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Context-Aware Speculative Prefetch for Soft Real-Time Applications

机译:用于软实时应用程序的上下文感知的推测性预取

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摘要

Dynamically reconfigurable computing devices have the ability to adapt their hardware to application demands, providing the performance of hardware acceleration, as well as high flexibility, at competitive costs. For these reasons, FPGA-based reconfigurable systems are becoming popular in many application domains, including soft real-time computing. Unfortunately, one of their biggest limitations is the high reconfiguration overhead. One method to overcome this problem is configuration prefetching, which tries to reduce the reconfiguration penalty by preloading modules on the FPGA before they are needed, and overlapping the reconfiguration with useful computation. In this paper we present a speculative approach to context-aware inter-procedural configuration prefetching that provides statistical guarantees by minimizing the alpha-percentile of the execution time distribution of a soft real-time application. Our method uses profile information and takes into account the calling context of a procedure in order to generate better prefetch solutions. We also propose a middleware needed to apply the context-dependent prefetches at run-time. Our experiments show that the developed algorithm outperforms the previous state-of-art.
机译:动态可重新配置的计算设备具有使其硬件适应应用程序需求的能力,以具有竞争力的成本提供了硬件加速性能以及高灵活性。由于这些原因,基于FPGA的可重配置系统在包括软实时计算在内的许多应用领域中正变得越来越流行。不幸的是,它们最大的限制之一是高配置开销。解决该问题的一种方法是配置预取,该方法通过在需要之前在FPGA上预加载模块,并通过有用的计算来重叠重新配置来尝试减少重新配置的代价。在本文中,我们提出了一种上下文相关的过程间配置预取的推测方法,该方法通过最小化软实时应用程序的执行时间分布的alpha百分位数来提供统计保证。我们的方法使用配置文件信息,并考虑过程的调用上下文,以便生成更好的预取解决方案。我们还提出了一种在运行时应用依赖于上下文的预取所需的中间件。我们的实验表明,所开发的算法优于以前的最新技术。

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