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.
Practical Task Scheduling Deployment
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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- Stunnel Security for Oracle
- The Firebird Project's Firebird Relational Database
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- SUSE LLC's SUSE Manager
- My +1 Sword of Productivity
- Non-Linux FOSS: Caffeine!
- Managing Linux Using Puppet
- SuperTuxKart 0.9.2 Released
- Doing for User Space What We Did for Kernel Space
- Google's SwiftShader Released
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide