有效的重新计时展开同步数据流图

Xue-Yang Zhu
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引用次数: 0

摘要

同步数据流图(sdfg)被广泛用于数据驱动程序的建模,对于数据驱动程序来说,吞吐量是一个重要的实时要求。重定时和展开是sdfg性能优化的重要图变换技术。重新计时通过重新分配其初始令牌来提高SDFG的吞吐量,同时通过调度图的多次迭代来展开。在本文中,我们提出了一种有效而精确的方法,可以在原始SDFG上找到可折叠SDFG的可行重定时和最优重定时,而无需将其转换为等效的齐次SDFG (HSDFG),因此无需进一步展开HSDFG。转换过程通常非常耗时和占用空间。我们还为sdfg扩展了两种最先进的重新定时方法来处理展开。我们实现了这三种方法,并在不同结构和大小的图上进行了实验,以彻底评估它们。结果表明,该方法总体上优于现有重定时方法的扩展,特别是对于大型图和结构复杂的图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Retiming of Unfolded Synchronous Dataflow Graphs
Synchronous dataflow graphs (SDFGs) are widely used to model data driven programs, for which throughput is an important real-time requirement. Retiming and unfolding are important graph transformation techniques for performance optimization of SDFGs. Retiming improves the throughput of an SDFG by redistributing its initial tokens, while unfolding by scheduling several iterations of the graph. In this paper, we present an efficient and exact method to find feasible retiming and optimal retiming of an unfolded SDFG on its original SDFG, without converting it to its equivalent homogeneous SDFG (HSDFG) and therefore without further unfolding the HSDFG. The conversion procedures are usually time and space-consuming. We also extend two state-of-the-art retiming methods for SDFGs to deal with unfolding. We implement all the three methods and perform experiments on graphs with various structures and sizes to evaluate them thoroughly. The results show that the proposal method generally outperforms the extensions of existing retiming methods, especially for the large graphs and the graphs with complex structures.
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