Tera-scale Computing

Media Mining—Emerging Tera-scale Computing Applications

INTRODUCTION

Rapid advances in the hardware technology of media capture, storage, and computation power have contributed to an amazing growth in digital media content. As content generation and dissemination grows, extracting meaningful knowledge from large amounts of multimedia data becomes increasingly important. Media mining is a kind of technology that helps end users search, browse, and manage large amounts of multimedia data [1]. It yields a wide range of emerging applications with various mass-market segments, e.g., image/video retrieval, video summarization, scene understanding, visual surveillance, digital home entertainment, smart health care, etc. Most of these applications are very complicated and have real-time or even super-real-time processing demands, which require tera-scale computing power to make them usable.

In this paper, we present several media-mining applications that require target architectures capable of delivering tera-scale computing. Our study shows that today’s single-core processor system performance is 10x–1000x slower for acceptable human interactions. To accelerate these compute-intensive applications, we exploit the inherent data and function parallelism of these workloads. Our experiments show that with proper parallelization, these workloads can scale well, achieving a speedup of up to 7.5x on a 2-socket, quad-core machine and a speedup of up to 30x on a 32-core CMP simulator.

This paper is organized as follows. First, we explore several media-mining usage models and their key techniques. Next, we present several different parallel schemes and a general parallel video-mining framework. Then, we show our performance analysis results of the parallelized workloads.

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