CUDA: Unraveling the Mysteries of 2D and 3D Arrays

Many questions arise when working with 2D and 3D arrays in CUDA, and conflicting answers can be frustrating. To address these concerns, let's delve into the common solutions and their implications:

2D Array Allocation: mallocPitch vs. Flatten

Commonly, cudaMallocPitch and cudaMemcpy2D are used for 2D arrays. However, these API functions actually work with pitched allocations rather than true 2D arrays. They require contiguous memory, something that cannot be achieved using malloc or loops.

For true 2D arrays, the recommended approach is flattening. By storing elements consecutively in a 1D array, you eliminate the need for pointer chasing and reduce complexity.

3D Array Allocation: Embracing Complexity or Embracing Flatten

Dynamically allocated 3D arrays introduce significant complexity compared to 2D arrays, often leading to the recommendation of flattening. Alternatively, special cases exist where known compile-time dimensions allow for more efficient handling of 2D and 3D arrays.

2D Access in Host Code, 1D Access in Device Code

A hybrid approach allows you to maintain 2D access in host code while utilizing 1D access in device code. This involves organizing allocations and managing pointers to simplify data transfer between host and device.

Considerations for Object Arrays with Nested Pointers

Arrays of objects with nested pointers are similar to 2D arrays. Dynamic allocation and flattening are viable options, but you should be aware of the potential overhead associated with dynamically allocating objects.

Conclusion

The choice of approach for handling 2D and 3D arrays in CUDA will depend on your specific requirements. While it's feasible to use true 2D arrays, the added complexity often favors flattening or using the aforementioned hybrid method that mixes 2D host code access with 1D device code access.