Polymorphism and derived classes in CUDA / CUDA Thrust

I am not going to attempt to answer everything in this question, it is just too large. Having said that here are some observations about the code you posted which might help:

• The GPU side new operator allocates memory from a private runtime heap. As of CUDA 6, that memory cannot be accessed by the host side CUDA APIs. You can access the memory from within kernels and device functions, but that memory cannot be accessed by the host. So using new inside a thrust device functor is a broken design that can never work. That is why your "vector of pointers" model fails.
• Thrust is fundamentally intended to allow data parallel versions of typical STL algorithms to be applied to POD types. Building a codebase using complex polymorphic objects and trying to cram those through Thrust containers and algorithms might be made to work, but it isn't what Thrust was designed for, and I wouldn't recommend it. Don't be surprised if you break thrust in unexpected ways if you do.
• CUDA supports a lot of C++ features, but the compilation and object models are much simpler than even the C++98 standard upon which they are based. CUDA lacks several key features (RTTI for example) which make complex polymorphic object designs workable in C++. My suggestion is use C++ features sparingly. Just because you can do something in CUDA doesn't mean you should. The GPU is a simple architecture and simple data structures and code are almost always more performant than functionally similar complex objects.

Having skim read the code you posted, my overall recommendation is to go back to the drawing board. If you want to look at some very elegant CUDA/C++ designs, spend some time reading the code bases of CUB and CUSP. They are both very different, but there is a lot to learn from both (and CUSP is built on top of Thrust, which makes it even more relevant to your usage case, I suspect).

I completely agree with @talonmies answer. (e.g. I don't know that thrust has been extensively tested with polymorphism.) Furthermore, I have not fully parsed your code. I post this answer to add additional info, in particular that I believe some level of polymorphism can be made to work with thrust.

A key observation I would make is that it is not allowed to pass as an argument to a __global__ function an object of a class with virtual functions. This means that polymorphic objects created on the host cannot be passed to the device (via thrust, or in ordinary CUDA C++). (One basis for this limitation is the requirement for virtual function tables in the objects, which will necessarily be different between host and device, coupled with the fact that it is illegal to directly take the address of a device function in host code).

However, polymorphism can work in device code, including thrust device functions.

The following example demonstrates this idea, restricting ourselves to objects created on the device although we can certainly initialize them with host data. I have created two classes, Triangle and Rectangle, derived from a base class Polygon which includes a virtual function area. Triangle and Rectangle inherit the function set_values from the base class but replace the virtual area function.

We can then manipulate objects of those classes polymorphically as demonstrated here:

#include <iostream>
#include <thrust/device_vector.h>
#include <thrust/for_each.h>
#include <thrust/sequence.h>
#include <thrust/iterator/zip_iterator.h>
#include <thrust/copy.h>
#define N 4


class Polygon {
  protected:
    int width, height;
  public:
  __host__ __device__  void set_values (int a, int b)
      { width=a; height=b; }
  __host__ __device__  virtual int area ()
      { return 0; }
};

class Rectangle: public Polygon {
  public:
  __host__ __device__  int area ()
      { return width * height; }
};

class Triangle: public Polygon {
  public:
  __host__ __device__   int area ()
      { return (width * height / 2); }
};


struct init_f {
  template <typename Tuple>
  __host__ __device__ void operator()(const Tuple &arg) {
    (thrust::get<0>(arg)).set_values(thrust::get<1>(arg), thrust::get<2>(arg));}
};

struct setup_f {
  template <typename Tuple>
  __host__ __device__ void operator()(const Tuple &arg) {
    if (thrust::get<0>(arg) == 0)
      thrust::get<1>(arg) = &(thrust::get<2>(arg));
    else
      thrust::get<1>(arg) = &(thrust::get<3>(arg));}
};

struct area_f {
  template <typename Tuple>
  __host__ __device__ void operator()(const Tuple &arg) {
    thrust::get<1>(arg) = (thrust::get<0>(arg))->area();}
};


int main () {

  thrust::device_vector<int>  widths(N);
  thrust::device_vector<int> heights(N);
  thrust::sequence( widths.begin(),  widths.end(), 2);
  thrust::sequence(heights.begin(), heights.end(), 3);
  thrust::device_vector<Rectangle> rects(N);
  thrust::device_vector<Triangle>  trgls(N);
  thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(rects.begin(), widths.begin(), heights.begin())), thrust::make_zip_iterator(thrust::make_tuple(rects.end(), widths.end(), heights.end())), init_f());
  thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(trgls.begin(), widths.begin(), heights.begin())), thrust::make_zip_iterator(thrust::make_tuple(trgls.end(), widths.end(), heights.end())), init_f());
  thrust::device_vector<Polygon *> polys(N);
  thrust::device_vector<int> selector(N);
  for (int i = 0; i<N; i++) selector[i] = i%2;
  thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(selector.begin(), polys.begin(), rects.begin(), trgls.begin())), thrust::make_zip_iterator(thrust::make_tuple(selector.end(), polys.end(), rects.end(), trgls.end())), setup_f());
  thrust::device_vector<int> areas(N);
  thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(polys.begin(), areas.begin())), thrust::make_zip_iterator(thrust::make_tuple(polys.end(), areas.end())), area_f());
  thrust::copy(areas.begin(), areas.end(), std::ostream_iterator<int>(std::cout, "\n"));
  return 0;
}

I suggest compiling the above code for a cc2.0 or newer architecture. I tested with CUDA 6 on RHEL 5.5.

(The polymorphic example idea, and some of the code, was taken from here.)