Final Thoughts, Performance Rankings, Conclusion

Final Thoughts

After reviewing all the benchmark data as well as the image quality screenshots, both GPGPU technologies had their pros and cons that could affect a consumer’s decision to purchase hardware and software that utilizes ATI Stream and/or CUDA. While Stream’s transcoding times were slightly better than CUDA in most of our performance tests, CUDA seemed to produce a higher quality image that evened things out a bit. Stream also seemed to be more efficient in using less of the CPU’s resources for transcoding while also producing fast transcoding times. However, these transcoding times might be lower because it is outputting lower-quality video files as our subjective image quality tests suggest.

Another interesting item to note is the GPU usage scores we recorded for our Radeon 4770. None of the GPU scores we received went over 23 percent, which indicates there’s still a lot of stream processing power available for programmers to take advantage of. Maybe we’ll also see some enhancements to future drivers from ATI and NVidia to use the extra GPU muscle too. Unfortunately, we weren’t able to record any of our 9800GTX+’s GPU scores, but we did see higher CPU usage numbers that indicate NVidia isn’t as concerned with multi-taskers who might like to use their computer for other tasks while they are transcoding video.

Cyberlink’s PowerDirector 7 is a full-featured video editing and transcoding application that supports both ATI Stream and CUDA. PowerDirector’s only “flaw”, if you want to call it that, is that it maxes out the CPU during transcoding and doesn’t leave any room for multi-tasking. The other program we got a chance to play with was another title from Cyberlink called MediaShow Expresso. A lot of talk has been buzzing around this particular app and for good reason. The transcoding times we recorded using Expresso were extremely quick. The UI had a Loiloscope feel to it and was intuitive from the moment I opened the program. Choosing different preset profiles was a snap and consumers should have an easy time adapting to Expresso’s two-step transcoding process. 

Lastly, we were pretty impressed with the simplicity of ATI’s Avivo HD benchmark results against Handbrake. The overall transcoding times were exceptional and Avivo kept the CPU usage down for those of us who like to multi-task. The interface was extremely simple to use, but lacked some advanced features we’ve become accustomed to seeing in video transcoders like video effects and transitions options and better video customization options. ATI confirmed to us that Avivo HD does not support iPod, PSP, VC1, H.264, and MKV video formats at this time. However, it does support MPEG-1, VCD, MPEG-2, DVD/VOB, DVR-MS, DivX (as long as the codec is installed), and WMV formats, which is more than adequate for most users. 

Performance rankings

To recap the goals of our review today, we wanted to rank how each GPGPU technology faired in meeting the intent of our testing perimeters for this article. A couple of our perimeters were specific to testing against a CPU-based transcoder,

Parameter 1: Evaluate CPU usage and determine how much of the computing load being handled by the CPU with ATI Stream/CUDA enabled and disabled

Winner: ATI Stream. During our evaluation, we noticed considerable differences in CPU usage between transcoding with ATI Stream and CUDA. CUDA’s average CPU usage was in the 80s, while Stream was closer to the high 60s. The extra CPU usage didn’t really help CUDA in producing faster transcoding times either. So, the winner would have to be ATI Stream because it used less resources and produced faster transcoding times. It also left enough resources for users to do additional tasks during transcoding.

Parameter 2: What performance differences will consumers notice between using ATI Stream or CUDA?

Winner: ATI Stream. The performance differences between these two GPGPU technologies was a bit mixed because Stream used less CPU power and had better transcoding times, but it seemed to produce lower quality videos. If we strictly viewed just the “performance” portion of our review, ATI Stream would win because of its benchmark results during performance testing. We’ll give a slight edge to ATI Stream in this portion of our ranking.

Parameter 3: Subjectively evaluate the image quality of outputted video that was transcoded with ATI Stream and CUDA

Winner: NVidia CUDA. CUDA seemed to produce a higher-quality image in two out of the three video clips we captured screenshots from. ATI Stream’s outputted video was a little bit softer in a few parts of the test videos and CUDA’s screenshots were brighter, clearer, and showed a little more detail overall. So, we’ll give CUDA the image quality crown.



We’d like to thank Cyberlink and AMD (ATI) for providing their respective transcoding software for our review today. GPGPU technology is really still in its infancy and GPU acceleration for video transcoding is just the beginning. I’m sure both AMD (ATI) and NVidia have their sights set on using the GPU for more general tasks and are working with programmers to move toward utilizing stream computing for other types of applications. The benefits of GPU acceleration is undeniable, especially in the video transcoding department. The differences between transcoding with the GPU and CPU in tandem as opposed to using the CPU alone suggest that GPU acceleration plays a large role in outputting video at faster rates. I’m sure we’ll see a lot more from the GPGPU realm that consumers and enthusiasts should benefit from not only from performing basic tasks, but with more computing-intensive programs.

Ryan’s Thoughts: Let me offer a second opinion on these results.  Everything that Steve has written in this piece is correct in terms of speed, performance and CPU utilization.  A lot of testing went into this piece and I think he did a very good job of summarizing his thoughts.  There is another opinion on this debate though, one that I follow more closely than he does.  The truth of the matter is that for video transcoding and encoding, there are two key points: speed and quality.  We want the GPU-based applications to increase the SPEED of our video work but we also have expectations of image quality. 

An individual user may in fact want different benefits at different times as well: if I am in a rush to catch a flight I might my movie to encode incredibly fast regardless of quality so I don’t miss the plane.  Or I might have planned ahead that night and decided I want a better quality encode but still have a time crunch.

CPU utilization is an important factor as well for multi-tasking.  NVIDIA’s implementation is obviously using some extra CPU cycles to improve quality in a way that AMD’s implementations are not.  Again – two different perspectives on what you want to do with your system.

What I am trying to get at is that for me – I would favor the higher image quality results of the NVIDIA CUDA-based implementations of these GPGPU apps in just about 99% of circumstances.  If you are considering UPCONVERTING your content to HD quality, for example, what is the point of getting it done “faster” if it isn’t done in the best quality possible?  If you are one of those users archiving your DVD content locally then you would also likely desire the better image quality of the NVIDIA CUDA software as opposed to the AMD Stream software. 

In the end though,  Steve is correct: GPU computing is here in a pretty big way but still has further to go before it is really everything for everyone.

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