Skip to main content
eScholarship
Open Access Publications from the University of California

UC Riverside

UC Riverside Electronic Theses and Dissertations bannerUC Riverside

Efficient Parallel Processing of Multimedia Applications on Multi-core Architectures

Abstract

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.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View