Parallel loop scheduling on grid environments is a challenging problem, especially for loops with irregular workload distribution. In the past, this problem of load imbalance resulting from irregular workload was not ...
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Parallel loop scheduling on grid environments is a challenging problem, especially for loops with irregular workload distribution. In the past, this problem of load imbalance resulting from irregular workload was not explicitly addressed. This paper proposes a new approach to schedule loop iterations with irregular workload on grid environments. Based on knowledge-based estimation of workload, the proposed method can dispatch an appropriate proportion of workload to each node for execution according to its performance. In addition, the scheduler uses historical statistics of CPU usage and network bandwidth to estimate the dynamically changing performance of each node. Two applications, regular type and irregular one respectively, are implemented and executed on a grid test-bed, which consists of four schools. Experimental results show that the new approach improves the performance on previous schemes
We have encountered two challenges when using the PACS system. First, PACS users are limited to certain bandwidths and locations. Second, Web PACS machine replacement is too costly, management is difficult, and better...
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We have encountered two challenges when using the PACS system. First, PACS users are limited to certain bandwidths and locations. Second, Web PACS machine replacement is too costly, management is difficult, and better image stability is needed. There are also speed variations for different users at different locations. For instance, radiologists use medical image workstations with direct access to the PACS information system and so have a greater speed for querying and file retrieval. Physicians, on the other hand, use web browsers with no direct access to the PACS information system, which leads to slower network speeds. Physicians also affect one another in overall network speed by processing queries and file retrievals via Web browser. There are also insufficient network bandwidth concerns. These often arise when exchanging medical images with other hospitals or downloading large numbers of images. Since these large file volumes are transferred via WANs, insufficient network bandwidths limit upload and download speeds. And if the Web PACS breaks down, the hospital must ask professional engineers for replacements, and spend large amounts of money about a NT$ million or more per unit. This is not only troublesome for system managers, but also costly for the hospital.
Smith-Purcell radiation (SPR) arises when an electron moves above a spatial grating, where fixed momentum compensation constrains the emission wavelength. In contrast, temporal SPR ( t -SPR) utilizes a time grating to...
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Smith-Purcell radiation (SPR) arises when an electron moves above a spatial grating, where fixed momentum compensation constrains the emission wavelength. In contrast, temporal SPR ( t -SPR) utilizes a time grating to leverage temporal periodicity for energy compensation, enabling more flexible radiation control. We introduce a generalized dispersion equation for t -SPR that predicts the interplay between radiation frequency, direction, electron velocity, modulation period, and harmonic orders. t -SPR offers several advantages over conventional SPR: robustness ( t -SPR maintains stable emission intensity without rapid decay as the electron moves away from the time grating), versatility ( t -SPR enables precise control via temporal modulation of period, amplitude, and waveform), and amplified emission (temporal modulation introduces additional energy channels, boosting emission). These features make t -SPR highly promising for advanced applications in on-chip light sources and electron accelerators.
Lots of submesh allocation strategies are devised in mesh connected computers. In chip multiprocessors connected by network on chip, submesh allocation is also used to allocate processors. Existing research mainly foc...
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Lots of submesh allocation strategies are devised in mesh connected computers. In chip multiprocessors connected by network on chip, submesh allocation is also used to allocate processors. Existing research mainly focuses on fragmentation, system performance and algorithmic complexity. The processors are assumed to be homogeneous, and the representation of them only reflects the state of processors usage, free or allocated. However, heterogeneous cores have been widely adopted in modern chip multiprocessors to better support the different computation needs. Therefore, the submesh allocation should be re-designed to reflect the difference between cores in heterogeneous chip multiprocessors. In this paper, a simple submesh allocation strategy based on processor array is presented. The strategy represents the heterogeneous processors by computing power array and usage bitmap. The jobs are represented by needed computing power array. A constrained first fit algorithm is devised to map jobs to the chip multiprocessors.
The sharable content object reference model (SCORM) has become a popular standard for sharing and reusing teaching materials in the field of e-learning. However, efficient retrieval of SCORM-compliant learning objects...
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The sharable content object reference model (SCORM) has become a popular standard for sharing and reusing teaching materials in the field of e-learning. However, efficient retrieval of SCORM-compliant learning objects stored on grid environments is a challenging problem. In this paper, a performance-based approach is proposed to retrieve teaching materials in grid environments. The proposed architecture uses the real-time information gathered by a resource-monitoring tool to estimate the dynamically changing performance of each node, for CPU loading and network bandwidth. In addition, a grid testbed is built to implement the model. Experimental results show that the proposed approach can choose the most appropriate site to reduce the retrieval time
Broadband ISDN has made possible a variety of new multimedia services, but also created new problems for congestion control, due to the bursty nature of traffic sources. Lazar and Pacifici (1991) showed that traffic p...
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Broadband ISDN has made possible a variety of new multimedia services, but also created new problems for congestion control, due to the bursty nature of traffic sources. Lazar and Pacifici (1991) showed that traffic prediction is able to alleviate this problem. The traffic prediction model in their framework is a special case of the Box-Jenkins ARIMA model. In this paper, we propose a neural network approach for traffic prediction. A (1,5,1) backpropagation feedforward neural network is trained to capture the linear and nonlinear regularities in several time series. A comparison between the results from the neural network approach and the Box-Jenkins approach is also provided. The nonlinearity used in this paper is chaotic. We have designed a set of experiments to show that a neural network's prediction performance is only slightly affected by the intensity of the stochastic component (noise) in a time series. We have also demonstrated that a neural network's performance should be measured against the variance of the noise, in order to gain more insight into its behavior and prediction performance. Based on experimental results, we then conclude that the neural network approach is an attractive alternative to traditional regression techniques as a tool for traffic prediction.< >
Detection of protein families in large scale database is a difficult but important biological problem. Computational clustering methods can effectively address the problem. Although there exist many clustering algorit...
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Detection of protein families in large scale database is a difficult but important biological problem. Computational clustering methods can effectively address the problem. Although there exist many clustering algorithms, most of them are just based on the threshold. Their computational performances are affected by the weight distribution greatly, and they are only valid for some special networks. A new network clustering algorithm, Markov Finding and Clustering (MFC), is proposed to cluster the proteins into their functionally specific families accurately in this paper. The MFC algorithm makes an improvement in the random walk process and reduces the affection of the noise on the clustering result. It has a good performance on these networks which are not well addressed by existing algorithms sensitive to the noise. Finally, experiments on the protein sequence datasets demonstrate that the algorithm is effective in the detection of protein families and has a better performance than the current algorithms.
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