Speed test

Speed test

When developing new features, I try to check how changes affect speed for estimating parameters and conducting bias-correction. Below, I show code for doing this using the simplified user-interface (in this case using the AFSC virtual machine):

# Download release number 3.0.0; its useful for reproducibility to use a specific release number
devtools::install_github("james-thorson/VAST", ref="3.0.0")

# Set local working directory (change for your machine)
setwd( "C:/Users/james.thorson.vm/Desktop/Speed_test" )

# Load packages
library(TMB)
library(VAST)

# load data set
# see `?load_example` for list of stocks with example data
# that are installed automatically with `FishStatsUtils`.
example = load_example( data_set="EBS_pollock" )

# Make settings
settings = make_settings( n_x=50, Region=example$Region, purpose="index",
  strata.limits=example$strata.limits )

# Simulation settings
n_replicates = 5
nx_set = c(25, 50, 100, 200, 400)
version_set = c("VAST_v7_0_0", "VAST_v8_0_0", "VAST_v8_0_0")
fine_set = c(FALSE, FALSE, TRUE)
biascorrect_set = c(FALSE, TRUE)
Speed_rxcb = array( NA, dim=c(n_replicates,length(nx_set),length(version_set),length(biascorrect_set)) )
dimnames(Speed_rxcb) = list( 1:n_replicates,paste0("nx=",nx_set), paste(version_set,"-fine=",fine_set,sep=""),
  paste0("biascorr=",biascorrect_set) )

# Loop
for( rI in 1:dim(Speed_rxcb)[1] ){
for( xI in 1:dim(Speed_rxcb)[2] ){
for( cI in 1:dim(Speed_rxcb)[3] ){
for( bI in 1:dim(Speed_rxcb)[4] ){
  # Set seed to conserve across xI and cI, and across runs of the experiment
  set.seed(rI)

  # Settings for each configuration cI
  settings$Version = version_set[cI]
  settings$fine_scale = fine_set[cI]
  settings$n_x = nx_set[xI]
  settings$bias.correct = biascorrect_set[bI]

  # Run model
  fit = fit_model( "settings"=settings, "Lat_i"=example$sampling_data[,'Lat'],
    "Lon_i"=example$sampling_data[,'Lon'], "t_i"=example$sampling_data[,'Year'],
    "c_i"=rep(0,nrow(example$sampling_data)), "b_i"=example$sampling_data[,'Catch_KG'],
    "a_i"=example$sampling_data[,'AreaSwept_km2'], "v_i"=example$sampling_data[,'Vessel'] )

  # Record run-time
  Speed_rxcb[rI,xI,cI,bI] = as.double( fit$parameter_estimates$time_for_run, units="mins")

  # Save
  save( Speed_rxcb, file="Speed_rxcb.RData" )
}}}}

apply( Speed_rxcb, MARGIN=2:4, FUN=mean, na.rm=TRUE )

This exercise shows that V8.0.0 is only 10-20% slower than previous versions when using fine_scale=FALSE and perhaps 50% slower when using fine_scale=TRUE without bias-correction. However, using fine_scale=TRUE and bias-correction, it is more like 5-10 fold slower than previous versions.