Boost the speed of R calls from Rcpp

Some motivation

Here is a small comparison of the old and the new APIs. The following toy example just calls an R function N times from C++. A pure R for loop is also provided as a reference.

Rcpp::cppFunction('
  void old_api(Function func, int n) {
    for (int i=0; i<n; i++) func();
  }
')

Rcpp::cppFunction(plugins = "unwindProtect", '
  void new_api(Function func, int n) {
    for (int i=0; i<n; i++) func();
  }
')

reference <- function(func, N) {
  for (i in 1:N) func()
}

func <- function() 1
N <- 1e6

system.time(old_api(func, N))

##    user  system elapsed 
##  17.863   0.006  17.950

system.time(new_api(func, N))

##    user  system elapsed 
##   0.289   0.000   0.290

system.time(reference(func, N))

##    user  system elapsed 
##   0.216   0.000   0.217

Obviously, there is still some penalty compared to not switching between domains, but the performance gain with respect to the old API is outstanding.

A real-world example

This is a quite heavy simulation of an M/M/1 system using simmer:

library(simmer)

system.time({
  mm1 <- trajectory() %>%
    seize("server", 1) %>%
    timeout(function() rexp(1, 66)) %>%
    release("server", 1)

  env <- simmer() %>%
    add_resource("server", 1) %>%
    add_generator("customer", mm1, function() rexp(50, 60), mon=F) %>%
    run(10000, progress=progress::progress_bar$new()$update)
})

In my system, it takes around 17 seconds with the old API. The new API makes it in less than 5 seconds. As a reference, if we avoid R calls in the timeout activity and precompute all the arrivals instead of defining a dynamic generator, i.e.:

system.time({
  input <- data.frame(
    time = rexp(10000*60, 60),
    service = rexp(10000*60, 66)
  )

  mm1 <- trajectory() %>%
    seize("server", 1) %>%
    timeout_from_attribute("service") %>%
    release("server", 1)

  env <- simmer() %>%
    add_resource("server", 1) %>%
    add_dataframe("customer", mm1, input, mon=F, batch=50) %>%
    run(10000, progress=progress::progress_bar$new()$update)
})

then the simulation takes around 2.5 seconds.

How to start using this feature

First of all, you need R >= 3.5.0 and Rcpp >= 0.12.18 installed. Then, if you are a user, the easiest way to enable this globally is to add CPPFLAGS += -DRCPP_USE_UNWIND_PROTECT to your ~/.R/Makevars. Packages installed or re-installed, as well as functions compiled with Rcpp::sourceCpp and Rcpp::cppFunction, will benefit from this performance gains. If you are a package maintainer, you can add -DRCPP_USE_UNWIND_PROTECT to your package’s PKG_CPPFLAGS in src/Makevars. Alternatively, there is a plugin available, so this flag can be enabled by adding [[Rcpp::plugins(unwindProtect)]] to one of your source files.

Note that this is fairly safe according to reverse dependency checks, but there might be still issues in some packages. But the sooner we start testing this feature and reporting possible issues, the sooner it will be enabled by default in Rcpp.