Scalable parallel algorithms for surface fitting and data mining

Peter Christen, Markus Hegland, Ole Nielsen, Stephen Roberts, Peter Strazdins, Irfan Altas

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper presents scalable parallel algorithms for high-dimensional surface fitting and predictive modelling which are used in data mining applications. These algorithms are based on techniques like finite elements, thin plate splines, wavelets and additive models. They all consist of two steps: First, data is read from secondary storage and a linear system is assembled. Secondly, the linear system is solved. The assembly can be done with almost no communication and the size of the linear system is independent of the data size. Thus the presented algorithms are both scalable with the data size and the number of processors.
Original languageEnglish
Pages (from-to)941-961
Number of pages21
JournalParallel Computing
Volume27
Issue number7
DOIs
Publication statusPublished - 2001

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    Christen, P., Hegland, M., Nielsen, O., Roberts, S., Strazdins, P., & Altas, I. (2001). Scalable parallel algorithms for surface fitting and data mining. Parallel Computing, 27(7), 941-961. https://doi.org/10.1016/S0167-8191(01)00076-X