›› 2018, Vol. 33 ›› Issue (2): 417-428.doi: 10.1007/s11390-018-1827-2

Special Issue: Computer Architecture and Systems

• Computer Graphics and Multimedia • Previous Articles    

GPU-Driven Scalable Parser for OBJ Models

Sunghun Jo, Yuna Jeong, Sungkil Lee*, Member, ACM   

  1. Department of Software, Sungkyunkwan University, Suwon 16419, Korea
  • Received:2017-01-02 Revised:2017-07-19 Online:2018-03-05 Published:2018-03-05
  • Contact: Sungkil Lee E-mail:sungkil@skku.edu
  • About author:Sunghun Jo received his B.S. degree in computer engineering at Hansei University, Gunpo City, in 2016. He is a M.S. student in computer engineering at Sungkyunkwan University, Suwon. His main research interest is real-time rendering
  • Supported by:

    This work was supported in part by the Mid-career and Global Frontier (on Human-centered Interaction for Coexistence) Research and Development Programs through the National Research Foundation (NRF) under Grant Nos. 2015R1A2A2A01003783 and 2012M3A6A3055695, the Information Technology Research Center (ITRC) Program under Grant No. ⅡTP-2017-2016-0-00312 supervised by the Institute for Information and Communications Technology Promotion (ⅡTP), funded by the Korea Government (Ministry of Science, ICT (Information and Communications Technologies) and Future Planning), and Faculty Research Fund, Sungkyunkwan University, 2011.

This paper presents a scalable parser framework using graphics processing units (GPUs) for massive text-based files. Specifically, our solution is designed to efficiently parse Wavefront OBJ models texts of which specify 3D geometries and their topology. Our work bases its scalability and efficiency on chunk-based processing. The entire parsing problem is subdivided into subproblems the chunk of which can be processed independently and merged seamlessly. The within-chunk processing is made highly parallel, leveraged by GPUs. Our approach thereby overcomes the bottlenecks of the existing OBJ parsers. Experiments performed to assess the performance of our system showed that our solutions significantly outperform the existing CPU-based solutions and GPU-based solutions as well.

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