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基于OpenCL的图像模糊化算法优化研究
Authors
张云泉
张樱
龙国平
Publication date
1 January 2011
Publisher
Abstract
现代GPU一般都提供特定硬件(如纹理部件、光栅化部件及各种片上缓存)以加速二维图像的处理和显示过程,相应的编程模型(CUDA、OpenCL)都定义了特定程序设计接口(CUDA的纹理内存,OpenCL的图像对象)便于图像应用能利用相关硬件支持。以典型图像模糊化处理算法在AMD平台GPU的优化为例,探讨OpenCL的图像对象在图像算法优化上的适用范围,尤其是其相对于更通用的基于全局内存加片上局部存储进行性能优化方法的优劣。实验结果表明图像对象只有在图像为四通道且计算过程中需要缓存的数据量较小时能带来较好的性能改善,其余情况采用全局内存加局部存储能获得更好性能。优化后的算法性能相对于精心实现的CPU版加速比为200-1000;相对于NVIDIA NPP库相应函数的性能加速比为1.3-5。中国计算机学
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Institute Of Software, Chinese Academy Of Sciences
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Last time updated on 30/12/2017