CUDA: Wrapping device memory allocation in C++

Question

I\'m starting to use CUDA at the moment and have to admit that I\'m a bit disappointed with the C API. I understand the reasons for choosing C but had the language been base

Answer 1

There are several projects that attempt something similar, for example CUDPP.

In the meantime, however, I've implemented my own allocator and it works well and was straightforward (> 95% boilerplate code).

Answer 2

In the meantime there were some further developments (not so much in terms of the CUDA API, but at least in terms of projects attempting an STL-like approach to CUDA data management).

Most notably there is a project from NVIDIA research: thrust

Answer 3

I would go with the placement new approach. Then I would define a class that conforms to the std::allocator<> interface. In theory, you could pass this class as a template parameter into std::vector<> and std::map<> and so forth.

Beware, I have heard that doing such things is fraught with difficulty, but at least you will learn a lot more about the STL this way. And you do not need to re-invent your containers and algorithms.

Answer 4

Does anybody have information about future CUDA developments that go in this general direction (let's face it: C interfaces in C++ s*ck)?

Yes, I've done something like that:

https://github.com/eyalroz/cuda-api-wrappers/

nVIDIA's Runtime API for CUDA is intended for use both in C and C++ code. As such, it uses a C-style API, the lower common denominator (with a few notable exceptions of templated function overloads).

This library of wrappers around the Runtime API is intended to allow us to embrace many of the features of C++ (including some C++11) for using the runtime API - but without reducing expressivity or increasing the level of abstraction (as in, e.g., the Thrust library). Using cuda-api-wrappers, you still have your devices, streams, events and so on - but they will be more convenient to work with in more C++-idiomatic ways.