Tera-scale Computing

Media Mining—Emerging Tera-scale Computing Applications

Yurong Chen, Corporation Technology Group, Intel Corporation
Eric Li, Corporation Technology Group, Intel Corporation
Wenlong Li, Corporation Technology Group, Intel Corporation
Tao Wang, Corporation Technology Group, Intel Corporation
Jianguo Li, Corporation Technology Group, Intel Corporation
Xiaofeng Tong, Corporation Technology Group, Intel Corporation
Patricia Wang, Corporation Technology Group, Intel Corporation
Wei Hu, Corporation Technology Group, Intel Corporation
Yimin Zhang, Corporation Technology Group, Intel Corporation
Yen-Kuang Chen, Corporation Technology Group, Intel Corporation

Index words: tera-scale computing, media mining, video processing, parallel computing, performance analysis

Citation for this paper: Chen, Y.; Li, E.; Li, W.; Wang, T.; Li, J.; Tong, X.; Wang, P.; Hu, W.; Zhang, Y.; Chen, Y. "Media Mining—Emerging Tera-scale Computing Applications." Intel Technology Journal. http://www.intel.com/technology/itj/2007/
v11i3/7-media_mining/1-abstract.htm (August 2007).

ABSTRACT

With the exponential increase in media data on personal computers and the Internet, it is critical for end users to efficiently manage metadata to find the information they are looking for. Media mining refers to a technique whereby a user can retrieve, organize, and manage media data. However, most media-mining applications are compute intensive, and they require tera-operations per second. This paper focuses on how tera-scale computing enables new usage models with media-mining techniques. Several representative media-mining usage examples are explored in detail.

First, we look at how these new usage models are enabled by a different kind of parallelism. For maximum performance, we provide a general parallel framework to abstract various parallelisms. We also present a detailed architectural performance analysis of several representative workloads on a dual-socket, quad-core system and on a 32-core Chip Multiprocessor (CMP) simulator. The results indicate that these media-mining applications have no obvious limits on concurrency and are amenable to future large-scale, multi-core architectures. They can take full advantage of tera-scale computing power in the form of thread-level parallelism to meet users’ needs.

Because the underlying techniques and fundamental algorithms in media mining are widely used in other applications, many of our findings are applicable to other emerging applications as well.

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