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中文摘要：
长流分段是提高流处理器上流寄存器文件（stream registerfile，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．
英文摘要：
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 optimal strip size. In order to achieve the theoretical optimal strip size, this paper proposes an optimal strip-mining technique 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-mining technique for any program is proposed. The experimental results show that our strip-mining technique can effectively hide and avoid the memory access latency, so as to exploit the powerful computation ability of stream processor.
同期刊论文项目
同项目期刊论文
Fine-grained Parallel RNAalifold Algorithm for RNA Secondary Structure Prediction on FPGA, Proceedin
Fine-grained parallel RNA secondary structure prediction using SCFGs on FPGA, Journal of Parallel Co
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