摘要
相比于传统同步并行计算策略,在异步并行计算框架下,针对最常用的总变分(TV)最小化重建模型,通过将其转化为不动点迭代问题,并利用异步交替方向法(ADM)进行求解,推导出基于TV最小化模型的异步ADM迭代重建算法,即异步交替方向总变分最小化算法(Async-ADTVM)。利用消息传递接口技术将该算法在图形处理器(GPU)集群上进行测试,进一步提高了原始基于TV最小化模型的迭代重建算法的计算效率。实验表明,该算法在计算求解精度上略优于ADTVM算法,同时在GPU性能存在差异的条件下相比传统多GPU加速策略可获得更高的加速比。
Compared to the traditional synchronous parallel computing,an asynchronous parallel alternating direction method(ADM)for total variation(TV)minimization reconstruction,namely asynchronous alternating direction total variation minimization method(Async-ADTVM),is proposed in this paper.Under the asynchronous parallel computing framework,Async-ADTVM transforms TV minimization reconstruction model to the problem of fixed-point iteration,which is solved by asynchronous parallel ADM.It is implemented on the graphics processing unit(GPU)cluster based on message passing interface technology.Experimental results show that the proposed Async-ADTVM can provide a little higher calculation accuracy than ADTVM.Meanwhile,it can provide a higher speed-up ratio than the traditional multi-GPU acceleration strategy when the performance of each GPU is different.
作者
路万里
蔡爱龙
郑治中
王林元
李磊
闫镔
Lu Wanli;Cai Ailong;Zheng Zhizhong;Wang Linyuan;Li Lei;Yan Bin(Institute of Information System Engineering, Information Engineering University of Chinese People's Liberation Army, Zhengzhou, Henan 450002, Chin)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2018年第4期153-160,共8页
Acta Optica Sinica
基金
国家自然科学基金(61372172
61601518)
关键词
成像系统
优化类重建算法
异步并行迭代
总变分最小化模型
多图形处理器加速
imaging systems
optimization-based reconstruction method
asynchronous parallel iteration
total variation minimization model
multi-graphics processing unit acceleration