Paper Info
Title
Parallelization and optimization of a CBVIR system on multi-core architectures
Abstract
Technique advances have made image capture and storage very convenient, which results in an explosion of the amount of visual information. It becomes difficult to find useful information from these tremendous data. Content-based visual information retrieval (CBVIR) is emerging as one of the best solutions to this problem. Unfortunately, CBVIR is a very compute-intensive task. Nowadays, with the boom of multi-core processors, CBVIR can be accelerated by exploiting multi-core processing capability. In this paper, we propose a parallelization implementation of a CBVIR system facing to server application and use some serial and parallel optimization techniques to improve its performance on an 8-core and on a 16-core systems. Experimental results show that optimized implementation can achieve very fast retrieval on the two multi-core systems. We also compare the performance of the application on the two multi-core systems and give an explanation of the performance difference between the two systems. Furthermore, we conduct detailed scalability and memory performance analysis to identify possible bottlenecks in the application. Based on these experimental results and performance analysis, we gain many insights into developing efficient applications on future multi-core architectures.
Year
DOI
Venue
2009
10.1109/IPDPS.2009.5161166
IPDPS
Keywords
Field
DocType
content-based visual information retrieval,memory performance analysis,parallel processing,parallelization implementation,image capture,multi-core processor,multi-core system,multi-core architectures,performance difference,multi-core processors,scalability,future multi-core architecture,image retrieval,multi-core processing capability,performance analysis,content-based retrieval,cbvir system,efficient application,databases,information retrieval,multi core processors,digital images,optimization,multicore processing,computer architecture,multi core processor,feature extraction,acceleration,process capability
Yarn,Computer science,Parallel computing,Image retrieval,Feature extraction,Digital image,Multi-core processor,Boom,Application server,Distributed computing,Scalability
Conference
ISSN
ISBN
Citations 
1530-2075 E-ISBN : 978-1-4244-3750-4
978-1-4244-3750-4
1
PageRank 
References 
Authors
0.35
12
6
Name
Order
Citations
PageRank
Qiankun Miao121.04
Yurong Chen246026.86
Jianguo Li337735.38
Qi Zhang4931179.66
Yimin Zhang51536130.17
Guoliang Chen630546.48