GIST特征提取的异构并发流计算实现-计算机工程与应用.PDF

Computer Engineering and Applications 计算机工程与应用 2015 ，51（6 ） 139 GIST 特征提取的异构并发流计算实现 仲济源，梅魁志，温哲西 ZHONG Jiyuan, MEI Kuizhi, WEN Zhexi 西安交通大学 电子与信息工程学院，西安 710049 School of Electronic and Information Engineering, Xi ’an Jiaotong University, Xi ’an 710049, China ZHONG Jiyuan, MEI Kuizhi, WEN Zhexi. Parallel stream computing implementation of GIST algorithm on hetero- geneous platform. Computer Engineering and Applications, 2015, 51 （6 ）：139-144. Abstract ：To extract the global feature of GIST, a heterogeneous CPU+GPU collaborative computing and optimization is firstly implemented: CPU is used to complete the tasks of small amount of calculations and irregular data operations, such as image quantization and linear extension, while using GPU to complete the tasks with compute-intensive and highly par- allel data operations, such as filtering, Gabor feature extracting and dimension reducing. For processing image sequences, the thread pool technology is introduced on the CPU side. Through the use of each thread binding a CUDA stream for one image, the parallel stream computing for multiple images between CPU and GPU and the streaming data transmission delay hidden are achieved. Moreover thread pool technology also offers the methods of thread pre-creating, pre-allocating of resources and running thread number changing on resource, which can improve the computing efficiency of GPU scheduled by the CPU. Under the same computing accuracy, experiments show that GIST implementation on heteroge- neous computing platforms for images reaches 8.35~9.31 times speedup of the running on traditional CPU platform, and has an upgrading rate of 10.0%~37.2% for image sequences data while using the thread pool. Key words ：GIST; Compute Unified Device Architecture（CUDA ）; thread pool; heterogeneous computing 摘 要：针对图像GIST 全局特征提取算法的计算任务，实现了CPU+GPU 异构协同计算与优化：使用CPU 完成图像 量化、线性延拓等小计算量、不规则的