The modular decomposition of a graph G = (V;E) does not contain prime modules if and only if G is a cograph, that is, if no quadruple of vertices induces a simple connected path P4. The cograph editing problem consist...
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The wide adoption of smart devices has stimulated a fast shift of security-critical data from desktop to mobile devices. However, recurrent device theft and loss expose mobile devices to various security threats and e...
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Sparse grids have become a versatile tool for a vast range of applications reaching from interpolation and numerical quadrature to data-driven problems and uncertainty *** review four selected real-world applications ...
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Sparse grids have become a versatile tool for a vast range of applications reaching from interpolation and numerical quadrature to data-driven problems and uncertainty *** review four selected real-world applications of sparse grids:financial product pricing with the Black-Scholes model,interactive explo-ration of simulation data with sparse-grid-based surrogate models,analysis of simu-lation data through sparse grid data mining methods,and stability investigations of plasma turbulence simulations.
Along with the rise of mobile handheld devices the resource demands of respective applications grow as well. However, mobile devices are still and will always be limited related to performance (e.g., computation, stor...
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Along with the rise of mobile handheld devices the resource demands of respective applications grow as well. However, mobile devices are still and will always be limited related to performance (e.g., computation, storage and battery life), context adaptation (e.g., intermittent connectivity, scalability and heterogeneity) and security aspects. A prominent solution to overcome these limita- tions is the so-called computation offloading, which is the focus of mobile cloud computing (MCC). However, current approaches fail to address the complexity that results from quickly and constantly changing context conditions in mobile user scenarios and hence developing effective and efficient MCC applications is still challenging. Therefore, this paper first presents a list of re- quirements for MCC applications together with a survey and classification of current solutions. Furthermore, it provides a design guideline for the selection of suitable concepts for different classes of common cloud-augmented mobile applications. Finally, it presents open issues that developers and researchers should be aware of when designing their MCC-approach.
Cloud computing has become a widely used paradigm in many IT domains such as e-health. It offers several advantages to the users, e.g. elasticity, flexibility and the rapid sharing of a huge set of digital data. Howev...
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Large-scale graph-structured computation usually exhibits iterative and convergence-oriented computing nature, where input data is computed iteratively until a convergence condition is reached. Such features have led ...
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ISBN:
(纸本)9781450332057
Large-scale graph-structured computation usually exhibits iterative and convergence-oriented computing nature, where input data is computed iteratively until a convergence condition is reached. Such features have led to the development of two different computation modes for graph-structured programs, namely synchronous (Sync) and asynchronous (Async) modes. Unfortunately, there is currently no in-depth study on their execution properties and thus programmers have to manually choose a mode, either requiring a deep understanding of underlying graph engines, or suffering from suboptimal performance. This paper makes the first comprehensive characterization on the performance of the two modes on a set of typical graph-parallel applications. Our study shows that the performance of the two modes varies significantly with different graph algorithms, partitioning methods, execution stages, input graphs and cluster scales, and no single mode consistently outperforms the other. To this end, this paper proposes Hsync, a hybrid graph computation mode that adaptively switches a graph-parallel program between the two modes for optimal performance. Hsync constantly collects execution statistics on-the-fly and leverages a set of heuristics to predict future performance and determine when a mode switch could be profitable. We have built online sampling and offline profiling approaches combined with a set of heuristics to accurately predicting future performance in the two modes. A prototype called PowerSwitch has been built based on PowerGraph, a state-of-the-art distributed graph-parallel system, to support adaptive execution of graph algorithms. On a 48-node EC2-like cluster, PowerSwitch consistently outperforms the best of both modes, with a speedup ranging from 9% to 73% due to timely switch between two modes. Copyright 2015 ACM.
Data aggregation is a key element in many applications that draw insights from data analytics, such as medical research, smart metering, recommendation systems and real-time marketing. In general, data is gathered fro...
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In this paper, computing systems that have no central control and consist of many (partially) autonomous components are studied. Two types of components are distinguished which are called workers and helpers respectiv...
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High quality video streaming for mobile users is difficult to achieve in some areas of the world due to poor broadband capacity and sparse network coverage. We propose a bandwidth-sharing scheme to allow users with li...
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