用于运行时可重构处理器的性能感知任务调度程序

L. Bauer, Artjom Grudnitsky, M. Shafique, J. Henkel
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引用次数: 15

摘要

多任务是复杂嵌入式系统满足用户期望(如系统的灵活性),提高资源利用率,从而提高系统效率的主要要求之一。一般来说,可以通过结合细粒度可重构结构(例如嵌入式FPGA)来提高灵活性和效率,该结构与通用处理器相结合,并加速计算密集型内核。这项工作的重点是可重构处理器,它使用可重构结构来实现由处理器和处理数据主导部分调用的特殊指令(si)。对于每个SI,是在硬件中执行还是在软件中模拟,都可以在运行时动态更改。在本文中,我们提出了一种新的性能感知任务调度程序(PATS),它在运行时考虑到可重构处理器的特定系统状态来决定任务调度。例如,如果一个任务t必须在软件中模拟几个SI执行,因为重新配置相应的硬件实现尚未完成,那么根据任务的软截止日期,先调度其他任务,直到完成该任务t的重新配置,可能会更有效。与其他任务调度器(最早的截止日期优先、速率单调调度和轮循)相比,PATS平均实现了1.45倍的系统延迟(即,任务错过截止日期的周期总和)。此外,PATS将make span(即所有任务完成所有工作的时间)平均减少了1.17倍(最多1.58倍)。特别是在具有紧迫期限或小型可重构结构的挑战性多任务场景中,PATS的性能明显优于其他任务调度程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PATS: A Performance Aware Task Scheduler for Runtime Reconfigurable Processors
Multi-tasking is one of the main requirements for complex embedded systems to fulfill user expectations (e.g. flexibility of the system), increase the resource utilization, and thus increase the system efficiency. In general, the flexibility and efficiency can be increased by incorporating a fine-grained reconfigurable fabric (e.g. an embedded FPGA) that is coupled with a general-purpose processor and accelerates the computationally intensive kernels. This work focuses on reconfigurable processors that use a reconfigurable fabric to implement Special Instructions (SIs) that are invoked by the processor and process data-dominant parts. For each SI the decision whether it is executed in hardware or emulated in software can be changed dynamically at runtime. In this paper, we present our novel Performance Aware Task Scheduler (PATS) that decides the task schedule at runtime while considering the specific system state of the reconfigurable processor. For instance, if a task t has to emulate several SI executions in software because reconfiguring the corresponding hardware implementations is not completed yet, then it might be more efficient to schedule other tasks first, depending on the soft-deadlines of the tasks, until the reconfigurations of that task t are completed. In comparison to other task schedulers (earliest deadline first, rate monotonic scheduling, and round robin), PATS achieves on average a 1.45x better system tardiness (i.e., the sum of cycles by which tasks miss their deadlines). Additionally, PATS reduces the make span (i.e. the time when all tasks have completed all of their jobs) on average by 1.17x (up to 1.58x). Especially in challenging multi-tasking scenarios with tight deadlines or a small reconfigurable fabric PATS performs significantly better than other task schedulers do.
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