CODEWITHSUNDEEP
c++cudalibraries
George Aprilis
George Aprilis

Reputation: 1200

Using external library classes in CUDA project

I am trying to enhance a small C++ project with CUDA. My project is using a custom library's classes and functions for example Matrix3d, Vector3d, Plane2d etc. They are mostly geometric objects.

When I try to use my code in the device (either __host__ __device__ functions or a kernel) all the library functions/objects are considered as host code and I get multiple warnings and errors for example error: identifier "Plane3d::~Plane3d" is undefined in device code

Is there a way to use my library on device as well? How is it done? I don't have experience on CUDA and C++ (I have only used CUDA with simple C code without classes) so I don't get the strategy very well.

Is there a method to avoid changing the library source code? It is possible to change the library's code but it would be better if I could avoid it.

Thanks a lot.

Upvotes: 3

Views: 2499

Answers (2)

talonmies
talonmies

Reputation: 72352

There is no particular problem with using C++ classes in CUDA. The object model is only slightly different to standard C++.

Any structure or class data members are automatically defined in whichever memory space (so host or device) the class or structure is instantiated in. What is not automatic is the code generation for function members and operators within classes and structure. The programmer must explicitly define and compile those for whichever memory space the object will be instantiated in. This latter requirement means you must have both __device__ and __host__ definitions of each function you call within the object. This includes the constructor and destructor, the latter being the error you show in your question.

Upvotes: 1

Danny Varod
Danny Varod

Reputation: 18118

You don't need to change the source code - what you need is to write an adapter.

CUDA kernels work with low level structures e.g. double*, double*, double** or float*, float*, float** as well as with the built in CUDA types.

CUDA can not work directly on memory allocated outside CUDA anyway (only with memory allocated on the Graphics card, not regular RAM), so you will have to copy your data into the graphics memory.

If you provide methods which have access to the buffers used by your types, you can copy them, continuously if your types have continuous memory, or in chunks if not, into the graphics card (using the CUDA memory copy function), then you can process them with kernels as double*** using simple indexing.

Upvotes: 0

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