Speed-up a parallel process calculating a mann-kendall test over a huge dataset in R

Question

Let's assume to have a large dataset of climatic data at monthly time steps for a large number of points in the world. Then dataset is shaped as a data.frame of the type:

lon, lat, data_month_1_yr_1, ..., data_month_12_yr_100

Example:

set.seed(123)
data<- data.frame(cbind(runif(10000,-180,180), runif(10000,-90,90))
, replicate(1200, runif(10000,0,150)))

I would like to perform a Mann-Kendall test (usingtrend::mk.test) over the monthly time series of each of the spatial points and get the main statistics in a data.frame. In order to speed up this very long process I parallelized my code and wrote something like the following:

coords<-data[,1:2] #get the coordinates out of the initial dataset
names(coords)<-c("lon","lat") 
data_t<- as.data.frame(t(data[,3:1202])) #each column is now the time series associated to a point
data_t$month<-rep(seq(1,12,1),100) # month index as last column of the data frame
# start the parallel processing

library(foreach)
library(doParallel)

cores=detectCores() #count cores
cl <- makeCluster(cores[1]-1) #take all the cores minus 1 not to overload the pc
registerDoParallel(cl)

mk_out<- foreach(m=1:12, .combine = rbind) %:%
         foreach (a =1:10000, .combine = rbind) %dopar% {

           data_m<-data_t[which(data_t$month==m),]
           library(trend) #need to load this all the times otherwise I get an error (don't know why)
           test<-mk.test(data_m[,a])
           mk_out_temp <- data.frame("lon"=coords[a,1],
                                     "lat"=coords[a,2],
                                     "p.value" = as.numeric(test$p.value),
                                     "z_stat" = as.numeric(test$statistic),
                                     "tau" = as.numeric(test$estimates[3]),
                                     "month"= as.numeric(m))
           mk_out_temp
}
stopCluster(cl)

head(mk_out)
         lon       lat    p.value     z_stat         tau month
1  -76.47209 -34.09350 0.57759040 -0.5569078 -0.03797980     1
2  103.78985 -31.58639 0.64436238  0.4616081  0.03151515     1
3  -32.76831  66.64575 0.11793238  1.5635113  0.10626263     1
4  137.88627 -30.83872 0.79096910  0.2650524  0.01818182     1
5  158.56822 -67.37378 0.09595919 -1.6647673 -0.11313131     1
6 -163.59966 -25.88014 0.82325630  0.2233588  0.01535354     1

This runs just fine and gives me exactly what I am after: a matrix reporting the M-K statistics for each combination of coordinates and month. Although the process is parallelized, however, the computation takes still a considerable amount of time.

Is there a way to speed up this process? Any room for using functions from the applyfamily?

Oliver · Accepted Answer

You note that you have already fixed your problem. Is obtainable using one of the following steps:

1: Copy the necessary objects to the foreach loops using .packages and .export. This ensures that each instance will not clash when trying to access the same memory.

2: Utilizing high performance libraries such as tidyverse of data.table to perform subsetting and computation.

The latter is a bit more complicated but yielded a massive boost to performance on my tiny tiny laptop. (Performing all calculations i roughly 1.5 minutes for the entire dataset.)

Below is my added code. Note that i replaced foreach with a single parLapply function from the parallel package.

set.seed(123)
data<- data.frame(cbind(runif(10000,-180,180), runif(10000,-90,90))
                  , replicate(1200, runif(10000,0,150)))

coords<-data[,1:2] #get the coordinates out of the initial dataset
names(coords)<-c("lon","lat") 
data_t<- as.data.frame(t(data[,3:1202])) #each column is now the time series associated to a point
data_t$month<-rep(seq(1,12,1),100) # month index as last column of the data frame
# start the parallel processing

library(data.table)
library(parallel)
library(trend)
setDT(data_t)
setDT(coords)
cores=detectCores() #count cores
cl <- makeCluster(cores[1]-1) #take all the cores minus 1 not to overload the pc

#user  system elapsed 
#17.80   35.12   98.72
system.time({
  test <- data_t[,parLapply(cl, 
                            .SD, function(x){
                              (
                                unlist(
                                  trend::mk.test(x)[c("p.value","statistic","estimates")]
                                )
                               )
                              }
                            ), by = month] #Perform the calculations across each month
  #create a column that indicates what each row is measuring
  rows <- rep(c("p.value","statistic.z","estimates.S","estimates.var","estimates.tau"),12)

  final_tests <- dcast( #Cast the melted structure to a nice form
                      melt(cbind(test,rowname = rows), #Melt the data for a better structure
                        id.vars = c("rowname","month"), #Grouping variables
                        measure.vars = paste0("V",seq.int(1,10000))), #variable names
                      month + variable ~ rowname, #LHS groups the data along rows, RHS decides the value columns
                      value.var = "value", #Which column contain values? 
                      drop = TRUE) #should we drop unused columns? (doesnt matter here)
  #rename the columns as desired
  names(final_tests) <- c("month","variable","S","tau","var","p.value","z_stat")
  #finally add the coordinates
  final_tests <- cbind(final_form,coords) 
})

Speed-up a parallel process calculating a mann-kendall test over a huge dataset in R

Answers (2)

Related Questions