Are you parallelizing your raster operations? You should!

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If you plan to do anything with the raster package you should definitely consider parallelize all your processes, especially if you are working with very large image files. I couldn’t find any blog post describing how to parallelize with the raster package (it is well documented in the package documentation, though). So here my notes.

Load some example data

Let’s first get some raster data from here, any file will do but I’m using the Cambodian population data for 2015 (KHM_ppp_v2b_2015_UNadj).

library(raster)
khm_pop.r <- 
  raster("~/Downloads/KHM_ppp_v2b_2015_UNadj/KHM_ppp_v2b_2015_UNadj.tif")

We can plot it with

library(rasterVis)
library(viridis)
library(ggplot2)
rasterVis::gplot(khm_pop.r) + 
  geom_tile(aes(fill = log(value)))  +
  viridis::scale_fill_viridis(direction = -1, 
                              na.value='#FFFFFF00') + 
  theme_bw()

Projection

Now, let’s first project the raster without any parallelization.

start_time <- Sys.time()
res1 <- 
  projectRaster(khm_pop.r,
                crs = '+proj=utm +zone=48 +datum=WGS84 +units=m +no_defs')
end_time <- Sys.time()
end_time - start_time
## Time difference of 1.088329 mins
rasterVis::gplot(res1) + 
  geom_tile(aes(fill = log(value)))  +
  viridis::scale_fill_viridis(direction = -1, 
                              na.value='#FFFFFF00') + 
  theme_bw()

And now let’s parallelize the process. There are two approaches to parallelization with raster objects (do ?clusterR for the documentation of the package mantainers):

  1. By including the raster function between a beginCluster() and an endCluster().
  2. By using clusterR() like in clusterR(x, fun, args=NULL, cl=mycluster), where mycluster is a cluster object generated for example by getCluster().

Yet clusterR() doesn’t work with merge, crop, mosaic, disaggregate, aggregate, resample, projectRaster, focal, distance, buffer and direction.

Let’s try the first approach first.

start_time <- Sys.time()
  beginCluster()
## 4 cores detected, using 3
  res2 <- 
    projectRaster(khm_pop.r, 
                  crs = '+proj=utm +zone=48 +datum=WGS84 +units=m +no_defs')
## Using cluster with 3 nodes
  endCluster() 
end_time <- Sys.time()
end_time - start_time
## Time difference of 1.548856 mins
rasterVis::gplot(res2) + 
  geom_tile(aes(fill = log(value)))  +
  viridis::scale_fill_viridis(direction = -1, na.value='#FFFFFF00') + 
  theme_bw()

Maths

To test the second approach, let’s use the calc() and sqrt() functions, first without parallelization:

start_time <- Sys.time()
calc(khm_pop.r, sqrt)
## class       : RasterLayer 
## dimensions  : 5205, 6354, 33072570  (nrow, ncol, ncell)
## resolution  : 0.0008333, 0.0008333  (x, y)
## extent      : 102.3375, 107.6323, 10.35008, 14.6874  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
## data source : in memory
## names       : layer 
## values      : 0.02269337, 42.87014  (min, max)
end_time <- Sys.time()
end_time - start_time
## Time difference of 3.316296 secs

and then with parallelization, this time with clusterR():

start_time <- Sys.time()
beginCluster()
## 4 cores detected, using 3
clusterR(khm_pop.r, sqrt)
## class       : RasterLayer 
## dimensions  : 5205, 6354, 33072570  (nrow, ncol, ncell)
## resolution  : 0.0008333, 0.0008333  (x, y)
## extent      : 102.3375, 107.6323, 10.35008, 14.6874  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
## data source : in memory
## names       : layer 
## values      : 0.02269337, 42.87014  (min, max)
endCluster()
end_time <- Sys.time()
end_time - start_time
## Time difference of 16.49228 secs
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