CHCF: A Cloud-Based Heterogeneous Computing Framework for Large-Scale Image Retrieval

The last decade has witnessed a dramatic growth of multimedia content and applications, which in turn requires an increasing demand of computational resources. Meanwhile, the high-performance computing world undergoes a trend toward heterogeneity. However, it is never easy to develop domain-specific applications on heterogeneous systems while maximizing the system efficiency. In this paper, a novel framework, namely, cloud-based heterogeneous computing framework (CHCF), is proposed with a set of tools and techniques for compilation, optimization, and execution of multimedia mining applications on heterogeneous systems. With the aid of the compiler and the utility library provided by CHCF, users are able to develop multimedia mining applications rapidly and efficiently. The proposed framework employs a number of techniques, including adaptive data partitioning, knowledge-based hierarchical scheduling, and performance estimation, to achieve high computing performance. As one of the most important multimedia mining applications, large-scale image retrieval is investigated based on the proposed CHCF. The scalability, computing performance, and programmability of CHCF are studied for large-scale image retrieval by case studies and experimental evaluations. The experimental results demonstrate that CHCF can achieve good scalability and significant computing performance improvements for image retrieval.