You will probably have to split the main.cpp from your others.cu like this:
others.hpp:
void others();
others.cu:
#include "others.hpp"
#include <boost/typeof/std/utility.hpp>
#include <thrust/device_vector.h>
void others() {
thrust::device_vector<int> dv(10);
BOOST_AUTO(iter, dv.begin()); // regular C++
}
main.cpp:
#include "others.hpp"
int main() {
others();
return 0;
}
This particular answer shows that compiling with an officially supported gcc version (as Robert Crovella stated correctly) should work out at least for c++11 code in the main.cpp file:
g++ -std=c++0x -c main.cpp
nvcc -arch=sm_20 -c others.cu
nvcc -lcudart -o test main.o others.o
(tested on Debian 8 with nvcc 5.5 and gcc 4.7.3).
To answer your underlying question: I am not aware that one can use C++11 in .cu files with CUDA 5.5 in Linux (and I was not aware the shown example with host-side C++11 gets properly de-cluttered under MSVC). I even filed a feature request for constexpr support which is still open.
The CUDA programming guide for CUDA 5.5 states:
For the host code, nvcc supports whatever part of the C++ ISO/IEC 14882:2003 specification the host c++ compiler supports.
For the device code, nvcc supports the features illustrated in Code Samples with some restrictions described in Restrictions; it does not support run time type information (RTTI), exception handling, and the C++ Standard Library.
Anyway, it is possible to use some of the C++11 features like auto in kernels, e.g. with boost::auto. As an outlook, other C++11 features like threads may be quite unlikely to end up in CUDA and I heard no official plans about them yet (as of supercomputing 2013).
Shameless plug: If you are interested in more of these tweeks, feel free to have a look in our library libPMacc which provides multi-GPU grid and particle abstractions for simulations. We implemented lambda, a STL-like access concept for 1-3D matrices and other useful stuff there.
All the best, Axel
Update: Since CUDA 7.0 C++11 support in kernels has been added officially. As BenC pointed our correctly, parts of this feature were already silently added in CUDA 6.5.