Massively parallel processing of spatial statistics

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Abstract
Statistical measures of spatial association have significant computational requirements when large data sets are analysed. In this paper, a measure of spatial association, G(d), is used to illustrate how a massively parallel computer can be used to address the computational requirements of spatial statistical analysis. The statistical algorithms were implemented using two MasPar MP-1 computers, one with 8192 and the other with 16834 processors, and an MP-2 machine with 4096 processors. The results demonstrate that substantial reductions in processing times can be achieved using massively parallel architectures: when compared to a superscalar workstation, speed-up values in excess of 20 were obtained. The design of parallel programmes, however, requires careful planning since many factors under programmer control affect the efficiency of the resulting computations.

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