Iñaki Ucar — written Aug 4, 2017 — source
Sitting on top of R’s external pointers, the
RcppXPtr class provides
a powerful and generic framework for
Passing user-supplied C++ functions
to a C++ backend. This technique is exploited in the
RcppDE package, an
efficient C++ based implementation of the
DEoptim package that
accepts optimisation objectives as both R and compiled functions (see
demo("compiled", "RcppDE") for further details). This solution has a
couple of issues though:
XPtrto R space.
XPtrhandling with RcppXPtrUtils
In a nutshell, RcppXPtrUtils provides functions for dealing with these
two issues: namely,
checkXPtr. As a package author,
you only need to 1) import and re-export
cppXPtr to compile code and
transparently retrieve an
XPtr, and 2) use
checkXPtr to internally
check function signatures.
cppXPtr works in the same way as
Rcpp::cppFunction, but instead of
returning a wrapper to directly call the compiled function from R, it
XPtr to be passed to, unwrapped and called from C++. The
returned object is an R’s
externalptr wrapped into a class called
XPtr along with additional information about the function signature.
'double foo(int a, double b)' <pointer: 0x55c64060de40>
checkXptr function checks the object against a given
signature. If the verification fails, it throws an informative error:
Error in checkXPtr(ptr, "int", c("int", "double")): Bad XPtr signature: Wrong return type 'double', should be 'int'.
Error in checkXPtr(ptr, "int", c("int")): Bad XPtr signature: Wrong return type 'double', should be 'int'. Wrong number of arguments, should be 1'.
Error in checkXPtr(ptr, "int", c("double", "std::string")): Bad XPtr signature: Wrong return type 'double', should be 'int'. Wrong argument type 'int', should be 'double'. Wrong argument type 'double', should be 'std::string'.
First, let us define a templated C++ backend that performs some processing with a user-supplied function and a couple of adapters:
Note that the user-supplied function takes two arguments: one is also user-provided and the other is provided by the backend itself. This core is exposed through the following R function:
Finally, we can compare the
XPtr approach with a pure R-based one,
and with a compiled function wrapped in R, as returned by
Unit: microseconds expr min lq mean median execute(func_r, 1.5) 13812.742 15287.713 16429.4728 16017.6470 execute(func_r_cpp, 1.5) 12150.643 13347.326 14482.0998 14145.5830 execute(func_cpp, 1.5) 288.156 369.646 440.1885 400.6895 uq max neval cld 16818.716 53182.418 100 c 15078.917 22634.887 100 b 445.511 1525.653 100 a