Parallel techniques for improving three-dimensional models storing and accessing performance

Nowadays, the volume of multimedia and unstructured data has grown rapidly. More and more three-dimensional (3D) models are created for ever increasing applications. New storage and processing technologies are needed to keep pace with the continuous growth of big data. Hadoop is an attractive and open-source platform for large-scale data storage and analytics. Our previous research work has applied Hadoop distributed file system to efficiently manage 3D data for a 3D model retrieval system. To take better advantages of Hadoop, in this paper we propose two parallel strategies to improve the storing and accessing performance of 3D models. The MapReduce paradigm is adopted to provide a coarse grained parallelism for data loading, and a lightweight multithreaded algorithm is presented for data accesses. We conduct an extensive performance study on a cluster and the results show that significant performance increase can be gained for the parallel techniques.