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    周国昌 沈绪榜. LS CSIMD配置存储器组织及管理算法研究[J]. 计算机研究与发展, 2007, 44(6): 1080-1087.
    引用本文: 周国昌 沈绪榜. LS CSIMD配置存储器组织及管理算法研究[J]. 计算机研究与发展, 2007, 44(6): 1080-1087.
    Zhou Guochang and Shen Xubang. Research of CRAM's Architecture and Scheduling Algorithm of LS CSIMD[J]. Journal of Computer Research and Development, 2007, 44(6): 1080-1087.
    Citation: Zhou Guochang and Shen Xubang. Research of CRAM's Architecture and Scheduling Algorithm of LS CSIMD[J]. Journal of Computer Research and Development, 2007, 44(6): 1080-1087.

    LS CSIMD配置存储器组织及管理算法研究

    Research of CRAM's Architecture and Scheduling Algorithm of LS CSIMD

    • 摘要: 通过对LS CSIMD体系结构的深入研究,提出了一种支持扩展配置存储器寻址空间的配置存储器组织结构(配置指令采用立即数寻址),该结构通过增加配置存储器容量,可以有效地降低配置延迟.同时,针对LS CSIMD配置存储器的组织结构,提出了一种基于任务核频率和容量的配置数据管理算法.该算法根据配置任务序列特征,动态产生静态区容量,并根据任务核频率和容量进行调度,有效地减少了重复取入片上的配置数据总量,从而降低了配置延迟.实验结果表明,该算法不但时间复杂度低(最好情况下O(n)),而且当任务核数较多且任务核之间容量相差较小时可得最优解,其他情况下可得到次优解.

       

      Abstract: Dynamically reconfigurable architectures are emerging as a viable design alternative to implement wide range of computationally intensive applications. The architecture of configuration memory and context (configuration) management are very critical issues in achieving the high performance of dynamically reconfigurable architectures. An architecture of configuration memory (CRAM) that can be extended accessing space (configuration instructions adopt immediate addressing mode) is proposed by deeply researching on the architecture of LS CSIMD in this paper. This architecture of CRAM, which has multiple contexts, can greatly reduce reconfiguration latency by increasing capacity of CRAM. For CRAM's architecture, a novel scheduling algorithm based on the frequency and capacity of task kernels is proposed in subtopic 3. According to the character of reconfigurable task sequence, this algorithm can dynamically compute the capacity of the static region CRAM, and then the algorithm decides the locations of task kernels in static or dynamic region. So, the amount of configuration data loaded repeatedly into the CRAM is effectively decreased, and reconfiguration latency is reduced. The experimental results (subtopic 4) show that the time complexity of the scheduling algorithm is about O(n) in best condition and the time complexity is about O(n·log2n) in other conditions. When there are more task kernels and less difference among kernels capacities, the results are optimal.

       

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