Pattern recognition is a resource intensive task which includes feature extraction, feature selection and classification. Optimizing any of these steps can significantly improve performance. Evolutionary computation m...
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ISBN:
(纸本)9781932415988
Pattern recognition is a resource intensive task which includes feature extraction, feature selection and classification. Optimizing any of these steps can significantly improve performance. Evolutionary computation methods are utilized to address optimization problems that explore a huge, nonlinear and multidimensional search space. In this paper a new distributed framework is introduced which greatly reduces the computation time of such systems, concentrating on feature extraction and feature selection which will operate parallel. This software architecture incorporates Mother Nature's most powerful tools, "evolution" and "parallelism", in its design, while maintaining robustness.
When data mining first appeared, several disciplines related to data analysis, like statistics or artificial intelligence were combined toward a new topic: extracting significant patterns from data. The original data ...
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Performance of distributedapplications largely depends oil the mapping of their Components on the underlying architecture. On one side, component-based approaches provide an abstraction suitable for development, but ...
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ISBN:
(纸本)9783540854500
Performance of distributedapplications largely depends oil the mapping of their Components on the underlying architecture. On one side, component-based approaches provide an abstraction suitable for development, but on the other side, actual hardware becomes every day more complex and heterogeneous. Despite this increasing gap, mapping components to processors and networks is commonly done manually and is mainly a matter of expertise. Worse, the amount of efforts required for this task rarely allows to further consider optimal hardware use or sensitivity analysis of data scaling. In this paper, we rely on a formal and e,experimentally sound model of performance and propose a constraint programming based framework to find consistent and efficient mappings of an application onto an architecture. Experiments show that an optimal mapping for a, medium-sized application can he found in a few seconds.
In this paper we present different FDTD techniques which we have developed for solving large electromagnetic structures. The first technique is the M3d(24) which is a low dispersion FDTD technique. In this scheme the ...
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ISBN:
(纸本)9780780397446
In this paper we present different FDTD techniques which we have developed for solving large electromagnetic structures. The first technique is the M3d(24) which is a low dispersion FDTD technique. In this scheme the higher order FDTD updating equations is modified and derived as applications of a version of Ampere's law and Faraday's law using central 2(nd) order in time and 4(th) order in space. The second technique is the FDTD hybrid "M3d(24) -Yee" with subgridding. This scheme is based on applying the conventional FDTD in the vicinity of the structure using a high resolution grid and the M3d24 method in the rest of the domain using low resolution grid. The third technique is domain decomposition FDTD (DDFDTD) which is based on dividing the 1structure into relatively small sub-regions and applying the FDTD in a serial manner. The fourth technique is the serial parallel FDTD which is based on applying the DDFDTD and the parallel FDTD using MPI functions along two orthogonal directions.
In edge computing systems, computation is rather offloaded to nearby resources than to the cloud, due to latency reasons. However, the performance demand in the edge grows steadily, which makes nearby resources insuff...
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ISBN:
(纸本)9781538651568
In edge computing systems, computation is rather offloaded to nearby resources than to the cloud, due to latency reasons. However, the performance demand in the edge grows steadily, which makes nearby resources insufficient for many applications. Additionally, the amount of parallel tasks in the edge increases, based on trends like machine learning, Internet of Things, and artificial intelligence. This introduces a trade-off between the performance of the cloud and the communication latency of the edge. However, many edge devices have powerful co-processors in form of their graphics-processing unit (GPU), which are mostly unused. These processing units have specialized parallel architectures, which are different from standard CPUs and complex to use. In this paper, we present GPU-accelerated task execution for edge computing environments. The paper has four contributions. First, we design and implement a GPU system extension for our Tasklet system - a distributed computing system, which supports edge- and cloud-based task offloading. Second, we introduce a computational abstraction for GPUs in form of a virtual machine, which exploits parallelism while considering device heterogeneity and maintaining unobtrusiveness. Third, we offer an easy-to-use programming interface for the rather complex architecture of GPUs. Fourth, we evaluate our prototype in a real-world testbed and compare the GPU performance to standard edge resources.
In the past decade, many database computer (DBCs) have been proposed to use parallel architecture to support processing of database modelled by various data models. The design of a database computer to support databas...
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The positioning technique is the key technique for developing geographic applications, like location based services. The Global Positioning System (GPS) is a common approach for positioning in vehicular navigations. A...
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Coupled systems comprise multiple mutually interacting subsystems, and are an increasingly common computational science application, most notably as multiscale and multiphysics models. parallel computing, and in parti...
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ISBN:
(纸本)9783540744825
Coupled systems comprise multiple mutually interacting subsystems, and are an increasingly common computational science application, most notably as multiscale and multiphysics models. parallel computing, and in particular message-passing programming have spurred the development of these models, but also present a parallel coupling problem (PCP) in the form of intermodel data dependencies. The PCP complicates model coupling through requirements for the description, transfer, and transformation of the distributed data that models in a parallel coupled system exchange. Component-based software engineering has been proposed as one means of conquering software complexity in scientific applications, and given the compound nature of coupled models, it is a natural approach to addressing the parallel coupling problem. We define a software component specification for solving the parallel coupling problem. This design draws from the already successful Common Component Architecture (CCA). We abstract the parallel coupling problem's elements and map them onto a set of CCA components, defining a parallel coupling infrastructure toolkit. We discuss a reference implementation based on the Model Coupling Toolkit. We demonstrate how these components might be deployed to solve a relevant coupling problems in climate modeling.
During the internationalconference on parallelprocessing, held August 15-19, 1994, we convened a panel to discuss the state of the art in parallel I/0, tools and techniques to address current problems, and challenge...
During the internationalconference on parallelprocessing, held August 15-19, 1994, we convened a panel to discuss the state of the art in parallel I/0, tools and techniques to address current problems, and challenges for the future. The following is an edited transcript of that panel.
There has seen a strong demand for provenance in grid applications, which enables users to trace how a particular result has been arrived at by identifying the resources, configurations and execution settings. In this...
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ISBN:
(数字)9783540377849
ISBN:
(纸本)3540377832
There has seen a strong demand for provenance in grid applications, which enables users to trace how a particular result has been arrived at by identifying the resources, configurations and execution settings. In this paper we analyses the requirements of provenance support and discusses the nature and characteristics of provenance data on the Grid. We define a new conception called augmented provenance that enhances conventional provenance data with extensive metadata and semantics. A hybrid approach is proposed for the creation and management of augmented provenance in which semantic annotation is used to generate semantic provenance data and the database management system is used for execution data management. The approach has been applied to a real world application, and tools and GUIs are developed to facilitate provenance management and exploitation.
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