scholarly journals Analysis of Parallel Multidimensional Wave Digital Filtering Network on IBM Cell Broadband Engine

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Chien Hsun Tseng
Keyword(s):  
Water System ◽  
Parallel Architecture ◽  
Excellent Performance ◽  
Physical Systems ◽  
Cell Broadband Engine ◽  
Alternative Approach ◽  
Significant Performance ◽  
Shallow Water System ◽  
Speed Up ◽  
Retiming Technique

As an alternative approach for the numerical integration of physical systems, the MDWDF technique has become of importance in the field of numerical analysis due to its attractive features, for example, massive parallelism and high accuracy both inherent in nature. In this study, speed-up efficiencies of a MDWDF network are studied for the linearized shallow water system, which plays an important role in fluid dynamics. To achieve the goal, the full parallelism of the MDWDF network is established in the first place based on the chained MD retiming technique. Following the implementation on the IBM Cell Broadband Engine (Cell/BE), excellent performance of the full parallel architecture is revealed. The IBM Cell/BE containing 1 power processor element (PPE) and 8 synergistic processor elements (SPEs) perfectly fits the architecture of the retimed MDWDF model. Empirical results have demonstrated that the full parallelized model with 8 processors (1PPE + 7SPEs) outperforms the other three models: partial right/left-loop retimed models and the full sequential model with 4× improvements for scheduled grids 51×51. In addition, for scheduled fine grids 201×201, the full parallel model is shown to possess significant performance over these models by up to 7× improvements.

scholarly journals Variations in the surface velocity of an alpine cirque glacier

2018 ◽  
Vol 64 (248) ◽  
pp. 969-976 ◽  
Author(s):  
J. W. SANDERS ◽  
K. M. CUFFEY ◽  
K. R. MACGREGOR ◽  
J. L. KAVANAUGH ◽  
C. F. DOW
Keyword(s):  
Water System ◽  
Heavy Rain ◽  
Temporal Variations ◽  
Surface Velocity ◽  
Channel Network ◽  
Glacier Surface ◽  
Ice Dynamics ◽  
Speed Up ◽  
Water Volumes ◽  
One Year

ABSTRACTFollowing pioneering work in Norway, cirque glaciers have widely been viewed as rigidly rotating bodies. This model is incorrect for basin-filling cirque glaciers, as we have demonstrated at West Washmawapta Glacier, a small glacier in the Canadian Rocky Mountains. Here we report observations at the same glacier that assess whether complex temporal variations of flow also occur. For parts of three summers, we measured daily displacements of the glacier surface. In one year, four short-duration speed-up events were recorded. Three of the events occurred during the intervals of warmest weather, when melt was most rapid; the fourth event occurred immediately following heavy rain. We interpret the speed-up events as manifestations of enhanced water inputs to the glacier bed and associated slip lubrication by increased water volumes and pressures. No further speed-ups occurred in the final month of the melt season, despite warm temperatures and several rainstorms; the dominant subglacial water system likely transformed from one of poorly connected cavities to one with an efficient channel network. The seasonal evolution of hydrology and flow resembles behaviors documented at other, larger temperate glaciers and indicates that analyses of cirque erosion cannot rely on simple assumptions about ice dynamics.

ACCELERATED LEARNING BY ACTIVE EXAMPLE SELECTION

1994 ◽  
Vol 05 (01) ◽  
pp. 67-75 ◽  
Author(s):  
BYOUNG-TAK ZHANG
Keyword(s):  
Neural Networks ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Accelerated Learning ◽  
Training Set ◽  
Alternative Approach ◽  
Speed Up ◽  
Multilayer Neural Networks ◽  
Training Examples ◽  
The Given

Much previous work on training multilayer neural networks has attempted to speed up the backpropagation algorithm using more sophisticated weight modification rules, whereby all the given training examples are used in a random or predetermined sequence. In this paper we investigate an alternative approach in which the learning proceeds on an increasing number of selected training examples, starting with a small training set. We derive a measure of criticality of examples and present an incremental learning algorithm that uses this measure to select a critical subset of given examples for solving the particular task. Our experimental results suggest that the method can significantly improve training speed and generalization performance in many real applications of neural networks. This method can be used in conjunction with other variations of gradient descent algorithms.

scholarly journals A New Well-Balanced Reconstruction Technique for the Numerical Simulation of Shallow Water Flows with Wet/Dry Fronts and Complex Topography

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1661 ◽  
Author(s):  
Zhengtao Zhu ◽  
Zhonghua Yang ◽  
Fengpeng Bai ◽  
Ruidong An
Keyword(s):  
Shallow Water ◽  
Water System ◽  
Reconstruction Technique ◽  
Test Cases ◽  
Bed Topography ◽  
Surface Gradient ◽  
Shallow Water Flows ◽  
Shallow Water System ◽  
The One ◽  
Flow Variables

This study develops a new well-balanced scheme for the one-dimensional shallow water system over irregular bed topographies with wet/dry fronts, in a Godunov-type finite volume framework. A new reconstruction technique that includes flooded cells and partially flooded cells and preserves the non-negative values of water depth is proposed. For the wet cell, a modified revised surface gradient method is presented assuming that the bed topography is irregular in the cell. For the case that the cell is partially flooded, this paper proposes a special reconstruction of flow variables that assumes that the bottom function is linear in the cell. The Harten–Lax–van Leer approximate Riemann solver is applied to evaluate the flux at cell faces. The numerical results show good agreement with analytical solutions to a set of test cases and experimental results.

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