文章基本信息

标题：An Approach to Parallelization of SIFT Algorithm on GPUs for Real-Time Applications
本地全文：下载
作者：Raghu Raj Prasanna Kumar ; Suresh Muknahallipatna ; John McInroy 等
期刊名称：Journal of Computer and Communications
印刷版ISSN：2327-5219
电子版ISSN：2327-5227
出版年度：2016
卷号：04
期号：17
页码：18-50
DOI：10.4236/jcc.2016.417002
语种：English
出版社：Scientific Research Publishing
摘要：Scale Invariant Feature Transform (SIFT) algorithm is a widely used computer vision algorithm that detects and extracts local feature descriptors from images. SIFT is computationally intensive, making it infeasible for single threaded im-plementation to extract local feature descriptors for high-resolution images in real time. In this paper, an approach to parallelization of the SIFT algorithm is demonstrated using NVIDIA’s Graphics Processing Unit (GPU). The parallel-ization design for SIFT on GPUs is divided into two stages, a) Algorithm de-sign-generic design strategies which focuses on data and b) Implementation de-sign-architecture specific design strategies which focuses on optimally using GPU resources for maximum occupancy. Increasing memory latency hiding, eliminating branches and data blocking achieve a significant decrease in aver-age computational time. Furthermore, it is observed via Paraver tools that our approach to parallelization while optimizing for maximum occupancy allows GPU to execute memory bound SIFT algorithm at optimal levels.
关键词：Scale Invariant Feature Transform (SIFT);Parallel Computing;GPU;GPU Occupancy;Portable Parallel Programming;CUDA