在动态可重构处理器上最小化执行延迟:一个重新定义的案例研究

R. Krishnamoorthy, Keshavan Varadarajan, Ganesh Garga, M. Alle, S. Nandy, R. Narayan, M. Fujita
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引用次数: 2

摘要

在动态可重构处理器(DRPs)中,编译涉及到将应用程序分解为子任务,以便在结构上分段执行。这些子任务是根据数据和控制依赖性排序的。在DRPs中,子任务预取用于在另一个子任务执行时隐藏重新配置时间。在我们的目标DRP——重新定义中,子任务被称为HyperOps。确定HyperOp的后继对象需要合并来自控制流图和HyperOp数据流图的信息。在许多情况下,演替依赖于数据。由于硬件分支预测器由于非二进制分支而无法应用,我们采用了推测预取单元和基于配置文件的预测方案。仿真结果显示,与没有预取的执行时间相比,总体执行时间减少了约7-33%。我们观察到,当fabric上用于执行预取HyperOps的资源更少时,性能会更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards minimizing execution delays on dynamically reconfigurable processors: a case study on REDEFINE
In Dynamically Reconfigurable Processors (DRPs), compilation involves breaking an application into sub-tasks for piecewise execution on the fabric. These sub-tasks are sequenced based on data and control dependences. In DRPs, sub-task prefetching is used to hide the reconfiguration time while another sub-task executes. In REDEFINE, our target DRP, subtasks are referred to as HyperOps. Determining the successor for a HyperOp requires merging information from the control flow graph and the HyperOp dataflow graph. Succession in many cases is data dependent. Since hardware branch predictors cannot be applied due to the non-binary branches, we employ a speculative prefetch unit together with a profile based prediction scheme. Simulation results show around 7-33% reduction in overall execution time, when compared to the execution time without prefetching. We observe better performance when fewer resources on the fabric are used to execute prefetched HyperOps.
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