Hello Roger,
Thanks for pointing to the excellent RANN package. It considerably speeds up
the search for nearest neighbors. Now I'm able to reduce the run time by
several folds.
D
________________________________
From: Roger Bivand <roger.biv...@nhh.no>
Sent: Thursday, August 18, 2016 1:55:18 AM
To: David Wang; Hodgess, Erin; r-sig-geo
Subject: Re: [R-sig-Geo] Parallel processing a list of SpatialPoints
Consider using the RANN package if your points are represented by projected
(planar) coordinates.
Hope this helps,
Roger Bivand
Norwegian School of Economics
Bergen, Norway
Fra: David Wang
Sendt: torsdag 18. august, 00.20
Emne: Re: [R-sig-Geo] Parallel processing a list of SpatialPoints
Til: Hodgess, Erin, r-sig-geo
Hi Erin et al., Thanks for the questions. I use Windows 7 on a 8-core PC. I
also have access to a Linux cluster. But in most cases, I have found using
multiple cores with doParallel and foreach on the PC gives me sufficient
speed-up. I'd not bother with mpi in this case either. D
________________________________ From: Hodgess, Erin Sent: Wednesday, August
17, 2016 6:11:31 PM To: David Wang; r-sig-geo Subject: RE: Parallel processing
a list of SpatialPoints Do you know Mpi, by any chance, please? What kind of
machine/operating system are you using, please? Thanks, Erin Erin M. Hodgess
Associate Professor Department of Mathematics and Statistics University of
Houston - Downtown mailto: hodge...@uhd.edu
________________________________________ From: R-sig-Geo
[r-sig-geo-boun...@r-project.org] on behalf of David Wang [dw2...@outlook.com]
Sent: Wednesday, August 17, 2016 5:00 PM To: r-sig-geo Subject: [R-sig-Geo]
Parallel processing a list of SpatialPoints Hello, I have a list of!
SpatialPointsDataFrame objects that represent feature centers along the time
axis. For example, let's say the list is centers. centers[[1]] are the points
at t = 1, centers[[2]] are the points at t = 2, and so on. Now for every point
at t, I need to link it to, if any, the nearest point within a search radius at
the next time step t + 1. The procedure is applied to all time steps, and the
result are tracks that connect selected feature centers in time. I have
implemented a solution using igraph package, with every center represented by a
vertex and every link by an edge. This way, tracks are simply connected
components of the graph. Now because the number of centers and links are quite
large, the algorithm takes a while to run. But since finding the nearest
neighbors between t and t + 1 and between t + 1 and t + 2 are independent, I
think parallel processing should be possible, although I haven't figured out
how. Does anyone here happen to have a pointer or ! two? I work o!
n a multicore PC. Here is my code snippet: g 0 & length(V(g)[id == k])
> 0) { v1
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