gstat 1.1-0, now on CRAN, no longer comes with its own functions for matrix factorization and solving systems of equations [1], but now directly uses Lapack (dpotrf and dtrsm) through R's own lapack interface and R_ext/Lapack.h header files.
For global kriging at one location from 10,000 observations, as in library(sp) library(gstat) set.seed(1331) n = 10000 pts = SpatialPoints(cbind(x = runif(n), y = runif(n))) pts$z = runif(n) k <- krige(z~1, pts, pts[1,], vgm(1, "Exp", 1)) I see a speed increase from 120 (gstat 1.0-26) to 46 seconds; using openblas on a 4 core laptop brings this down to 15 seconds - I expect sth similar with MKL/RevoR. For local kriging on large data sets with smaller neighborhoods and many locations, I wouldn't expect large improvements; for global kriging of large data sets to many prediction locations, krige0 may be faster when you use openblas or MKL - as long as things fit in memory. I'd be happy to hear experiences (positive and negative), or otherwise reactions or questions. [1] it formerly used meschach, http://homepage.math.uiowa.edu/~dstewart/meschach/ -- Edzer Pebesma Institute for Geoinformatics (ifgi), University of Münster, Heisenbergstraße 2, 48149 Münster, Germany; +49 251 83 33081 Journal of Statistical Software: http://www.jstatsoft.org/ Computers & Geosciences: http://elsevier.com/locate/cageo/ Spatial Statistics Society http://www.spatialstatistics.info
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