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A task-level OoO framework for heterogeneous systems

机译:用于异构系统的任务级ooo框架

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This paper proposes a framework targeting the problem of task-level out-of-order (OoO) execution for heterogeneous systems. The framework consists of three layers: 1) Programming model; 2) OoO task scheduler; 3) Processing Elements. In order to uncover task-level parallelism automatically, renaming scheme is applied from instruction-level parallelism (ILP) to task-level parallelism (TLP). With the help of renaming scheme, inter-task data dependencies can be detected automatically during execution, and then task-level WAW and WAR dependencies can be eliminated dynamically. We applied Tomasulo algorithm from ILP to perform task-level OoO execution, and implemented a prototype on a state-of-art reconfigurable FPGA platform. Experimental results show that the framework is efficient for heterogeneous systems.
机译:本文提出了一个框架,其针对非均匀系统执行任务级超出(OOO)执行问题的框架。 该框架由三层:1)编程模型组成; 2)OOO任务调度程序; 3)处理元素。 为了自动发现任务级并行性,重命名方案从指令级并行性(ILP)应用于任务级并行性(TLP)。 在重命名方案的帮助下,可以在执行期间自动检测任务间数据依赖性,然后可以动态地消除任务级WAW和WAR依赖关系。 我们从ILP应用了Tomasulo算法在执行任务级OOO执行,并在最先进的可重新配置FPGA平台上实现了原型。 实验结果表明,该框架对于异质系统是有效的。

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