Executing `koma` in Parallel
parallel.Rmd
library(koma)Introduction
This vignette demonstrates how to use the future::plan
function to run koma package functions either sequentially
or in parallel, depending on your operating system and other
preferences.
For more details, see the future package documentation: https://cran.r-project.org/web/packages/future/future.pdf
Preliminaries
Install the future and parallelly
packages:
install.packages("future")
install.packages("parallelly")We will use the simulated data contained in koma to
illustrate the use of future::plan. The setup is the
following:
library(koma)
equations <- "consumption ~ gdp + consumption.L(1),
investment ~ investment.L(1),
exports ~ world_gdp + exports.L(1),
imports ~ domestic_demand + imports.L(1),
prices ~ exchange_rate + oil_price + prices.L(1),
interest_rate ~ prices + interest_rate_germany + prices.L(1),
gdp == 0.6*consumption + 0.6*domestic_demand + 0.5*exports - 0.4*imports,
domestic_demand == 0.6*consumption + 0.4*investment"
exogenous_variables <- c("world_gdp", "interest_rate_germany", "exchange_rate", "oil_price")
dates <- list(
estimation = list(start = c(1996, 1), end = c(2019, 4)),
forecast = list(start = c(2023, 1), end = c(2023, 4))
)
sys_eq <- system_of_equations(equations, exogenous_variables)
series <- names(small_open_economy)
series <- series[!series %in% c("interest_rate", "interest_rate_germany")]
ts_data <- lapply(series, function(x) {
as_ets(small_open_economy[[x]],
series_type = "level", method = "diff_log"
)
})
names(ts_data) <- series
ts_data$interest_rate <- as_ets(small_open_economy$interest_rate,
series_type = "rate", method = "none"
)
ts_data$interest_rate_germany <- as_ets(small_open_economy$interest_rate_germany,
series_type = "rate", method = "none"
)Sequential Execution
By default, R executes code sequentially. For example:
out_estimation <- estimate(ts_data, sys_eq, dates)Parallel Execution with future::plan
To enable parallel execution, you can use future::plan.
The type of parallel execution depends on your operating system or
whether you are running the code on a cluster.
Worker Setup
You need to define the number of workers that your parallel job to run on. Here we use all cores except one.
# Get the available workers
workers <- parallelly::availableCores(omit = 1)