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DOI:
Journal of Software:2009.20(9):2320-2331

流处理器上基于参数模型的长流分段技术
杜静,敖富江,唐滔,杨学军
(63880 部队 博士后科研工作站,河南 洛阳 471003;国防科学技术大学 计算机学院,湖南 长沙 410073)
Parameter Model Based Strip-Mining Technique on the Stream Processor
DU Jing,AO Fu-Jiang,TANG Tao,YANG Xue-Jun
()
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Received:February 14, 2008    Revised:June 03, 2008
> 中文摘要: 长流分段是提高流处理器上流寄存器文件(stream register file,简称SRF)带宽利用率的重要途径之一.其中,量化受段大小影响的程序运行时间是获得最优分段的关键.为此提出了一种基于参数模型的长流分段技术,旨在获得理论上的最优分段以最小化程序运行时间.首先,建立了一个预取和重用优化指导的参数模型,以反映段大小对流处理器上程序性能的影响.然后,基于该模型分析,分别研究了计算密集型程序和访存密集型程序的最优分段策略.最后提出一种面向任意程序的最优分段技术.实验结果表明,该长流分段技术能够有效地避免和隐藏片外访存延迟,从而充分开发流处理器强大的计算能力.
Abstract:The Strip-Mining technique is significant for improving SRF bandwidth utilization on the stream processor. It is critical to quantify the program execution time influenced by the strip size for achieving optimalstrip size. In order to achieve the theoretical optimal strip size, this paper proposes an optimal strip-miningtechnique based on a parameter model to minimize the execution time. Firstly, the paper builds a prefetching and reusing optimizations guided parameter model that characterizes the effect of strip size on program behavios.Secondly, based on the model analysis, this paper explores the optimal strip size selection approaches to the computation intensive programs and memory intensive programs respectively. Finally, an optimal strip-miningtechnique for any program is proposed. The experimental results show that our strip-mining technique caneffectively hide and avoid the memory access latency, so as to exploit the powerful computation ability of stream processor.
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基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60621003, 60633050 (国家自然科学基金) Supported by the National Natural Science Foundation of China under Grant Nos.60621003, 60633050 (国家自然科学基金)
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杜静,敖富江,唐滔,杨学军.流处理器上基于参数模型的长流分段技术.软件学报,2009,20(9):2320-2331

DU Jing,AO Fu-Jiang,TANG Tao,YANG Xue-Jun.Parameter Model Based Strip-Mining Technique on the Stream Processor.Journal of Software,2009,20(9):2320-2331