Exploiting Parallelization in Spatial Statistics: an Applied Survey using R
Computing tasks may be parallelized top-down by splitting into per-node chunks when the tasks permit this kind of division, and particularly when there is little or no need for communication between the nodes. Another approach is to parallelize bottom-up, by the substitution of multi-threaded low-level functions for single-threaded ones in otherwise unchanged user-level functions. This survey examines the timings of typical spatial data analysis tasks across a range of data sizes and hardware under different combinations of these two approaches. Conclusions are drawn concerning choices of alternatives for parallelization, and attention is drawn to factors conditioning those choices.
|Date of creation:||06 Oct 2010|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +47 55 959 277
Fax: 5595 9100
Web page: http://www.nhh.no/sam/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bivand, Roger & Müller, Werner G. & Reder, Markus, 2009. "Power calculations for global and local Moran's," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2859-2872, June.
When requesting a correction, please mention this item's handle: RePEc:hhs:nhheco:2010_025. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dagny Hanne Kristiansen)
If references are entirely missing, you can add them using this form.