Advertisement

Journal of Computer Science and Technology

, Volume 33, Issue 2, pp 417–428 | Cite as

GPU-Driven Scalable Parser for OBJ Models

  • Sunghun Jo
  • Yuna Jeong
  • Sungkil Lee
Regular Paper
  • 46 Downloads

Abstract

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.

Keywords

3D model Wavefront OBJ parser GPU 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgment

Models of Stanford Dragon, XYZ Dragon, XYZ Thai Statue, and Lucy 3D are provided by the courtesy of the Stanford 3D Scanning Repository and the Hairball model by Samuli Laine, Tero Karras, and Morgan McGuire at NVIDIA.

Supplementary material

11390_2018_1827_MOESM1_ESM.pdf (327 kb)
ESM 1 (PDF 326 kb)

References

  1. 1.
    Cignoni P, Corsini M, Ranzuglia G. MeshLab: An open-source 3D mesh processing system. ERCIM News, 2008, 73: 45-46.Google Scholar
  2. 2.
    Lu W, Chiu K, Pan Y. A parallel approach to XML parsing. In Proc. the 7th ACM/IEEE Int. Conf. Grid Computing, Sept. 2006, pp.223-230.Google Scholar
  3. 3.
    Ghorpade J, Parande J, Kulkarni M, Bawaskar A. GPGPU processing in CUDA architecture. arXiv preprint arXiv:1202.4347, Feb. 2012.Google Scholar
  4. 4.
    Han T D, Abdelrahman T S. hiCUDA: High-level GPGPU programming. IEEE Trans. Parallel and Distributed Systems, 2011, 22(1): 78-90.Google Scholar
  5. 5.
    Si X, Yin A, Huang X, Yuan X, Liu X, Wang G. Parallel optimization of queries in XML dataset using GPU. In Proc. the 4th Int. Symp. Parallel Architectures, Algorithms and Programming, Dec. 2011, pp.190-194.Google Scholar
  6. 6.
    Johnson M. Parsing in parallel on multiple cores and GPUs. In Proc. Australasian Language Technology Association Workshop, Dec. 2011, pp.29-37.Google Scholar
  7. 7.
    Bakkum P, Skadron K. Accelerating SQL database operations on a GPU with CUDA. In Proc. Workshop on General-Purpose Computation on Graphics Processing Units, March 2010, pp.94-103.Google Scholar
  8. 8.
    Possemiers A L, Lee I. Fast OBJ file importing and parsing in CUDA. Computational Visual Media, 2015, 1(3): 229-238.CrossRefGoogle Scholar
  9. 9.
    Head M R, Govindaraju M. Parallel processing of large-scale XML-based application documents on multi-core architectures with PiXiMaL. In Proc. the 4th IEEE Int. Conf. on eScience, Dec. 2008, pp.261-268.Google Scholar
  10. 10.
    Li X, Wang H, Liu T, Li W. Key elements tracing method for parallel XML parsing in multi-core system. In Proc. Int. Conf. Parallel and Distributed Computing, Applications and Technologies, Dec. 2009, pp.439-444.Google Scholar
  11. 11.
    Cameron R D, Herdy K S, Lin D. High performance XML parsing using parallel bit stream technology. In Proc. Conf. the Center for Advanced Studies on Collaborative Research: Meeting of Minds, Oct. 2008.Google Scholar
  12. 12.
    Hou Q, Zhou K, Guo B. BSGP: Bulk-synchronous GPU programming. ACM Trans. Graphics, 2008, 27(3): Article No. 19.Google Scholar
  13. 13.
    Canny J, Hall D, Klein D. A multi-Teraflop constituency parser using GPUs. In Proc. Conf. Empirical Methods in Natural Language Processing, Oct. 2013, pp.1898-1907.Google Scholar
  14. 14.
    Lewis M, Lee K, Zettlemoyer L. LSTM CCG parsing. In Proc. Annual Conf. North American Chapter of the Association for Computational Linguistics, June 2016.Google Scholar
  15. 15.
    Hall D L W, Berg-Kirkpatrick T, Klein D. Sparser, better, faster GPU parsing. In Proc. ACL, June 2014, pp.208-217.Google Scholar
  16. 16.
    Hensley J, Scheuermann T, Coombe G, Singh M, Lastra A. Fast summed-area table generation and its applications. Computer Graphics Forum, 2005, 24(3): 547-555.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of SoftwareSungkyunkwan UniversitySuwonKorea

Personalised recommendations