Most efficient way to return Eigen::VectorXi with more than 2^31-1 elements to R

I have a vector x of type Eigen::VectorXi with more than 2^31-1 entries, which I would like to return to R. I can do that by copying x entry-wisely to a new vector of type Rcpp::IntegerVector, but that seems to be quite slow.

I am wondering:

1. whether there is a more efficient workaround;
2. why in the following reproducible example Rcpp::wrap(x) doesn't work.

test.cpp

#include <RcppEigen.h>

// [[Rcpp::depends(RcppEigen)]]

// [[Rcpp::export]]
SEXP foo(const R_xlen_t size) {
  Eigen::VectorXi x(size);
  for (R_xlen_t i = 0; i < size; i++) {
    x(i) = 1;
  }
  return Rcpp::wrap(x);
}
    
// [[Rcpp::export]]
Rcpp::IntegerVector fooSlow(const R_xlen_t size) {
  Eigen::VectorXi x(size);
  for (R_xlen_t i = 0; i < size; i++) {
    x(i) = 1;
  }
  Rcpp::IntegerVector y(size);
  for (R_xlen_t i = 0; i < size; i++) {
    y(i) = x(i);
  }   
  return y;
}

test.R

Rcpp::sourceCpp("./test.cpp")
a <- foo(2^(31)) # Error in foo(2^(31)) : negative length vectors are not allowed
a <- fooSlow(2^(31)) # runs fine but it's slow

Rcpp::wrap is dispatching to a method for Eigen matrices and vectors implemented in RcppEigen. That method doesn't appear to support long vectors, currently. (Edit: It now does; see below.)

The error about negative length is thrown by allocVector3 here. It arises when allocVector3 is called with a negative value for its argument length. My guess is that Rcpp::wrap tries to represent 2^31 as an int, resulting in integer overflow. Maybe this happens here?

In any case, you seem to have stumbled on a bug, so you might consider sharing your example with the RcppEigen maintainers on GitHub. (Edit: Never mind - I've just submitted a patch.) (Edit: Patched now, if you'd like to build RcppEigen from sources [commit 5fd125e or later] in order to update your Rcpp::wrap.)

Attempting to answer your first question, I compared your two approaches with my own based on std::memcpy. The std::memcpy approach supports long vectors and is only slightly slower than Rcpp::wrap.

The std::memcpy approach

The C arrays beneath Eigen::VectorXi x and Rcpp::IntegerVector y have the same type (int) and length (n), so they contain the same number of bytes. You can use std::memcpy to copy that number of bytes from one's memory address to other's without a for loop. The hard part is knowing how to obtain the addresses. Eigen::VectorXi has a member function data that returns the address of the underlying int array. R objects of integer type use INTEGER from the R API, which does the same thing.

Tests

Rcpp::sourceCpp(code = '
#include <RcppEigen.h>
#include <Rinternals.h>

// [[Rcpp::depends(RcppEigen)]]
// [[Rcpp::export]]
Rcpp::IntegerVector f_for(const R_xlen_t n) {
  Eigen::VectorXi x(n);
  for (R_xlen_t i = 0; i < n; ++i) {
    x(i) = i % 10;
  }
  Rcpp::IntegerVector y(n);
  for (R_xlen_t i = 0; i < n; ++i) {
    y(i) = x(i);
  }
  return y;
}

// [[Rcpp::export]]
Rcpp::IntegerVector f_wrap(const R_xlen_t n) {
  Eigen::VectorXi x(n);
  for (R_xlen_t i = 0; i < n; ++i) {
    x(i) = i % 10;
  }
  return Rcpp::wrap(x);
}

// [[Rcpp::export]]
Rcpp::IntegerVector f_memcpy(const R_xlen_t n) {
  Eigen::VectorXi x(n);
  for (R_xlen_t i = 0; i < n; ++i) {
    x(i) = i % 10;
  }
  Rcpp::IntegerVector y(n);
  memcpy(INTEGER(y), x.data(), n * sizeof(int));
  return y;
}
')

n <- 100L
x <- rep_len(0:9, n)
identical(f_for(n),    x) # TRUE
identical(f_wrap(n),   x) # TRUE
identical(f_memcpy(n), x) # TRUE

b <- function(n) microbenchmark::microbenchmark(f_for(n), f_wrap(n), f_memcpy(n), setup = gc(FALSE))

b(2^10)
## Unit: microseconds
##         expr   min     lq     mean median      uq     max neval
##     f_for(n) 6.806 8.5280 15.09497 10.332 11.8900 461.496   100
##    f_wrap(n) 4.469 6.2115 12.60750  8.569  9.7170 435.420   100
##  f_memcpy(n) 4.633 7.0520 13.64193  9.061  9.6965 465.924   100

b(2^20)
## Unit: microseconds
##         expr      min        lq      mean    median       uq      max neval
##     f_for(n) 3094.106 3118.2960 3160.2501 3132.4205 3171.329 3515.996   100
##    f_wrap(n)  864.690  890.0485  912.7006  905.4440  929.593  988.797   100
##  f_memcpy(n)  940.048  971.6590 1001.9805  987.3825 1009.195 1428.235   100

b(2^30)
## Unit: seconds
##         expr      min       lq     mean   median       uq      max neval
##     f_for(n) 3.527164 3.554672 3.575698 3.573021 3.593006 3.693711   100
##    f_wrap(n) 1.119750 1.133130 1.143425 1.139702 1.149030 1.203602   100
##  f_memcpy(n) 1.304877 1.330994 1.343253 1.339099 1.354286 1.422912   100