Implement slab transport contribution on the GPU

[tlestang]

Slab transport is evaluating the contribution to the transport term coming from derivatives along a single direction. Considering a slab of data in the $j$-orientation, we want to compute

$$u_i * \frac{\partial u_i}{\partial x_j} + \frac{\partial u_i u_j}{\partial x_j} + \nu \frac{\partial^2 u_i}{\partial x_j}$$$

Implementation of slab transport can then split into three steps:

1. Add a new cuda_diffengine_t derived type providing a diff bound procedure executing a cuda kernel performing the differentiation (Implement differentiation on the GPU #3).
2. Compute and combine derivatives into a transport_dir bound procedure on the cuda slab.
3. Implement the top level transport bound procedure on the cuda slab type.

Although the GPU implementation might eventually differ from the CPU implementation for performance reasons (e.g. computation of all derivatives and their sum within a single CUDA kernel), I suggest that the first working version follows the CPU implementation closely.

Just like the diffengine_t type is responsible for instanciating a tridiagonal solver from the bulk and boundary differentiation schemes, the cuda_diffengine_t could hold device arrays and stencil instances for the thomas solver coefficeints and stencil, respectively. These device objects could be initialised from host code before calling the differentatiation kernel.