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High-Performance Dynamic Quantum Clustering on Graphics Processors
University of Borås, Swedish School of Library and Information Science.
2013 (English)In: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 233, 262- p.Article in journal (Refereed)
Abstract [en]

Clustering methods in machine learning may benefit from borrowing metaphors from physics. Dynamic quantum clustering associates a Gaussian wave packet with the multidimensional data points and regards them as eigenfunctions of the Schrödinger equation. The clustering structure emerges by letting the system evolve and the visual nature of the algorithm has been shown to be useful in a range of applications. Furthermore, the method only uses matrix operations, which readily lend themselves to parallelization. In this paper, we develop an implementation on graphics hardware and investigate how this approach can accelerate the computations. We achieve a speedup of up to two magnitudes over a multicore CPU implementation, which proves that quantum-like methods and acceleration by graphics processing units have a great relevance to machine learning.

Place, publisher, year, edition, pages
Academic Press , 2013. Vol. 233, 262- p.
Keyword [en]
high-performance computing, quantum physics, machine learning, quantum-like learning, clustering, High-performane computing
National Category
Other Computer and Information Science
Research subject
Library and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-1425DOI: 10.1016/j.jcp.2012.08.048ISI: 000311644200014Local ID: 2320/11759OAI: oai:DiVA.org:hb-1425DiVA: diva2:869480
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2017-12-01

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