The amount of information available through the Internet has been showing a significant growth in the last decade. The information can result from various sources such as scientific experiments resulting from particle...
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The amount of information available through the Internet has been showing a significant growth in the last decade. The information can result from various sources such as scientific experiments resulting from particle acceleration, recording the flight data of a commercial aircraft, or sets of documents from a given domain such as medical articles, news headlines from a newspaper, or social networks contents. Due to the volume of data that must be analyzed, it is necessary to endow the search engines with new tools that allow the user to obtain the desired information in a timely and accurate manner. One approach is the annotation of documents with their relevant expressions. The extraction of relevant expressions from natural language text documents can be accomplished by the use of semantic, syntactic, or statistical techniques. Although the latter tend to be not so accurate, they have the advantage of being independent of the language. This investigation was performed in the context of LocalMaxs, which is a statistical method, thus language-independent, capable of extracting relevant expressions from natural language corpora. However, due to the large volume of data involved, the sequential implementations of the above techniques have severe limitations both in terms of execution time and memory space. In this thesis we propose a distributed architecture and strategies for parallel implementations of statistical-based extraction of relevant expressions from large corpora. A methodology was developed for modeling and evaluating those strategies based on empirical and theoretical approaches to estimate the statistical distribution of n-grams in natural language corpora. These approaches were applied to guide the design and evalu- ation of the behavior of LocalMaxs parallel and distributed implementations on cluster and cloud computing platforms. The implementation alternatives were compared regar- ding their precision and recall, and their performance metrics, namel
This paper evaluates dependability on parallel differential evolution (DE) based voltage and reactive power control (Volt/Var Control: VVC) Considering large penetration of renewable energies and deregulated environme...
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Big data is a potential research area receiving considerable attention from academia and IT communities. In the digital world, the amounts of data generated and stored have expanded within a short period of time. Cons...
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Big data is a potential research area receiving considerable attention from academia and IT communities. In the digital world, the amounts of data generated and stored have expanded within a short period of time. Consequently, this fast growing rate of data has created many challenges. In this paper, we use structuralism and functionalism paradigms to analyze the origins of big data applications and its current trends. This paper presents a comprehensive discussion on state-of-the-art big data technologies based on batch and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also discusses big data analytics techniques, processing methods, some reported case studies from different vendors, several open research challenges, and the opportunities brought about by big data. The similarities and differences of these techniques and technologies based on important parameters are also investigated. Emerging technologies are recommended as a solution for big data problems. (C) 2016 Elsevier Ltd. All rights reserved.
This paper evaluates dependability of parallel differential evolutionary particle swarm optimization (DEEPSO) based voltage and reactive power control (Volt/Var Control: VVC). Considering large penetration of renewabl...
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ISBN:
(纸本)9781467388481
This paper evaluates dependability of parallel differential evolutionary particle swarm optimization (DEEPSO) based voltage and reactive power control (Volt/Var Control: VVC). Considering large penetration of renewable energies and deregulated environment of power systems, VVC is required to realize faster computation to larger-scale problems and one of the solutions for the problem is applications of parallel and distributed computing. Since power system is one of the infrastructures of social community, not only fast computation, but also sustainable control (dependability) is strongly required for VVC. The simulation results with IEEE 118 bus systems indicate that parallel DEEPSO is superior to parallel PSO and parallel differential evolution (DE) from the dependability point of view.
Intel Xeon Phi Processor High Performance Programming is an all-in-one source of information for programming the Second-Generation Intel Xeon Phi product family also called Knights Landing. The authors provide detaile...
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ISBN:
(数字)9780128091951
ISBN:
(纸本)9780128091944
Intel Xeon Phi Processor High Performance Programming is an all-in-one source of information for programming the Second-Generation Intel Xeon Phi product family also called Knights Landing. The authors provide detailed and timely Knights Landingspecific details, programming advice, and real-world examples. The authors distill their years of Xeon Phi programming experience coupled with insights from many expert customers Intel Field Engineers, Application Engineers, and Technical Consulting Engineers to create this authoritative book on the essentials of programming for Intel Xeon Phi products. Intel Xeon Phi鈩?Processor High-Performance Programming is useful even before you ever program a system with an Intel Xeon Phi processor. To help ensure that your applications run at maximum efficiency, the authors emphasize key techniques for programming any modern parallelcomputing system whether based on Intel Xeon processors, Intel Xeon Phi processors, or other high-performance microprocessors. Applying these techniques will generally increase your program performance on any system and prepare you better for Intel Xeon Phi processors.A practical guide to the essentials for programming Intel Xeon Phi processorsDefinitive coverage of the Knights Landing architecturePresents best practices for portable, high-performance computing and a familiar and proven threads and vectors programming modelIncludes real world code examples that highlight usages of the unique aspects of this new highly parallel and high-performance computational productCovers use of MCDRAM, AVX-512, Intel Omni-Path fabric, many-cores (up to 72), and many threads (4 per core)Covers software developer tools, libraries and programming modelsCovers using Knights Landing as a processor and a coprocessor
During the last decade, the fast evolution in communication networks has facilitated the development of complex applications that manage vast amounts of data, like Big Data applications. Unfortunately, the high comple...
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During the last decade, the fast evolution in communication networks has facilitated the development of complex applications that manage vast amounts of data, like Big Data applications. Unfortunately, the high complexity of these applications hampers the testing process. Moreover, generating adequate test suites to properly check these applications is a challenging task due to the elevated number of potential test cases. Mutation testing is a valuable technique to measure the quality of the selected test suite that can be used to overcome this difficulty. However, one of the main drawbacks of mutation testing lies on the high computational cost associated to this process. In this paper we propose a dynamic distributed algorithm focused on HPC systems, called EMINENT, which has been designed to face the performance problems in mutation testing techniques. EMINENT alleviates the computational cost associated with this technique since it exploits parallelism in cluster systems to reduce the final execution time. In addition, several experiments have been carried out on three applications in order to analyse the scalability and performance of EMINENT. The results show that EMINENT provides an increase in the speed-up in most scenarios.
We propose a distributed local search (DLS) algorithm, which is a parallel formulation of a local search procedure in an attempt to follow the spirit of standard local search metaheuristics. Applications of different ...
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ISBN:
(纸本)9783319503073;9783319503066
We propose a distributed local search (DLS) algorithm, which is a parallel formulation of a local search procedure in an attempt to follow the spirit of standard local search metaheuristics. Applications of different operators for solution diversification are possible in a similar way to variable neighborhood search. We formulate a general energy function to be equivalent to elastic image matching problems. A specific example application is stereo matching. Experimental results show that the GPU implementation of DLS seems to be the only method that provides an increasing acceleration factor as the instance size augments, among eight tested energy minimization algorithms.
The aim of this paper is to present a new distributedcomputing middleware for High Performance computing (HPC) based cloud micro-services. The great challenge is to maintain the scalability and efficiency of massivel...
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ISBN:
(纸本)9781509051465
The aim of this paper is to present a new distributedcomputing middleware for High Performance computing (HPC) based cloud micro-services. The great challenge is to maintain the scalability and efficiency of massively parallel and distributed computational system when the intensive big data processed by its applications is widely increased. Besides, the proposed middleware implements a new cooperative micro-services team works model for massively parallel and distributed computing. This model is constituted by distributed micro-services as Micro-service Virtual Processing Units (MsVPUs) with integrated load balancing service and an AMQP communication protocol that grant HPC. The paper shows the proposed distributed computational scheme and its integrated middleware accompanying by some experimental results.
Feature extraction and tracking is a fundamental operation used in many geoscience applications. In this paper, we present a scalable method for computing and tracking features on distributed memory machines for large...
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ISBN:
(纸本)9781509033324
Feature extraction and tracking is a fundamental operation used in many geoscience applications. In this paper, we present a scalable method for computing and tracking features on distributed memory machines for large-scale geospatial data. We carefully apply new communication schemes to minimize the data exchanged among the computing nodes in building and updating the global connectivity information of features. We present a theoretical complexity analysis, and show that our method can significantly reduce the communication cost compared to the traditional method.
During the last decade, the fast evolution in communication networks has facilitated the development of complex applications that manage vast amounts of data, like Big Data applications. Unfortunately, the high comple...
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During the last decade, the fast evolution in communication networks has facilitated the development of complex applications that manage vast amounts of data, like Big Data applications. Unfortunately, the high complexity of these applications hampers the testing process. Moreover, generating adequate test suites to properly check these applications is a challenging task due to the elevated number of potential test cases. Mutation testing is a valuable technique to measure the quality of the selected test suite that can be used to overcome this difficulty. However, one of the main drawbacks of mutation testing lies on the high computational cost associated to this process. In this paper we propose a dynamic distributed algorithm focused on HPC systems, called EMINENT, which has been designed to face the performance problems in mutation testing techniques. EMINENT alleviates the computational cost associated with this technique since it exploits parallelism in cluster systems to reduce the final execution time. In addition, several experiments have been carried out on three applications in order to analyse the scalability and performance of EMINENT. The results show that EMINENT provides an increase in the speed-up in most scenarios.
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