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15 January 2019

Publication details

R. Mancini, A. Ritacco, G. Lanciano, T. Cucinotta. "XPySom: High-Performance Self-Organizing Maps," (to appear) in Proceedings of the 32nd IEEE International Symposium on Computer Architecture and High Performance Computing (IEEE SBAC-PAD 2020), September 8-11, 2020. Porto, Portugal (turned to a virtual on-line event due to the Covid-19 emergency).

Abstract

In this paper, we introduce XPySom, a new open-source Python implementation of the well-known Self-Organizing Maps (SOM) technique. It is designed to achieve high performance on a single node, exploiting widely available Python libraries for vector processing on multi-core CPUs and GP-GPUs. We present results from an extensive experimental evaluation of XPySom in comparison to widely used open-source SOM implementations, showing that it outperforms the other available alternatives. Indeed, our experimentation carried out using the Extended MNIST open data set shows a speed-up of about 7x and 100x when compared to the best open-source multi-core implementations we could find with multi-core and GP-GPU acceleration, respectively, achieving the same accuracy levels in terms of quantization error.

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