "Building Maze Solutions with Computational Dreaming." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49679.
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Modern parallel computing techniques are subject to poor scalability. Their performance tends to suffer diminishing returns and even losses with increasing parallelism. Some methods of intelligent computing, such as neural networks and genetic algorithms, lend themselves well to massively parallel systems but come with other drawbacks that can limit their usefulness such as the requirement of a training phase and/or sensitivity to randomness. This thesis investigates the feasibility of a novel method of intelligent parallel computing by implementing a true multiple instruction stream, single data stream (MISD) computing system that is theoretically nearly perfectly scalable. Computational dreaming (CD) is inspired by the structure and dreaming process of the human brain. It examines previously observed input data during a 'dream phase' and is able to develop and select a simplified model to use during the day phase of computation. Using mazes as an example problem space, a CD simulator is developed and successfully used to demonstrate the viability and robustness of CD. Experiments that focused on CD viability resulted in the CD system solving 15% of mazes (ranging from small and simple to large and complex) compared with 2.2% solved by random model selection. Results also showed that approximately 50% of successful solutions generated match up with those that would be generated by algorithms such as depth first search and Dijkstra's algorithm. Experiments focusing on robustness performed repeated trials with identical parameters. Results demonstrated that CD is capable of achieving this result consistently, solving over 32% of mazes across 10 trials compared to only 3.6% solved by random model selection. A significant finding is that CD does not get stuck on local minima, always converging on a solution model. Thus, CD has the potential to enable significant contributions to computing by potentially finding elegant solutions to, for example, NP-hard or previously intractable problems.Master of Science
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Neto, Domingos Soares. "Filtros para a busca e extração de padrões aproximados em cadeias biológicas." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-19102009-002745/.
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Esta dissertação de mestrado aborda formulações computacionais e algoritmos para a busca e extração de padrões em cadeias biológicas. Em particular, o presente texto concentra-se nos dois problemas a seguir, considerando-os sob as distâncias de Hamming e Levenshtein: a) como determinar os locais nos quais um dado padrão ocorre de modo aproximado em uma cadeia fornecida; b) como extrair padrões que ocorram de modo aproximado em um número significativo de cadeias de um conjunto fornecido. O primeiro problema, para o qual já existem diversos algoritmos polinomiais, tem recebido muita atenção desde a década de 60, e ganhou novos ares com o advento da biologia computacional, nos idos dos anos 80, e com a popularização da Internet e seus mecanismos de busca: ambos os fenômenos trouxeram novos obstáculos a serem superados, em razão do grande volume de dados e das bastante justas restrições de tempo inerentes a essas aplicações. O segundo problema, de surgimento um pouco mais recente, é intrinsicamente desafiador, em razão de sua complexidade computacional, do tamanho das entradas tratadas nas aplicações mais comuns e de sua dificuldade de aproximação. Também é de chamar a atenção o seu grande potencial de aplicação. Neste trabalho são apresentadas formulações adequadas dos problemas abordados, assim como algoritmos e estruturas de dados essenciais ao seu estudo. Em especial, estudamos a extremamente versátil árvore dos sufixos, assim como uma de suas generalizações e sua estrutura irmã: o vetor dos sufixos. Grande parte do texto é dedicada aos filtros baseados em q-gramas para a busca aproximada de padrões e algumas de suas mais recentes variações. Estão cobertos os algoritmos bit-paralelos de Myers e Baeza-Yates-Gonnet para a busca de padrões; os algoritmos de Sagot para a extração de padrões; os algoritmos de filtragem de Ukkonen, Jokinen-Ukkonen, Burkhardt-Kärkkäinen, entre outros.This thesis deals with computational formulations and algorithms for the extraction and search of patterns from biological strings. In particular, the present text focuses on the following problems, both considered under Hamming and Levenshtein distances: 1. How to find the positions where a given pattern approximatelly occurs in a given string; 2. How to extract patterns which approximatelly occurs in a certain number of strings from a given set. The first problem, for which there are many polinomial time algorithms, has been receiving a lot of attention since the 60s and entered a new era of discoveries with the advent of computational biology, in the 80s, and the widespread of the Internet and its search engines: both events brought new challenges to be faced by virtue of the large volume of data usually held by such applications and its time constraints. The second problem, much younger, is very challenging due to its computational complexity, approximation hardness and the size of the input data usually held by the most common applications. This problem is also very interesting due to its potential of application. In this work we show computational formulations, algorithms and data structures for those problems. We cover the bit-parallel algorithms of Myers, Baeza-Yates-Gonnet and the Sagots algorithms for patterns extraction. We also cover here the oustanding versatile suffix tree, its generalised version, and a similar data structure: the suffix array. A significant part of the present work focuses on q-gram based filters designed to solve the approximate pattern search problem. More precisely, we cover the filter algorithms of Ukkonen, Jokinen-Ukkonen and Burkhardt-Kärkkäinen, among others.
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Schmollinger, Martin. "Designing parallel algorithms for SMP clusters." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=969343841.
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Cheung, Wing-fung Jacob, and 張穎鋒. "Kinematics, dynamics and control of high precision parallel manipulators." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B37907116.
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Riedel, Marion Köchel Peter. "Parallele Genetische Algorithmen." [S.l.] : Techn. Univ. Chemnitz, Fakultät für Informatik, 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10324739.
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Hafeez, Abdul. "A Software Framework For the Detection and Classification of Biological Targets in Bio-Nano Sensing." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/50490.
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Detection and identification of important biological targets, such as DNA, proteins, and diseased human cells are crucial for early diagnosis and prognosis. The key to discriminate healthy cells from the diseased cells is the biophysical properties that differ radically. Micro and nanosystems, such as solid-state micropores and nanopores can measure and translate these properties of biological targets into electrical spikes to decode useful insights. Nonetheless, such approaches result in sizable data streams that are often plagued with inherit noise and baseline wanders. Moreover, the extant detection approaches are tedious, time-consuming, and error-prone, and there is no error-resilient software that can analyze large data sets instantly. The ability to effectively process and detect biological targets in larger data sets lie in the automated and accelerated data processing strategies using state-of-the-art distributed computing systems.
In this dissertation, we design and develop techniques for the detection and classification of biological targets and a distributed detection framework to support data processing from multiple bio-nano devices. In a distributed setup, the collected raw data stream on a server node is split into data segments and distributed across the participating worker nodes. Each node reduces noise in the assigned data segment using moving-average filtering, and detects the electric spikes by comparing them against a statistical threshold (based on the mean and standard deviation of the data), in a Single Program Multiple Data (SPMD) style. Our proposed framework enables the detection of cancer cells in a mixture of cancer cells, red blood cells (RBCs), and white blood cells (WBCs), and achieves a maximum speedup of 6X over a single-node machine by processing 10 gigabytes of raw data using an 8-node cluster in less than a minute, which will otherwise take hours using manual analysis.
Diseases such as cancer can be mitigated, if detected and treated at an early stage. Micro and nanoscale devices, such as micropores and nanopores, enable the translocation of biological targets at finer granularity. These devices are tiny orifices in silicon-based membranes, and the output is a current signal, measured in nanoamperes. Solid-state micropore is capable of electrically measuring the biophysical properties of human cells, when a blood sample is passed through it. The passage of cells via such pores results in an interesting pattern (pulse) in the baseline current, which can be measured at a very high rate, such as 500,000 samples per second, and even higher resolution. The pulse is essentially a sequence of temporal data samples that abruptly falls below and then reverts back to a normal baseline with an acceptable predefined time interval, i.e., pulse width. The pulse features, such as width and amplitude, correspond to the translocation behavior and the extent to which the pore is blocked, under a constant potential. These features are crucial in discriminating the diseased cells from healthy cells, such as identifying cancer cells in a mixture of cells.Ph. D.
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Kohlberger, Timo. "Variational domain decomposition for parallel image processing." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985127996.
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Meyer, Julia S. "Mesoscopic phenomena driven by parallel magnetic fields." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963498185.
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