Why does climate4R use too many cpu? TOO SLOW

[louwangzhiyuwhy]

options(java.parameters = "-Xmx8g")

library(climate4R.UDG) library(loadeR) library(loadeR.2nc) library(transformeR) library(climate4R.datasets) library(downscaleR) library(visualizeR) library(VALUE) library(climate4R.value)

vars <- c("var151","var165","var166") #psl; uas; vas varp <- c("var131@85000","var132@85000","var129@50000") #131-ua; 132-va; 130-ta; 129-zg; grid.list <- lapply(vars, function(x) { loadGridData(dataset = "/home/inspur/working/climate4r/ERA-I/box_surface_interim_1979_2018.nc", var = x, years = 1990:2018) } ) grid.listp <- lapply(varp, function(x) { loadGridData(dataset = "/home/inspur/working/climate4r/ERA-I/box_pressure_interim_1979_2018.nc", var = x, years = 1990:2018) } ) pred <- downscaleCV(xs, wsobs, folds = 3, sampling.strategy = "kfold.chronological", scaleGrid.args = list(type = "standardize"), method = "GLM", prepareData.args = list( "spatial.predictors" = list(which.combine = getVarNames(xs), v.exp = 0.9)))

the downscaleCV method uses 12603% of one CPU and TOO SLOW why? A 30*40 box of ERA-I dataset was used to downscaling dataset is small enough why take so many resource??? here is the cenos7 top result: PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
553757 inspur 20 0 105.6g 74.1g 28200 R 12603 7.4 440:22.97 R