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.
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.
通讯作者: Sungkil Lee
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
Sunghun Jo, Yuna Jeong, Sungkil Lee.针对OBJ模型的GPU驱动可扩展解析器[J] Journal of Computer Science and Technology , 2018,V33(2): 417-428
Sunghun Jo, Yuna Jeong, Sungkil Lee.GPU-Driven Scalable Parser for OBJ Models[J] Journal of Computer Science and Technology, 2018,V33(2): 417-428
 Cignoni P, Corsini M, Ranzuglia G. MeshLab:An opensource 3D mesh processing system. ERCIM News, 2008, 73:45-46. 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. Ghorpade J, Parande J, Kulkarni M, Bawaskar A. GPGPU processing in CUDA architecture. arXiv preprint arXiv:1202.4347, Feb. 2012. Han T D, Abdelrahman T S. hiCUDA:High-level GPGPU programming. IEEE Trans. Parallel and Distributed Systems, 2011, 22(1):78-90. 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. Johnson M. Parsing in parallel on multiple cores and GPUs. In Proc. Australasian Language Technology Association Workshop, Dec. 2011, pp.29-37. 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. Possemiers A L, Lee I. Fast OBJ file importing and parsing in CUDA. Computational Visual Media, 2015, 1(3):229-238. Head M R, Govindaraju M. Parallel processing of largescale XML-based application documents on multi-core architectures with PiXiMaL. In Proc. the 4th IEEE Int. Conf. on eScience, Dec. 2008, pp.261-268. 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. 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. Hou Q, Zhou K, Guo B. BSGP:Bulk-synchronous GPU programming. ACM Trans. Graphics, 2008, 27(3):Article No. 19. 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. Lewis M, Lee K, Zettlemoyer L. LSTM CCG parsing. In Proc. Annual Conf. North American Chapter of the Association for Computational Linguistics, June 2016. Hall D L W, Berg-Kirkpatrick T, Klein D. Sparser, better, faster GPU parsing. In Proc. ACL, June 2014, pp.208-217. 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.