NDLI: A GPU-based high-throughput image retrieval algorithm

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A GPU-based high-throughput image retrieval algorithm

Content Provider	ACM Digital Library
Author	Chen, Peng Chen, Haibo Zang, Binyu Yang, Donglei Zhu, Feiwen Zhang, Weihua
Abstract	With the development of Internet and cloud computing, multimedia data, such as images and videos, has become one of the most common data types being processed. As the scale of multimedia data being still increasing, it is vitally important to efficiently extract useful information from such a huge amount of multimedia data. However, due to the complexity of the core algorithms, multimedia retrieval applications are not only data intensive but also computationally intensive. Therefore, it has been a major challenge to accelerate the processing speed of such applications to satisfy the real-time requirement. As Graphic Processing Unit (GPU) has entered the general-propose computing domain (GPGPU), it has become one of the most popular accelerators for the applications with real-time requirements. In this paper, we parallelize a widely-used image retrieval algorithm called SURF on GPGPU, which is the core algorithm for many video and image retrieval applications. We first analyze the parallelism within SURF to guarantee that there are sufficient tasks being mapped to the large-scale computation resources in GPGPU. We then exploit some inherent GPGPU characteristics, such as 2D memory, to further boost the performance. Finally, we provide some optimization to the cooperation between CPU and GPGPU, which is generally ignored in previous designs. Experimental results show that our parallelization and optimization achieve a throughput of 340.5 frames/s on a NVIDIA GTX295 GPGPU, which is 15X faster than the maximal optimized CPU version. Compared to CUDA SURF, a state-of-the-art parallelization of SURF on GPGPU, our system achieves a speedup by a factor of 2.3X.
Starting Page	30
Ending Page	37
Page Count	8
File Format	PDF
ISBN	9781450312332
DOI	10.1145/2159430.2159434
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-03-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	High throughput Surf Image retrieval Gpu-based
Content Type	Text
Resource Type	Article

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