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Sunghun Jo, Yuna Jeong, and Sungkil Lee

Journal of Computer Science and Technology, 33(2), 417–428, 2018.
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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@ARTICLE{jo18:pop, title={{GPU-Driven Scalable Parser for OBJ Models}}, author={Sunghun Jo and Yuna Jeong and Sungkil Lee}, journal={{Journal of Computer Science and Technology}}, volume={33}, number={2}, pages={417--428}, year={2018} }

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