DVD Transcoding with Linux Metacomputing
Once the video partitioning stage is done, video data chunks are submitted to Condor transcoding jobs. These jobs are processed in the Condor Vanilla universe, because they load the DivX library dynamically.
In order to transcode a data chunk, every transcoder reads the data directly from source VOBs. Output data is written to the same folder. Read/write operations are performed on a frame-to-frame basis: a transcoder reads a frame, transcodes it and writes the result back. This strategy yields a better performance than delivering whole chunks to the workers, transcoding them in worker local filesystems and sending whole transcoded results from the workers to the server computer for joining. All computers used NFS to share both input VOB files and the output folder. Once the parallel transcoding stage finishes, transcoded results are a set of independent files, which are concatenated at the master to generate a DivX movie. Table 2 presents the results.
We used the two load balancing strategies, Small-Chunks and Master-Worker. The testing movie was All about My Mother, which has a length of 1 hour and 37 minutes and an original size of 2.94GB. The tuples in the comp column in Table 2 are the first letters of the names of the test bed computers, for example, g refers to gigabyte and t refers to titan. A - symbol indicates that the computer was not used in that particular test. Chunk size in Small-Chunks was set to 60MB. Video preprocessing time has not been included because it was negligible in all cases.
Several conclusions can be extracted from Table 2. First, according to the Fps column, load balancing is better with Master-Worker than it is with Small-Chunks. The difference is small, but it tends to grow as the number of machines used increases. In general, parallelization increases transcoding performance, which is evident when adding a second powerful machine (see [g----] vs. [gk---]). The impact of adding low-end machines successively is low (see [gk---] vs. [gk--b] and [gk--b] vs. [gkntb]). However, the combined impact of all low-end machines is noticeable, especially when departing from the case of a single available powerful machine (see [g----] vs. [g-ntb]).
In order to evaluate further the behavior of the prototype, we compared it with two popular transcoding tools, Mencoder and FlaskMpeg. Table 3 shows these results. The speed of the monoprocessor version of our prototype lies between FlaskMpeg's and Mencoder's. Regarding output size, in the worst case (Small-Chunks), our prototype delivers a DivX movie that is only 2.6% larger than FlaskMpeg's output. Indeed, the global compression rates achieved by Small-Chunks (24.67%) and FlaskMpeg (24.05%) are similar, and the difference is not relevant if processing speedup is taken into consideration. It is important to note that FlaskMpeg uses DivX codec v. 5.0.5 Pro, which was not available for Linux at the time this article was written. Therefore, compression performances may be even closer when the Linux version becomes available.
Finally, Figures 1 and 2 show the throughput of the machines in the system prototypes, both Small-Chunks and Master-Worker, for load balancing. The computers do not finish their assignments exactly at the same time. This should be expected, though, as Small-Chunks load balancing is only approximate. Plus, Master-Worker job size is assigned according to the results of a training stage, which is representative but not exact.
In this article, we have presented a Condor high-throughput DVD transcoding system for Linux. Our results indicate that metacomputing-oriented parallel transcoding is of practical interest, and it can achieve noticeable improvements when compared to existing monoprocessor Windows tools.
Attending to the statistics of our case study, pure Master-Worker produces better results than Small-Chunks, but the difference is minimal and seems irrelevant in practice.
Getting Started with DevOps - Including New Data on IT Performance from Puppet Labs 2015 State of DevOps Report
August 27, 2015
12:00 PM CDT
DevOps represents a profound change from the way most IT departments have traditionally worked: from siloed teams and high-anxiety releases to everyone collaborating on uneventful and more frequent releases of higher-quality code. It doesn't matter how large or small an organization is, or even whether it's historically slow moving or risk averse — there are ways to adopt DevOps sanely, and get measurable results in just weeks.
Free to Linux Journal readers.Register Now!
- Django Models and Migrations
- Hacking a Safe with Bash
- Secure Server Deployments in Hostile Territory, Part II
- The Controversy Behind Canonical's Intellectual Property Policy
- Home Automation with Raspberry Pi
- Huge Package Overhaul for Debian and Ubuntu
- Shashlik - a Tasty New Android Simulator
- KDE Reveals Plasma Mobile
- Embed Linux in Monitoring and Control Systems
- diff -u: What's New in Kernel Development