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A Highly Scalable Parallel Implementation of H.264

  • Arnaldo Azevedo
  • Ben Juurlink
  • Cor Meenderinck
  • Andrei Terechko
  • Jan Hoogerbrugge
  • Mauricio Alvarez
  • Alex Ramirez
  • Mateo Valero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6760)

Abstract

Developing parallel applications that can harness and efficiently use future many-core architectures is the key challenge for scalable computing systems. We contribute to this challenge by presenting a parallel implementation of H.264 that scales to a large number of cores. The algorithm exploits the fact that independent macroblocks (MBs) can be processed in parallel, but whereas a previous approach exploits only intra-frame MB-level parallelism, our algorithm exploits intra-frame as well as inter-frame MB-level parallelism. It is based on the observation that inter-frame dependencies have a limited spatial range. The algorithm has been implemented on a many-core architecture consisting of NXP TriMedia TM3270 embedded processors. This required to develop a subscription mechanism, where MBs are subscribed to the kick-off lists associated with the reference MBs. Extensive simulation results show that the implementation scales very well, achieving a speedup of more than 54 on a 64-core processor, in which case the previous approach achieves a speedup of only 23. Potential drawbacks of the 3D-Wave strategy are that the memory requirements increase since there can be many frames in flight, and that the frame latency might increase. Scheduling policies to address these drawbacks are also presented. The results show that these policies combat memory and latency issues with a negligible effect on the performance scalability. Results analyzing the impact of the memory latency, L1 cache size, and the synchronization and thread management overhead are also presented. Finally, we present performance requirements for entropy (CABAC) decoding.

Keywords

Intra Prediction Memory Latency Video Decode Reference Count Task Pool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Arnaldo Azevedo
    • 1
  • Ben Juurlink
    • 1
  • Cor Meenderinck
    • 1
  • Andrei Terechko
    • 2
  • Jan Hoogerbrugge
    • 3
  • Mauricio Alvarez
    • 4
  • Alex Ramirez
    • 4
    • 5
  • Mateo Valero
    • 4
    • 5
  1. 1.Delft University of TechnologyDelftNetherlands
  2. 2.Vector FabricsEindhovenNetherlands
  3. 3.NXPEindhovenNetherlands
  4. 4.Technical University of Catalonia (UPC)BarcelonaSpain
  5. 5.Barcelona Supercomputing Center (BSC)BarcelonaSpain

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