加快在R中庞大数据集上计算mann-kendall检验的并行过程

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 (using trend::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 MK 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 apply family?

Answer 1

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) 
})

Answer 2

At the end the problem was easily addressed by replacing the second loop with a lapply function (inspired by this answer ). The execution time is now contained to just few seconds. Vectorizing remains the best solution to execution times in R (see this post and this )

I share the final code here below for reference:

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]
names(coords)<-c("lon","lat")
data_t<- as.data.frame(t(data[,3:1202]))
data_t$month<-rep(seq(1,12,1),100)


library(foreach)
library(doParallel)

cores=detectCores()
cl <- makeCluster(cores[1]-1) #take all the cores minus 1
registerDoParallel(cl)

mk_out<- foreach(m=1:12, .combine = rbind) %dopar% {
    data_m<-data_t[which(data_t$month==m),]
    library(trend)
    mk_out_temp <- do.call(rbind,lapply(data_m[1:100],function(x)unlist(mk.test(x))))
    mk_out_temp <-cbind(coords,mk_out_temp,rep(m,dim(coords)[1]))
    mk_out_temp
  }
stopCluster(cl)


head(mk_out)

head(mk_out)
         lon       lat data.name            p.value        statistic.z null.value.S parameter.n estimates.S estimates.varS
1  -76.47209 -34.09350         x  0.577590398263635 -0.556907839290681            0         100        -188         112750
2  103.78985 -31.58639         x  0.644362383361713  0.461608102085858            0         100         156         112750
3  -32.76831  66.64575         x  0.117932376736468   1.56351131351662            0         100         526         112750
4  137.88627 -30.83872         x   0.79096910003836  0.265052394100912            0         100          90         112750
5  158.56822 -67.37378         x 0.0959591933285242  -1.66476728429674            0         100        -560         112750
6 -163.59966 -25.88014         x  0.823256299016955  0.223358759073802            0         100          76         112750
       estimates.tau alternative                  method              pvalg rep(m, dim(coords)[1])
1 -0.037979797979798   two.sided Mann-Kendall trend test  0.577590398263635                      1
2 0.0315151515151515   two.sided Mann-Kendall trend test  0.644362383361713                      1
3  0.106262626262626   two.sided Mann-Kendall trend test  0.117932376736468                      1
4 0.0181818181818182   two.sided Mann-Kendall trend test   0.79096910003836                      1
5 -0.113131313131313   two.sided Mann-Kendall trend test 0.0959591933285242                      1
6 0.0153535353535354   two.sided Mann-Kendall trend test  0.823256299016955                      1