Decreasing the power consumption of content-addressable memory in the dataflow parallel computing system

Author(s):  
N.N. Levchenko ◽  
A.S. Okunev ◽  
D.E. Yakhontov ◽  
D.N. Zmejev
Author(s):  
U. Kliegis ◽  
R. Neumann ◽  
Th. Kortmann ◽  
W. Schwesig ◽  
R. Mittelstädt ◽  
...  

2014 ◽  
Vol 556-562 ◽  
pp. 4746-4749
Author(s):  
Bin Chu ◽  
Da Lin Jiang ◽  
Bo Cheng

This paper concerns about Large-scale mosaic for remote sensed images. Base on High Performance Computing system, we offer a method to decompose the problem and integrate them with logical and physical relationship. The mosaic of Large-scale remote sensed images has been improved both at performance and effectiveness.


2013 ◽  
Vol 756-759 ◽  
pp. 2583-2587 ◽  
Author(s):  
Zi Yang Han ◽  
Feng Ying Wang ◽  
Ping Sun ◽  
Zheng Yu Li

There are so many Deep Webs in Internet, which contains a large amount of valuable data, This paper proposes a Deep Web data extraction and service system based on the principle of cloud technology. We adopt a kind of multi-node parallel computing system structure and design a task scheduling algorithm in the data extraction process, in above foundation, balance the task load of among nodes to accomplish data extraction rapidly; The experimental results show that cloud parallel computing and dispersed network resources are used to extract data in Deep Web system is valid and improves the data extraction efficiency of Deep Web and service quality.


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