NDLI: A comprehensive analysis and parallelization of an image retrieval algorithm

Content Provider	IEEE Xplore Digital Library
Author	Zhenman Fang Donglei Yang Weihua Zhang Haibo Chen Binyu Zang
Copyright Year	2011
Description	Author affiliation: Parallel Processing Institute, Fudan University (Donglei Yang; Weihua Zhang; Haibo Chen; Binyu Zang) \|\| The State Key Lab of ASIC & System, Fudan University (Zhenman Fang)
Abstract	The prevalence of the Internet and cloud computing has made multimedia data, such as image data and video data, become major data types in our daily life. For example, many data-intensive applications, such as health care and video recommendation, involve collecting, indexing and retrieving tera-scale multimedia data every day. With such a huge amount of multimedia data to process, the processing speed has been one of the major challenges to guarantee real-time requirements. The advent of multi-core hardware has opened new opportunities to improve the effectiveness of multimedia data processing. In this paper, we make a comprehensive analysis on different potential parallelism, including pipeline parallelism, task parallelism at both scale level and block level, data parallelism, and their combinations, in a typical image retrieval algorithm called SURF, which is the core algorithm of many multimedia (i.e., image and video) retrieval applications. Experimental results show the following observations of parallelism in SURF: 1) when only one level parallelism is exploited, block-level parallelism is more efficient and scalable than other alternatives; 2) data parallelism cannot be ignored especially when parallel resources increase and 3) the combination of block-level parallelism and pipeline parallelism is the most efficient parallelizing manner for the studied image retrieval algorithm. Based on these observations, we have implemented a parallel image retrieval algorithm. It can be easily mapped onto different multi-core platforms with good scalability. On a commodity server machine with 16-core, the parallel implementation achieves a speedup of 13X, which is 84% faster than P-SURF, a previous state-of-the-art parallelization of SURF on CPU; while on GPGPU, it achieves a speedup of 46X, which is 53% faster than CUDA SURF, a previous state-of-the-art parallelization of SURF on GPGPU.
Starting Page	154
Ending Page	164
File Size	233526
Page Count	11
File Format	PDF
ISBN	9781612843674
e-ISBN	9781612843681
DOI	10.1109/ISPASS.2011.5762732
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parallel processing Image retrieval Pipelines Feature extraction Algorithm design and analysis Streaming media Hardware
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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