GPU-Driven Scalable Parser for OBJ Models  

作　　者：Sunghun Jo Yuna Jeong Sungkil Lee 

机构地区：[1]Department of Software, Sungkyunkwan University, Suwon 16419, Korea

出　　处：《Journal of Computer Science & Technology》2018年第2期417-428,共12页计算机科学技术学报（英文版）

摘　　要：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 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.

关 键 词：3D model Wavefront OBJ PARSER GPU 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TP334.7[自动化与计算机技术—计算机科学与技术]