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.
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.
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.
Related
I have a working CPU-based implementation of a simple deep learning framework where the main components are nodes of a computation graph which can perform computations on tensors.
Now I need to extend my implementation to GPU, I would like to use the existing class structure and only extend its functionality to GPU however, I'm not sure if that's even possible.
Most of the classes have methods that work on and return tensors such as:
tensor_ptr get_output();
where tensor_ptr is simply std::shared_ptr pointer of my tensor class. Now what I would like to do is to add a GPU version for each such method. The idea that I had in mind was to define a struct in a separate file tensor_gpu.cuh as follows
struct cu_shape {
int n_dims;
int x,y,z;
int len;
};
struct cu_tensor {
__device__ float * array;
cu_shape shape;
};
and then the previous function would be mirrored by:
cu_tensor cu_get_output();
The problem seems to be that the .cuh file gets treated as a regular header file and is compiled by the default c++ compiler and gives error:
error: attribute "device" does not apply here
on the line with the definition of __device__ float * array.
I am aware that you cannot mix CUDA and pure C++ code so I planned to hide all the CUDA runtime api functions into .cu files which would be defined in .h files. The problem is that I wanted to store the device pointers within my class and then pass those to the CUDA-calling functions.
This way I could still use all of my existing object structure and only modify the initialization and computation parts.
If a regular c++ class cannot touch anything with __device__ flag then how can you even integrate CUDA code into C++ code?
Can you only use CUDA runtime calls and keywords literally just in .cu files?
Or is there some smart way to hide the fact from c++ compiler that it is dealing with CUDA pointers?
Any insight is deeply appreciated!
EDIT: There seems to be a misunderstanding on my part. You don't need to put the __device__ flag and you'll still be able to use it as a pointer to device memory. If you have something valuable to add to good practices on CUDA integration or clarify something else, don't hesitate!
'__' is reserved for implementation purposes. That is why the Nvidia implementation can use __device__. But the other "regular" C++ implementation has its own reserved symbols.
In hindsight Nvidia could have designed a better solution but that is not going to help you here.
Let say there is a C++ functor:
class Dummy
{
public:
int operator() (const int a, const int b)
{
return a+b;
}
};
This functor doesn't use any function that can't execute on GPU but it can't be called from CUDA kernel cause there is no __device__ declaration in front of operator(). I would like to create factory class that converts such functors to device compatible functors that can be called within CUDA kernel. For example:
Dummy d;
auto cuda_d = CudaFunctorFactory.get(d);
Can this be accomplished in any way? Feel free to add some constraints as long as it can be accomplished...
The one word answer is no, this isn't possible.
There is no getting around the fact that in the CUDA compilation model, any method code contained in a class or structure which will execute on the GPU must be statically declared and defined at compile time. Somewhere in that code, there has to be a __device__ function available during compilation, otherwise the compilation fails. That is a completely non-negotiable cornerstone of CUDA as it exists today.
A factory design pattern can't sidestep that requirement. Further, I don't think it is possible to implement a factory for GPU instances in host code because there still isn't any way of directly accessing __device__ function pointers from the host, and no way of directly instantiating a GPU class from the host because the constructor must execute on the GPU. At the moment, the only program units which the host can run on the GPU are __global__ functions (ie. kernels), and these cannot be contained within classes. In CUDA, GPU classes passed by argument must be concretely defined, virtual methods aren't supported (and there is not RTTI). That eliminates all the paths I can think of to implement a factory in CUDA C++ for the GPU.
In summary, I don't see any way to make magic that can convert host code to device code at runtime.
I am new to Cuda and I need to know its limits before running my C++ project via Cuda.
Suppose I have a C++ class called MyClass. Knowing that Cuda uses C99, is it possible to declare an object of type MyClass inside a kernel? Would the below code snippet be appropriate?
_global__ void SolveBlaBlaBLa(int x, ...)
{
MyClass obj1;
.
.
.
}
Thanks in Advance,
- Ruru
Just providing an answer to get this off the unanswered list. I think #JaredHoberock will not mind.
In general, CUDA supports a large subset of C++ functionality, including support for objects in device code.
Any code that executes on the device, however, must be properly decorated. For ordinary individual functions (not kernels), the decorator that the compiler recognizes to create a device callable version of the code is __device__. This applies to any object method which may be used on the device, including constructors and destructors.
You may also wish to familiarize yourself with other restrictions on C++ classes used in the device code, as documented in the programming guide.
I have two dll-exported classes A and B. A's declaration contains a function which uses a std::vector in its signature like:
class EXPORT A{
// ...
std::vector<B> myFunction(std::vector<B> const &input);
};
(EXPORT is the usual macro to put in place _declspec(dllexport)/_declspec(dllimport) accordingly.)
Reading about the issues related to using STL classes in a DLL interface, I gather in summary:
Using std::vector in a DLL interface would require all the clients of that DLL to be compiled with the same version of the same compiler because STL containers are not binary compatible. Even worse, depending on the use of that DLL by clients conjointly with other DLLs, the ''instable'' DLL API can break these client applications when system updates are installed (e.g. Microsoft KB packages) (really?).
Despite the above, if required, std::vector can be used in a DLL API by exporting std::vector<B> like:
template class EXPORT std::allocator<B>;
template class EXPORT std::vector<B>;
though, this is usually mentioned in the context when one wants to use std::vector as a member of A (http://support.microsoft.com/kb/168958).
The following Microsoft Support Article discusses how to access std::vector objects created in a DLL through a pointer or reference from within the executable (http://support.microsoft.com/default.aspx?scid=kb;EN-US;Q172396). The above solution to use template class EXPORT ... seems to be applicable too. However, the drawback summarized under the first bullet point seems to remain.
To completely get rid of the problem, one would need to wrap std::vector and change the signature of myFunction, PIMPL etc..
My questions are:
Is the above summary correct, or do I miss here something essential?
Why does compilation of my class 'A' not generate warning C4251 (class 'std::vector<_Ty>' needs to have dll-interface to be used by clients of...)? I have no compiler warnings turned off and I don't get any warning on using std::vector in myFunction in exported class A (with VS2005).
What needs to be done to correctly export myFunction in A? Is it viable to just export std::vector<B> and B's allocator?
What are the implications of returning std::vector by-value? Assuming a client executable which has been compiled with a different compiler(-version). Does trouble persist when returning by-value where the vector is copied? I guess yes. Similarly for passing std::vector as a constant reference: could access to std::vector<B> (which might was constructed by an executable compiled with a different compiler(-version)) lead to trouble within myFunction? I guess yes again..
Is the last bullet point listed above really the only clean solution?
Many thanks in advance for your feedback.
Unfortunately, your list is very much spot-on. The root cause of this is that DLL-to-DLL or DLL-to-EXE is defined on the level of the operating system, while the the interface between functions is defined on the level of a compiler. In a way, your task is similar (although somewhat easier) to that of client-server interaction, when the client and the server lack binary compatibility.
The compiler maps what it can to the way the DLL importing and exporting is done in a particular operating system. Since language specifications give compilers a lot of liberty when it comes to binary layout of user-defined types and sometimes even built-in types (recall that the exact size of int is compiler-dependent, as long as minimal sizing requirements are met), importing and exporting from DLLs needs to be done manually to achieve binary-level compatibility.
When you use the same version of the same compiler, this last issue above does not create a problem. However, as soon as a different compiler enters the picture, all bets are off: you need to go back to the plainly-typed interfaces, and introduce wrappers to maintain nice-looking interfaces inside your code.
I've been having the same problem and discovered a neat solution to it.
Instead of passing std:vector, you can pass a QVector from the Qt library.
The problems you quote are then handled inside the Qt library and you do not need to deal with it at all.
Of course, the cost is having to use the library and accept its slightly worse performance.
In terms of the amount of coding and debugging time it saves you, this solution is well worth it.
I separated a code fragment into a DLL because it will be frequently updated and in this way it should be easier to deploy.
But I have questions about what I can do and what I cannot do with a DLL.
Can I pass a std:string or a CString to a DLL?
Can I pass a pointer to a struct with std::string members and fill it in a DLL?
Can a DLL return a pointer to a struct allocated there? Will it be valid? Can I delete it after?
What should better to pass, a std::String or a Cstring?
Thanks !
You have a choice to make:
Tightly coupled DLL: The DLL is built with the exact same compiler version, packing and calling convention settings, library options as the application, and both dynamically link to the runtime library (/MD compiler option). This lets you pass objects back and forth including STL containers, allocate DLL objects from inside the application, derive from base classes in the other module, do just about everything you could without using DLLs. The disadvantage is that you can no longer deploy the DLL independently of the main application. Both must be built together. The DLL is just to improve your process startup time and working set, because the application can start running before loading the DLL (using the /delayload linker option). Build times are also faster than a single module, especially when whole program optimization is used. But optimization doesn't take place across the application-DLL boundary. And any non-trivial change will still require rebuilding both.
Loosely coupled: The application doesn't depend on the class layout of objects defined by the DLL. You use only highly compatible data types: primitive types, pointers, function pointers, and user-defined types made up of these elements. Classes inherit from a base class which defines interface and has no data members and no non-virtual functions (this means no constructors and no sharing of standard library objects such as std::string or CString). All allocation and object creation must be done through a factory function. Memory must be deallocated from the module which allocated it. Code and data are separated. The header file explicitly states the calling convention of each exported function and packing of each structure allowed to cross module boundaries. The advantage is that the DLL and application can be updated completely independently. You can rebuild one with a new runtime library, new compiler version, or even in a completely new language, and don't have to even touch the other.
I always advise using the loosely coupled approach.
There is a danger when passing anything into and out of a DLL if it's based on a template. Compiler options can affect the layout of the object, and a template class can't be confined to a single compilation unit; some of it will be distributed to the calling module.
In the case of a string, I would pass a const char * (or const wchar_t * or const TCHAR *) and do the conversion to std::string or CString on the other side of the interface, within the DLL.