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Compiler-based adaptive fetch throttling for energy-efficiency

机译:基于编译器的自适应访存节流以提高能效

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Front-end instruction delivery accounts for a significant fraction of energy consumption in dynamically scheduled superscalar processors. Different front-end throttling techniques have been introduced to reduce the chip-wide energy consumption caused by redundant fetching. Hardware-based techniques, such as flow-based throttling, could reduce the energy consumption considerably, but with a high performance loss. On the other hand, compiler-based IPC-estimation-driven software fetch throttling (CFT) techniques result in relatively low performance degradation, which is desirable for high-performance processors. However, their energy savings are limited by the fact that they typically use a predefined fixed low IPC-threshold to control throttling. In this paper, we propose a compiler-based adaptive fetch throttling (CAFT) technique that allows changing the throttling threshold dynamically at runtime. Instead of using a fixed threshold, our technique uses the decode/issue difference (DID) to assist the fetch throttling decision based on the statically estimated IPC. Changing the threshold dynamically makes it possible to throttle at a higher estimated IPC, thus increasing the throttling opportunities and resulting in larger energy savings. We demonstrate that CAFT could increase the energy savings significantly compared to CFT, while preserving its benefit of low performance loss. Our simulation results show that the proposed technique doubles the energy-delay product (EDP) savings compared to the fixed threshold throttling and achieves a 6.1% average EDP saving.
机译:在动态调度的超标量处理器中,前端指令传递占据了很大一部分能耗。已经引入了不同的前端节流技术,以减少由于冗余获取而导致的整个芯片范围的能量消耗。基于硬件的技术(例如基于流的节流)可以显着降低能耗,但会带来很高的性能损失。另一方面,基于编译器的IPC估计驱动的软件访存节流(CFT)技术导致相对较低的性能下降,这是高性能处理器所希望的。但是,由于它们通常使用预定义的固定低IPC阈值来控制节流,因此限制了它们的节能。在本文中,我们提出了一种基于编译器的自适应提取节流(CAFT)技术,该技术允许在运行时动态更改节流阈值。代替使用固定的阈值,我们的技术使用解码/问题差异(DID)来基于静态估计的IPC来协助获取节流决策。动态更改阈值可以在较高的IPC估计值上进行节流,从而增加了节流的机会,并节省了更多的能源。我们证明,与CFT相比,CAFT可以显着提高能耗,同时保留其低性能损失的优势。我们的仿真结果表明,与固定阈值节流相比,所提出的技术使能量延迟积(EDP)节省了两倍,并实现了6.1%的平均EDP节省。

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