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CUDA on GPU N-Body
no vote
Application background Implement N-BODY algorithm on GPU. The N-Body simulation problem covers many areas of natural science, from the viewpoint of the universe to the macroscopic fluid dynamics, until the microscopic molecular dynamics. For example through the research around the Galactic dark matter halos of shape and dynamic characteristics to explore the galaxy formation process and need to millions of stars and dark matter between the role of simulation. Modern chemistry and Biophysics in many studies, such as bacterial or plant photosynthetic membrane occurs at the light to chemical energy conversion, description of the chromosomal DNA and protein molecules, need simulating millions of atoms and molecules. Key Technology Two important features of the N-Body problem are: first, the size of the computation is large, because both the size of the universe and the micro scale of the micro scale. Because of the interaction between two particles i
hnucsx
2016-08-23
5
1
CUDA on GPU kMeans
4.0
Application background CUDA on the kMeans to achieve GPU, a cluster analysis.We often come into contact with the clustering analysis. It is a common practice to extract N features, and then put them together to form a N vector, so that a mapping from the original data set to a N dimensional vector space is needed.Can also be applied to image segmentation, etc.. Key Technology CUDA GPU implementation of kMeans, parallel computing, data division and other related technologies.The steps of the algorithm are as follows:1, from the D K random elements, as the K of the cluster of their respective centers.2, calculated the remaining elements to the centers K cluster dissimilarity, these elements will be classified to the lowest cluster dissimilarity.3. According to the results of clustering, the K cluster is calculated. The calculation method is the arithmetic mean of all the elements in the cluster.4, the D of all elements in accordance with the new ce
hnucsx
2016-08-23
0
1
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