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Efficient Parallel Processing of Multimedia Applications on Multi-core Architectures


The well-known wave-front parallelization is proposed for parallel H.264/AVC video

processing. Under this approach, groups of independent macro-blocks (MBs) are

simultaneously processed, one group after another. Barrier mechanism is employed

to synchronize processing of the independent MBs. This approach, however, has a

substantial synchronization overhead that significantly affects the throughput

performance. A novel dynamic scheduling scheme with recursive tail submit provides a

good throughput performance by exploiting macro-block level parallelism and alleviating the synchronization overhead and thread contention. Nevertheless, it fails to

achieve an optimal performance due to the use of a global queue, and an unawareness

of cache locality of the underlying multi-core architecture. I propose an adaptive dynamic scheduling scheme that employs distribued queues, and dynamically schedules

tasks in a cache locality-aware and load-balancing fashion.

As a graphics accelerator, GPGPU is able to off-loads compute intensive

functions. In H.264 video encoding, hierarchical search is a widely proposed for the most

expensive motion estimation. GPGPU is suitable, especially with full search-based

approaches as the process can be efficiently parallelized. However, their fixed pyramid

structure lacks a mechanism to select the best multiple-candidate schemes

considering diverse video encoding characteristics. I propose profiled-based fixed multiple candidate motion vector selection scheme, and an efficient dynamic multiple candidate

motion vector selection scheme to dynamically select best multiple-candidate motion

vector schemes at runtime.

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