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Which Open Source Video Apps Use SMP Effectively?
ydrol writes "After building my new Core 2 Quad Q6600 PC, I was ready to unleash video conversion activity the likes of which I had not seen before. However, I was disappointed to discover that a lot of the conversion tools either don't use SMP at all, or don't balance the workload evenly across processors, or require ugly hacks to use SMP (e.g. invoking distributed encoding options). I get the impression that open source projects are a bit slow on the uptake here? Which open source video conversion apps take full native advantage of SMP? (And before you ask, no, I don't want to pick up the code and add SMP support myself, thanks.)"
x264 use slices and scales pretty well across multiple cores. I use it on windows via megui, but you could easily use it in Linux as well. You could use mencoder to pipe out raw video to a fifo and use x264 to do the actual conversion, for instance.
Or just convert 2 videos at once, or 4 for a quad core etc. They did suggest they have lots to convert, and it's a pretty easy way to get all available cores working hard.
don't balance the workload evenly across processors
Why is balancing the load evenly important, as long as one thread is not bottlenecking the others? Loading a particular core or set of cores might even be beneficial depending on the cache implementation, especially when other applications are also contending for CPU time.
Sure, a nice even load distribution might be an indicator for good design, but it doesn't have to apply in every case. I don't think software should be designed so you can be pleased with the aesthetics of the charts in task manager.
The problem with MPEG encoding and decoding is that the data itself is not well suited to multi-threaded analysis.
Multi-threading is most efficient when it is applied to discrete data sets that have little or no dependency on each other.
For example, suppose I have a table with four columns -- three holding input values (A, B, and C) and one holding an output value (X). If the data in a given row of the table has nothing to do with the data in any other row, multi-threading works efficiently, because none of the threads are waiting for data from any of the other threads. If I want to process multiple rows at once, I simply spawn additional threads.
On the other hand, for data such as MPEG video, the composition of the next frame is equal to the composition of the current frame, plus some delta transformation - the changed pixels.
This introduces a dependency which precludes efficient multi-threaded processing, because each succeeding frame depends on the output of the calculations used to generate the prior frame. Even if more than one core is dedicated to processing the video stream, one core would wind up waiting on another, because the output from the first core would be used as the input to the second.
On a two processor system this would result in multi-threading being off.
Exactly. Too many people assume that any given programmer can write any given program. What isn't generally realized (at least by the masses) is that programming really is about acquiring expertise in a particular domain and then solving problems in that domain through the use of computer programs. Generally some of the most effective programs I've seen have been written, on their first pass, by a person with intimate domain knowledge, and mediocre programming/computer knowledge. The program then becomes a standout when someone with intense programming and computer architecture knowledge improves the code from there (they need not be a subject domain expert, but it helps).
I do take issue with sexconker assuming that I "just don't get it". Heh. If s/he only knew. Whatever, no biggie. I do agree that distributed algorithms are generally more difficult to implement/design than non-distributed, but that's not exactly the same thing as serial versus parallel algorithms (non-distributed generally involves access to data through a common address space, distributed doesn't, though even those pseudo-definitions come up a bit short).
Again and again I read in industry rags and on various web sites that multi-threaded programming is hard, and nobody knows how to do it, and that it's difficult to debug, and all that. I believe what they're really saying is "The set of programmers who are accustomed to multi-threaded programming/debugging is (relatively) small, and thus applications aren't going to make good use of the shift to multicore CPU packages." Familiarity with a skill, and the supply of labor familiar with said skill, is distinct from it being easy or hard.
Anyway, I stand by my belief that parallel programming is not as difficult as most people are led to believe. Some problems don't lend themselves well to parallel solutions, or don't merit the added complexity, but many many of them do. In ten years time I predict that most computer programming education will assume the use of threading, and that anyone who isn't competent with threading will severely limit their own job prospects.
Cyrano de Maniac
As other commenters have said, decoding video is not, per se, a trivially parallelized algorithm. Especially for modern codecs with lots of temporal encoding. MJPEG would be easily parallelized, buy you'd have to be dealing with fairly ancient sources...MediaComposer 1 for instance.
However, there are different classes of "video app" that are good targets for parallelization. Real world video editing for instance: consider multiple streams of video with overlays, rotations, effects etc. Video and audio decoding can happen in parallel, you can pipeline the effects stages so that each effect is handed off to another core. Modern video editing systems do this with aplomb.
I'm from the commercial end of this so, I can't comment much on open source alternatives. But I will say that a lot of the algorithms in certain products are highly tuned to the particular CPU type.
And they're smart enough to distribute work across only as many cores as actually exist.
Finally. Don't forget that optimization is hard. You have to consider the speed of the hard drive, the cost of sharing data between threads and cpu caches and a bunch of other real constraints. Any half decent cpu of the last five years or so can easily decode most video faster than it can be read and written to disk. So long as this is true, you won't get any benefit from parallelization.
Apple has spent a lot of time and money convincing everybody that they don't sell PCs, they sell Macs. I'm not sure what the point of arguing with both the general public as well as Apple is.
At this point, the term PC does not include Apple computers. It's a change to the definition which happens when the vast majority of people decide amongst themselves that the definition should change.
In terms of the topic at hand, most video apps really should be capable of using multiple cores, tasks of this sort are quite easy to finish in parallel. Either by doing ever n frames or subdividing the image into a number of regions which can be completed separately and joined at the end before writing the frame to disk.
No - HP did (for their calculators), way before there "was" an Apple.
Also, I don't even think Apple marketing would agree with you - or they wouldn't have "I'm a Mac... and I'm a PC" adverts.