In this paper, we introduce XPySom, a new opensource 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.

XPySom : High-Performance Self-Organizing Maps

Lanciano, Giacomo;
2020

Abstract

In this paper, we introduce XPySom, a new opensource 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.
2020
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
PROCEEDINGS SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING
IEEE COMPUTER SOC
978-1-7281-9924-5
self-organizing maps (SOMs); performance comparison; experimental evaluation; GP-GPU acceleration
   A Model-driven development framework for highly Parallel and EneRgy-Efficient computation supporting multi-criteria optimisation
   AMPERE
   European Commission
   Horizon 2020 Framework Programme
   871669
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/131262
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