An efficient sparse conjugate gradient solver using a Beneš permutation network

© 2014 Technical University of Munich (TUM).The conjugate gradient (CG) is one of the most widely used iterative methods for solving systems of linear equations. However, parallelizing CG for large sparse systems is difficult due to the inherent irregularity in memory access pattern. We propose a novel processor architecture for the sparse conjugate gradient method. The architecture consists of multiple processing elements and memory banks, and is able to compute efficiently both sparse matrix-vector multiplication, and other dense vector operations. A Beneš permutation network with an optimised control scheme is introduced to reduce memory bank conflicts without expensive logic. We describe a heuristics for offline scheduling, the effect of which is captured in a parametric model for estimating the performance of designs generated from our approach

Benefit salience and consumers' selective attention to product features.

Although attention is a key construct in models of marketing communication and consumer choice, its selective nature has rarely been examined in common time-pressured conditions. We focus on the role of benefit salience, that is, the readiness with which particular benefits are brought to mind by consumers in relation to a given product category. Study I demonstrated that when product feature information was presented rapidly, individuals for whom the benefit of personalised customer service had high habitual salience displayed selective attention as evidenced by elevated recall and recognition of a target feature (a bank's ''friendly employees''). Also, as expected, individual differences in habitual benefit salience affected judgements of the target product. Study 2 showed that when subjects were additionally informed about a specific product usage situation, selective attention was primarily influenced by the relevance of the target feature to benefits made salient by the usage situation; individual differences played a less important role. Discussion emphasises theoretical aspects of the findings as well as managerial implications with respect to person-situation approaches to benefit segmentation. (C) 1997 Elsevier Science B.V.attention; benefit segmentation; individual construct accessibility; usage context; involvement; segmentation; substitution; experiences; memory; recall; choice; link;

The influence of consumers' goals on selective attention to product features.

Although attention is a key construct in models of marketing communications and consumer choice, its selective nature has rarely been examined in the time-pressured conditions that consumers face everyday. We investigate how consumers' goals influence selective attention to product features under such conditions. Specifically, we focus on the role of goal salience, that is, the readiness with which particular goals (e.g., personalized customer service) are brought to mind by consumers in relation to a given product category (e.g., banks). Study1 demonstrated that when product feature information was presented rapidly, individuals for whom the goal of personalized customer service had high chronic or habitual salience displayed selective attention in terms of their elevated recall of a target feature (a bank's 'friendly employees'). Also, as expected, individual differences in chronic goal salience affected judgments of the target product. Study2 showed that when subjects were additionally informed about a specific product usage situation (e.g., being new in town or experiencing difficulty in balancing a checkbook), selective attention was no longer affected by individuals' chronic tendencies. Instead, both feature recall and judgments were influenced by the relevance of the target feature to the goals made salient by the situational context. Discussion emphasizes the theoretical and managerial implications of the findings regarding the role of goal salience in selective attention to product features.Product;

On the estimation of the domain of attraction for discrete-time switched and hybrid nonlinear systems

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Protective efficacy of poultry vaccines against recently circulating Highly Pathogenic Avian Influenza (HPAI) H5N1 virus isolater from markets and farms in Hong Kong 2008

Parallel Session 2 - Emerging/Infectious Diseases: no. S7Theme: Translating Health Research into Policy and Practice for Health of the PopulationINTRODUCTION: Highly pathogenic avian influenza (HPAI) H5N1 remains a major threat to animal and public health. Since 2003, Hong Kong has successfully used poultry vaccination as part of its strategy to minimise this threat within Hong Kong. In mid-2008, an HPAI H5N1 outbreak occurred in a vaccinated poultry farm in Hong Kong. AIMS: a) to compare protective efficacy of different poultry vaccines against the 2008 farm outbreak strain; and b) to assess whether there needs to be a change in the poultry vaccine used in Hong Kong. METHODS: White leghorn chickens were raised in a clean laboratory environment and …published_or_final_versio

A domain specific approach to high performance heterogeneous computing

Users of heterogeneous computing systems face two problems: first, in understanding the trade-off relationships between the observable characteristics of their applications, such as latency and quality of the result, and second, how to exploit knowledge of these characteristics to allocate work to distributed computing platforms efficiently. A domain specific approach addresses both of these problems. By considering a subset of operations or functions, models of the observable characteristics or domain metrics may be formulated in advance, and populated at run-time for task instances. These metric models can then be used to express the allocation of work as a constrained integer program. These claims are illustrated using the domain of derivatives pricing in computational finance, with the domain metrics of workload latency and pricing accuracy. For a large, varied workload of 128 Black-Scholes and Heston model-based option pricing tasks, running upon a diverse array of 16 Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both the makespan and accuracy are generally within 10 percent of the run-time performance. When these models are used as inputs to machine learning and MILP-based workload allocation approaches, a latency improvement of up to 24 and 270 times over the heuristic approach is seen

Homogeneous Polynomial Lyapunov Functions for Robust Local Synchronisation with Time-varying Uncertainties

This study studies robust local synchronisation in multi-agent systems with time-varying parametric uncertainties constrained in a polytope. In contrast to the existing methods with non-convex conditions via using quadratic Lyapunov function, a new criteria is proposed based on using homogeneous polynomial Lyapunov functions where the original system is suitably approximated by an uncertain polytopic system. Furthermore, the corresponding tractable conditions of linear matrix inequalities have been provided by exploiting the squares matrix representation. Then, the polytopic synchronisation margin problem is, for the first time, proposed and investigated via handling generalised eigenvalue problems. Lastly, numerical examples illustrate the usefulness of the proposed method.postprin

Neurorestoratology evidence in an animal model with cervical spondylotic myelopathy

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