Abstract
We have introduced a new mapping and a block strategy for the SOFM allowing to increase the speed-up of the parallel implementation. This method has been implemented on a iPSC/860 MIMD parallel computer. It performs well on learning dots randomly chosen in the unit square, i.e. it learns as well as the classical algorithm. Its performances outperform the parallel implementation of the classical algorithm since the achieved speed-up is increased from 2–3 to 7. The SOFM has been implemented on a SIMD computer, namely the MasPar MPI exhibiting an almost perfect matching between Kohonen maps, the grid architecture and the synchronous programming model of the MasPar system
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References
Chwan-Hwa Wu, Russel Hodges, Chia-Jiu Wang, “Parallelizing the Self-Organizing Feature Map on multiprocessor systems”, Parallel Computing 17, pp. 821–832, 1991.
A. Ultsch, H.P. Siemon, “Exploratory Data Analysis: Using Kohonen networks on Transputers”, Dpt Comp. Sc., University of Dortmund, Dec. 89.
Teuvo Kohonen, “Self-Organization and Associative Memory”, Springer-Verlag, Berlin, 1984.
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© 1992 Springer-Verlag Berlin Heidelberg
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Demian, V., Mignot, J.C. (1992). Implementation of the self-organizing feature map on parallel computers. In: Bougé, L., Cosnard, M., Robert, Y., Trystram, D. (eds) Parallel Processing: CONPAR 92—VAPP V. VAPP CONPAR 1992 1992. Lecture Notes in Computer Science, vol 634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55895-0_483
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DOI: https://doi.org/10.1007/3-540-55895-0_483
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