Parallelization of Full Search Motion Estimation Algorithm for Parallel and Distributed Platforms

This work presents an efficient method to map the Full Search algorithm for Motion Estimation (ME) onto General Purpose Graphic Processing Unit (GPGPU) architectures using Compute Unified Device Architecture (CUDA) programming model. Our method jointly exploits the massive parallelism available in c...

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Veröffentlicht in:International journal of parallel programming Jg. 42; H. 2; S. 239 - 264
Hauptverfasser: Monteiro, Eduarda, Vizzotto, Bruno, Diniz, Cláudio, Maule, Marilena, Zatt, Bruno, Bampi, Sergio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.04.2014
Springer Nature B.V
Schlagworte:
Algorithms
Coding standards
Computer architecture
Computer Science
Devices
Distributed processing
Libraries
Mathematical models
Motion simulation
Parallel processing
Platforms
Processor Architectures
Programming
Quality standards
Search algorithms
Searching
Software Engineering/Programming and Operating Systems
Studies
Theory of Computation
CUDA
Motion estimation
OpenMP
GPU
MPI
Block matching
ISSN:0885-7458, 1573-7640
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Zusammenfassung:This work presents an efficient method to map the Full Search algorithm for Motion Estimation (ME) onto General Purpose Graphic Processing Unit (GPGPU) architectures using Compute Unified Device Architecture (CUDA) programming model. Our method jointly exploits the massive parallelism available in current GPGPU devices and the parallelism potential of Full Search algorithm. Our main goal is to evaluate the feasibility of video codecs implementation using GPGPUs and its advantages and drawbacks compared to other platforms. Therefore, for comparison reasons, three solutions were developed using distinct programming paradigms for distinct underlying hardware architectures: (i) a sequential solution for general-purpose processor (GPP); (ii) a parallel solution for multi-core GPP using OpenMP library; (iii) a distributed solution for cluster/grid machines using Message Passing Interface (MPI) library. The CUDA-based solution for GPGPUs achieves speed-up compatible to the indicated by the theoretical model for different search areas. Our GPGPU Full Search Motion Estimation provides 2×, 20× and 1664× speed-up when compared to MPI, OpenMP and sequential implementations, respectively. Compared to state-of-the-art, our solution reaches up to 17× speed-up.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-012-0216-7