Mode grouping for more effective generalized scheduling of dynamic dataflow applications
Mode grouping for more effective generalized scheduling of dynamic dataflow applications
复制标题
模式分组可实现动态数据流应用程序更有效的广义调度
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
S. Bhattacharyya
中科院分区：
文献类型：
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作者：
W. Plishker;N. Sane;S. Bhattacharyya
多年来，数据流概念为数字信号处理系统的设计者提供了能够表达高级软件架构以及低级、面向性能的内核的环境。为了将这些经过验证的技术应用到新的复杂的动态应用程序中，我们在动态参与者中识别原子的、可重复的操作（“模式”）的重复序列，以暴露应用程序的更多静态性质。在这项工作中，我们提出了一种模式分组策略，有助于将动态数据流图分解为一组动态交互的静态数据流图。模式分组能够发现更大的静态子图，从而改善调度结果。我们表明，分组模式可以改进调度，实现内存需求降低高达 37%，其中包括具有动态行为的常见成像基准：3D B 样条插值。
For a number of years, dataflow concepts have provided designers of digital signal processing systems with environments capable of expressing high-level software architectures as well as low-level, performance-oriented kernels. To apply these proven techniques to new complex, dynamic applications, we identify repetitive sequences of atomic, repeatable actions ("modes") inside dynamic actors to expose more of the static nature of the application. In this work, we propose a mode grouping strategy that aids in the decomposition of a dynamic dataflow graph into a set of static dataflow graphs that interact dynamically. Mode grouping enables the discovery of larger static subgraphs improving scheduling results. We show that grouping modes results in improved schedules with lower memory requirements for implementations by up to 37% including a common imaging benchmark with dynamic behavior: 3D B-spline interpolation.