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The CUDA APIs

Nvidia's CUDA: The End of the CPU?
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But, Brook’s critical success was enough to attract the attention of ATI and Nvidia, since the two giants saw the incipient interest in this type of initiative as an opportunity to broaden their market even more by reaching a new sector that had so far been indifferent to their graphics achievements.

Researchers who were in on Brook at its origin quickly joined the Santa Clara development teams to put together a global strategy for targeting the new market. The idea was to offer a hardware/software ensemble suited to this type of calculation – since Nvidia’s developers know all the secrets of their GPU, there was no question of relying only on a graphics API, which only communicates with the hardware via a driver, with all the problems that implies, as we saw above. So the CUDA (Compute Unified Device Architecture) development team created a set of software layers to communicate with the GPU.

nvidia CUDA

As you can see on this diagram, CUDA provides two APIs:

  • A high-level API: the CUDA Runtime API;
  • A low-level API: the CUDA Driver API.

Since the high-level API is implemented “above” the low-level API, each call to a function of the Runtime is broken down into more basic instructions managed by the Driver API. Note that these two APIs are mutually exclusive – the programmer must use one or the other, but it’s not possible to mix function calls from both. The term “high-level API” is relative. Even the Runtime API is still what a lot of people would consider very low-level; yet it still offers functions that are highly practical for initialization or context management. But don’t expect a lot more abstraction – you still need a good knowledge of Nvidia GPUs and how they work.

nvidia CUDA

The Driver API, then, is more complex to manage; it requires more work to launch processing on the GPU. But the upside is that it’s more flexible, giving the programmer who wants it additional control. The two APIs are capable of communicating with OpenGL or Direct3D resources (only nine for the moment). The usefulness of this is obvious – CUDA could be used to generate resources (geometry, procedural textures, etc.) that could then be passed to the graphics API, or conversely, it’s possible that the 3D API could send the results of the rendering to CUDA, which in that case would be used to perform post-processing. There are numerous examples of interactions, and the advantage is that the resources remain stored in the GPU’s RAM without having to transit through the bottleneck of the PCI-Express bus.

nvidia CUDA

Conversely, we should point out that sharing resources – in this case video memory – with graphics data is not always idyllic and can lead to a few headaches. For example, for a change of resolution or color depth, the graphics data have priority. So, if the resources for the frame buffer need to increase, the driver won’t hesitate to grab the ones that are allocated to applications using CUDA, causing them to crash. It’s not very elegant, granted; but you have to admit that the situation shouldn’t come up very often. And since we’re on the subject of little disadvantages: If you want to use several GPUs for a CUDA application, you’ll have to disable SLI mode first, or only a single GPU will be visible to CUDA.

nvidia CUDA

Finally, the third software layer is a set of libraries – two to be precise:

  • CUBLAS, which has a set of building blocks for linear algebra calculations on the GPU;
  • CUFFT, which can handle calculation of Fourier transforms – an algorithm much used in the field of signal processing.
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