Paper Info
Title
FIPIP: A novel fine-grained parallel partition based intra-frame prediction on heterogeneous many-core systems.
Abstract
Intra-frame prediction is an important time-consuming component of the widely used H.264/AVC encoder. To speed up prediction, one promising direction is to introduce parallelism and there have been many heterogeneous many-core based approaches proposed. But most of these approaches are limited by their use of highly irregular prediction formulas, which require significant amount of branch instructions. They only use coarse-grained parallel partition, which considers blocks or sub-region of images as parallel processing units. In this paper, by contrast, we propose a fine-grained intra-frame prediction approach based on parallel partition (FIPIP) and implement it on Graphics Processing Unit (GPU) based heterogeneous many-core systems. The approach is characterized by the following aspects. First, our approach takes individual pixels as parallel processing units, instead of blocks. Imposing pixel-level parallelism is capable of fully exploiting the computational power of heterogeneous GPU-based systems and hence tremendously reduces the encoding time. Second, we unify irregular prediction formulas in intra-frame prediction into a well-designed uniform one, and propose a table-lookup method to efficiently perform intra-frame prediction. Our formula can eliminate unnecessary branch instructions by using a unified predictor array, which improves the efficiency of the fine-grained parallel partition significantly. Third, two optimized encoding orders assisted by an improved combined frame strategy are adopted to implement multi-level parallelism. Finally, an efficient self-synchronizing method is realized for fine-grained task scheduling on heterogeneous CPU–GPU architecture. We apply FIPIP to encode a set of benchmark videos under varying conditions and compare it with other popular intra-frame prediction methods. Results show that FIPIP outperforms existing state-of-the-art work with speedups factor of 2–6.
Year
DOI
Venue
2018
10.1016/j.future.2016.05.009
Future Generation Computer Systems
Keywords
Field
DocType
Parallelism,Fine-grained partition,Intra-frame prediction,Fast mode decision,GPU,H.264/AVC
ENCODE,Computer science,Scheduling (computing),Parallel computing,Real-time computing,Encoder,Intra-frame,Pixel,Graphics processing unit,Encoding (memory),Distributed computing,Speedup
Journal
Volume
ISSN
Citations 
78
0167-739X
1
PageRank 
References 
Authors
0.36
14
7
Name
Order
Citations
PageRank
Wenbin Jiang135536.55
Min Long217923.63
Laurence T. Yang36870682.61
Xiaobai Liu480040.79
Hai Jin56544644.63
Alan L. Yuille6277.33
Ye Chi761.83