Page 1:Image Quality: Examined
Page 2:Intel, AMD, And Nvidia: Decode And Encode Support
Page 3:Transcoding Quality Revisited: CUDA Problems?
Page 4:Test Setup
Page 5:Hardware Decoder Quality: Examined
Page 6:Software Decoding: All CPU, All the Time
Page 7:Full Blu-ray Transcoding Speed: APP Versus CUDA Versus Quick Sync
Page 8:Small Clip Transcoding Speed: APP Versus CUDA Versus Quick Sync
Page 9:Transcoding Quality: APP Versus CUDA Versus Quick Sync
Page 10:Transcoding Quality: Rated By Software Title
Page 11:Playing Devil's Advocate: "There is No Spoon"
Page 12:Inside The Black Box: GPGPU Encoding
Page 13:Final Words
Inside The Black Box: GPGPU Encoding
Alright, so we've established that video quality analysis isn't easy, unless you're looking at a clear and present mistake.But how are Nvidia and AMD handling transcoding on the GPU? More specifically, how are they taking what logically seems like a serial process and turning it into a parallel one?
Threaded encoding is dynamic. When a software encoder is optimized for a multi-core CPU, each thread tries to encode an individual frame. However, multi-threaded time allocation is controlled by the OS without any software oversight. This means a programmer can't control which thread finishes first and gets allocated CPU resources. For example, core number one may be just completing the encode for frame 80, even though cores two and three aren't finished with frame 78 and 79. As there aren't extra buffers for frame 81, the dynamic bitrate for the next frame gets altered. You need to do this in order to optimize for threaded performance, otherwise you have threads waiting on one another, and it ends up being a one-core/one-thread encode. That is why frame 81 in transcoding trial #1 can be different from transcoding trial #2.
This is only one way to program for multi-threaded encoders. There are other strategies that programmers employ. For example, you could choose to divide the work by slices and have n slices per frame. You could also assign specific parts of the encoding pipeline to each thread (for instance, motion search, macroblock encoding, entropy encoding, rate control). There are advantages and disadvantages with each strategy. For consistent scaling, specifically with systems with many CPU cores, you should divide the work by frames, because each portion of the encoding pipeline takes differing times to complete. The deterministic portion of this process is the complex bitrate control inherent to each encoder, but it may be tied into specific operations like motion search/estimation.
That alone isn't what sets one encoder apart from another. Stages within the encoding pipeline may not differ sequentially, but the process itself may. Operations like motion search can significantly differ between encoders. One might predict a starting point based on the prior frame or based on neighboring macroblocks. As Mike Schmit, senior manager of digital video software at AMD explains, "If you are processing many frames (or macroblocks) at once, you will not have the luxury/advantage of a predictor, so many frames/macroblocks will start their search with no predictor. This can cause different outcomes. But knowing this, a programmer could force the serial path to also start with no predictors to force identical outcomes. This probably wouldn't happen in real code because it would be a slower choice."
MediaEspresso: SW Encode / Decode Trial 1 MediaEspresso: SW Encode / Decode Trial 2
All of these programming strategies introduce some randomness into transcoding, as it is another factor that can throw off comparisons between encoders, specifically those that run on the CPU. So, even if you transcode with the same software encoder, you can still end up with different-quality video over multiple transcoding runs.
MediaConverter: SW Encode / Decode - Single Core
MediaConverter is the only software out of the three that allows you to select how many cores you want transcoding to monopolize. If you transcode with a single core, you will also get the same file size output every single time. This makes sense, since the entire process is now serial. Frame 81 depends on 80 clearing the buffer.
What does this have to do with graphics processors? A lot, actually. I accidentally reran a benchmark twice and stumbled on a curiosity. The file sizes of GPGPU- and fixed-function-encoded video are the same, no matter how many times you try it. For example, when we run Quick Sync-based encode and decode with MediaConverter, Badaboom, or MediaEspresso, we get the same file size every time. Why does this parallel process behave like a serial one?
Obviously, there are some things that have to be run in a serial manner, like reassembling the encoded frames. But the same file size means that frame #80 is always encoded the same way every single time. This is the same thing we see on a single-thread video transcode. What is going on here?
For the most part, the experts we interviewed said that while they have control over the flow of data, they don't really know what happens between the time they pass a frame to a reference library and when it is passed back, encoded.
Frankly, answers were very hard to come by until we talked to Mike Schmit, who leads the team that does video codec research and development. He gave the following answer: "Even though the GPU is all about parallelism, it is sort of similar to a single core where you have the SSE instructions...and they're just a 16-byte wide instruction. Essentially, one way (not the only way) to program the GPU shaders is to basically program as if it is just a thousand-wide SIMD instruction inside of SSE. So, it's still deterministic, but it doesn't have that randomness that you might think. It completely depends on the programmer and how they attack the problem."
- Image Quality: Examined
- Intel, AMD, And Nvidia: Decode And Encode Support
- Transcoding Quality Revisited: CUDA Problems?
- Test Setup
- Hardware Decoder Quality: Examined
- Software Decoding: All CPU, All the Time
- Full Blu-ray Transcoding Speed: APP Versus CUDA Versus Quick Sync
- Small Clip Transcoding Speed: APP Versus CUDA Versus Quick Sync
- Transcoding Quality: APP Versus CUDA Versus Quick Sync
- Transcoding Quality: Rated By Software Title
- Playing Devil's Advocate: "There is No Spoon"
- Inside The Black Box: GPGPU Encoding
- Final Words