Abstract
The performance gain obtained by the adaptation of the G-means algorithm for a Cell BE environment using the CellSs framework is described. G-means is a clustering algorithm based on k-means, used to find the number of Gaussian distributions and their centers inside a multi-dimensional dataset. It is normally used for data mining applications, and its execution can be divided into 6 execution steps. This paper analyzes each step to select which of them could be improved. In the implementation, the algorithm was modified to use the specific SIMD instructions of the Cell processor and to introduce parallel computing using the CellSs framework to handle the SPU tasks. The hardware used was an IBM BladeCenter QS22 containing two PowerXCell processors. The results show the execution of the algorithm 60% faster as compared with the non-improved code.
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Foina, A.G., Badia, R.M., Ramirez-Fernandez, J. (2010). G-Means Improved for Cell BE Environment. In: Keller, R., Kramer, D., Weiss, JP. (eds) Facing the Multicore-Challenge. Lecture Notes in Computer Science, vol 6310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16233-6_8
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DOI: https://doi.org/10.1007/978-3-642-16233-6_8
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