在不影响二级缓存的情况下,对单核处理器的一级缓存进行基于阶段的重新配置

Avinash Kumar
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引用次数: 0

摘要

在设计时以这样一种方式配置缓存,以便在广泛的应用程序中产生较低的平均内存访问时间。这可能导致较差的性能,因为实际上,没有一个特定的缓存体系结构可以在广泛的应用程序中补充应用程序的固有需求。此外,应用程序在执行过程中遵循唯一的模式序列。这些被称为阶段的模式周期性地重复自己。有些阶段是稳定的,可以延续数十亿条指令。这些稳定阶段构成了应用程序执行的大部分。本文设计了一种新的可重构缓存架构,可以动态识别这些阶段,并根据吞吐量对缓存进行配置。对于缓存的重新配置,我们建议在保持大小不变的情况下改变其结合性。这种关联性的变化是针对第一级缓存进行的,并且得出的结论是,第一级缓存的关联性降低不会导致第二级缓存的缓存缺失增加。为此,我们描述了由于结合性变化而发生的第一级缓存缺失的行为,并将这些缺失很好地限制在第二级缓存中。因此,我们成功地提出了一个简单的方程,它可以仲裁缓存重新配置决策的阈值。阈值的决定取决于应用程序的实际运行时间,因此不需要为每个应用程序单独训练。
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Phase-based reconfiguration of level one cache for single-core processors without affecting second level cache
Caches are configured at design time in such a way so as to produce a low average memory access time across a wide range of applications. This may result in a poor performance, because effectively, no single specific cache architecture can complement with the application's innate requirements over a wide range of applications. Also an application follows a unique sequence of patterns during its execution. These patterns called phases repeats themselves periodically. Some phases are stable and extend for billions of instructions. These stable phases constitute the majority of the applications' execution. In this paper, a novel reconfigurable architecture is designed for caches which dynamically discerns these phases and configures the caches based on the throughput. For the reconfiguration of the caches, we have proposed the change in its associativity while keeping the size constant. This change in associativity is made for the first level cache and is concluded that a decrease in associativity at the first level cache does not result in any increase in the cache misses at the second level cache. For this, we delineate the behavior of the level one cache misses which occurs because of the change in the associativity and confine these misses well within the second level cache. As a consequence we have successfully come up with a simple equation which arbitrates the threshold value for the cache reconfiguration decision. This decision for the threshold value is a contingent on the actual running time of the application and hence does not need to be trained for each application separately.
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