Snoring might indicate the first stage of the Obstructive Sleep Apnea (OSA) syndrome, and has received much attention in recent years. Previous studies on snoring have shown that upper airway narrowing, collapse, and ...
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We propose a method that automatically generates a smooth chase camera movement to follow a subject, a user-controlled character or a character with unknown behaviors, in a 3D environment freely in real time. We consi...
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Electronic systems that offer elastic mechanical responses to high strain deformations are of growing interest, due to their ability to enable new electrical, optical and biomedical devices and other applications whos...
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This paper discusses a parallel implementation of the Finite-Difference Time-Domain (FDTD) method using Unified Parallel C (UPC). UPC is an extension of ANSI C - primarily designed for programming distributed memory p...
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Applications with divisible loads have such a rich source of parallelism that their parallelization can significantly reduce their total completion time on grid computing environments. However, it is a challenge for g...
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Applications with divisible loads have such a rich source of parallelism that their parallelization can significantly reduce their total completion time on grid computing environments. However, it is a challenge for grid users, probably scientists and engineers, to develop their applications which can exploit the computing power of the grid. We propose a performance-based skeleton algorithm for implementing divisible load applications on grids. Following this skeleton, novice grid programmers can easily develop a highperformance grid application. To examine the performance of programs developed by this approach, we apply this skeleton to implement three kinds of applications and conduct experiments on our grid test-bed. Experimental results show that programs implemented by this approach run more rapidly than those using conventional scheduling schemes.
In this paper, we develop data-driven strategies for batch computing schedulers. Current CPU-centric batch schedulers ignore the data needs within workloads and execute them by linking them transparently and directly ...
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ISBN:
(纸本)9781605585895
In this paper, we develop data-driven strategies for batch computing schedulers. Current CPU-centric batch schedulers ignore the data needs within workloads and execute them by linking them transparently and directly to their needed data. When scheduled on remote computational resources, this elegant solution of direct data access can incur an order of magnitude performance penalty for data-intensive workloads. Adding data-awareness to batch schedulers allows a careful coordination of data and CPU allocation thereby reducing the cost of remote execution. We offer here new techniques by which batch schedulers can become data-driven. Such systems can use our analytical predictive models to select one of the four data-driven scheduling policies that we have created. Through simulation, we demonstrate the accuracy of our predictive models and show how they can reduce time to completion for some workloads by as much as 80%. Copyright 2009 ACM.
Applications performance is a criterion for system evaluation, and hence performance tuning for these applications is of great interest. One such benchmark application is highperformance Linpack (HPL). Although guide...
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With the development of emerging social networks, such as Facebook and MySpace, security and privacy threats arising from social network analysis bring a risk of disclosure of confidential knowledge when the social ne...
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ISBN:
(纸本)9781615671090
With the development of emerging social networks, such as Facebook and MySpace, security and privacy threats arising from social network analysis bring a risk of disclosure of confidential knowledge when the social network data is shared or made public. In addition to the current social network anonymity de-identification techniques, we study a situation, such as in a business transaction network, in which weights are attached to network edges that are considered to be confidential (e.g., transactions). We consider perturbing the weights of some edges to preserve data privacy when the network is published, while retaining the shortest path and the approximate cost of the path between some pairs of nodes in the original network. We develop two privacy-preserving strategies for this application. The first strategy is based on a Gaussian randomization multiplication, the second one is a greedy perturbation algorithm based on graph theory. In particular, the second strategy not only yields an approximate length of the shortest path while maintaining the shortest path between selected pairs of nodes, but also maximizes privacy preservation of the original weights. We present experimental results to support our mathematical analysis.
The discovery of biomarkers and the underlying causes of diseases are enabled through the analysis of biological samples and data stored in biobanks. Biological samples and their associated data are expensive to colle...
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