计算机工程与应用Issue(6)：139-144,187,7.DOI:10.3778/j.issn.1002-8331.1407-0490
GIST特征提取的异构并发流计算实现
Parallel stream computing implementation of GIST algorithm on heterogeneous platform
摘要
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.关键词
GIST特征/统一计算设备架构(CUDA)/线程池/异构计算Key words
GIST/Compute Unified Device Architecture(CUDA)/thread pool/heterogeneous computing分类
信息技术与安全科学引用本文复制引用
仲济源,梅魁志,温哲西..GIST特征提取的异构并发流计算实现[J].计算机工程与应用,2015,(6):139-144,187,7.基金项目
国家高技术研究发展计划（863）（No.2012AA010904）；国家自然科学基金（No.61375023）。 （）
