Emulab is an emulation-based network test-bed constructed for research and education. It is used for building and testing applications in fields of information security and computer network. The application of Emulab ...
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ISBN:
(纸本)9781479965137
Emulab is an emulation-based network test-bed constructed for research and education. It is used for building and testing applications in fields of information security and computer network. The application of Emulab is being extended to parallel processing of scientific data. The DNA sequence search is one of major research areas in the bio-informatics. With the high-performance computing, the biologists can get their results easily and faster. mpiBLAST and mr-mpi-blast can process DNA sequence alignment on parallel computer. In this research, the research environments for mpiBLAST and mr-mpi-blast are built on Emulab. Also, the DNA sequence alignment is performed with NCBI database. This research shows that the Emulab is an effective environment for the research of the bio-informatics.
In the design of fast arithmetic circuits, the two's complement number representation can be alternatively replaced by a signed digit number representation. Compared to standard full adders used in two's compl...
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ISBN:
(纸本)9781479943241
In the design of fast arithmetic circuits, the two's complement number representation can be alternatively replaced by a signed digit number representation. Compared to standard full adders used in two's complement arithmetic, signed digit adder cells offer the potential for improved performance. Designing an efficient signed digit adder cell leads to the problem of analyzing 2 to the power of 44 truth tables originating from different signed digit encodings. Since different digit encodings can produce identical truth tables, it is favorable to reduce this large number of truth tables by identifying identical ones. We introduce a novel approach for the solution of this problem using the MapReduce programming model. We take a step towards solving this problem using three different implementations of MapReduce (Hadoop, Disco, and mr-mpi) and compare their performance on an Opteron-based cluster using up to 64 physical cores.
In data mining, k-means is a method of cluster analysis using the nearest mean. It has been successfully used in various topics, ranging from market segmentation, computer vision, geostatistics, and astronomy to agric...
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ISBN:
(纸本)9781479983421
In data mining, k-means is a method of cluster analysis using the nearest mean. It has been successfully used in various topics, ranging from market segmentation, computer vision, geostatistics, and astronomy to agriculture. But k-means like clustering is not easy to apply MapReduce model due to the iterative manner that can happen the stagger map tasks with high likelihood. This paper presents the result of performance evaluation of K-means application running on Twister and Hadoop framework. We report how to design a MapReduce application to organize the objects of dataset into k partitions. This approach provides the way to cluster a dataset by Hadoop, the MapReduce frameworks in a parallel manner.
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