Nvidia introduced CUDA with the release of the GeForce 8800. At that time the promises they were making were extremely seductive, but we kept our enthusiasm in check. After all, wasn’t this likely to be just a way of staking out the territory and surfing the GPGPU wave? Without an SDK available, you can’t blame us for thinking it was all just a marketing operation and that nothing really concrete would come of it. It wouldn’t be the first time a good initiative has been announced too early and never really saw the light of day due to a lack of resources – especially in such a competitive sector. Now, a year and a half after the announcement, we can say that Nvidia has kept its word.
Not only was the SDK available quickly in a beta version, in early 2007, but it’s also been updated frequently, proving the importance of this project for Nvidia. Today CUDA has developed nicely; the SDK is available in a beta 2.0 version for the major operating systems (Windows XP and Vista and Linux and 1.1 for Mac OS X), and Nvidia is devoting an entire section of its site for developers.
On a more personal level, the impression we got from our first steps with CUDA was extremely positive. Even if you’re familiar with the GPU’s architecture, it’s natural to be apprehensive about programming it, and while the API looks clear at first glance you can’t keep from thinking it won’t be easy to get convincing results with the architecture. Won’t the gain in processing time be siphoned off by the multiple CPU-GPU transfers? And how to make good use of those thousands of threads with almost no synchronization primitive? We started our experimentation with all these uncertainties in mind. But they soon evaporated when the first version of our algorithm, trivial as it was, already proved to be significantly faster than the CPU implementation.
So, CUDA is not a gimmick intended for researchers who want to cajole their university into buying them a GeForce. CUDA is genuinely usable by any programmer who knows C, provided he or she is ready to make a small investment of time and effort to adapt to this new programming paradigm. That effort won’t be wasted provided your algorithms lend themselves to parallelization. We should also tip our hat to Nvidia for providing ample, quality documentation to answer all the questions of beginning programmers.
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- Vive le GeForce FX!
- The advent of GPGPU
- The CUDA APIs
- A Few Definitions
- The Theory: CUDA from the Hardware Point of View
- Hardware Point of View, Continued
- The Theory: CUDA from the Software Point of View
- In Practice
- Conclusion, Continued