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Meng, J., Morozov, V.A., Vishwanath, V. and Kumaran, K. (2012) Dataflow-Driven GPU Performance Projection for Multi-Kernel Transformations. Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 82.

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• TITLE: An Approach to Parallelization of SIFT Algorithm on GPUs for Real-Time Applications

  AUTHORS: Raghu Raj Prasanna Kumar, Suresh Muknahallipatna, John McInroy

  KEYWORDS: Scale Invariant Feature Transform (SIFT), Parallel Computing, GPU, GPU Occupancy, Portable Parallel Programming, CUDA

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.17, December 29, 2016

  ABSTRACT: 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.