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A hybrid intelligent system to improve predictive accuracy for cache prefetching

机译：一种混合智能系统，可提高高速缓存预取的预测准确性

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摘要

Cache being the fastest medium in memory hierarchy has a vital role to play for fully exploiting available resources, concealing latencies in 10 operations, languishing the impact of these latencies and hence in improving system response time. Despite plenty of efforts made, caches alone cannot comprehend larger storage requirements without prefetching. Cache prefetching is speculatively fetching data to restrain all delays. However, effective prefetching requires a strong prediction mechanism to load relevant data with higher degree of accuracy. In order to ameliorate the predictive performance of cache prefetching, we applied the hybrid of two Al approaches named case based reasoning (CBR) and artificial neural networks (ANN). CBR maintains the past experience and ANN are used in adaptation phase of CBR instead of employing static rule base. The novelty of technique in this domain is valued due to hybrid of two approaches as well as usage of suffix tree in populating the CBR's case base. Suffix trees provide rich data patterns for populating case base and greatly enhanced the overall performance. A number of evaluations from different aspects with varying parameters are presented (along with some findings) where the efficacy of our technique is affirmed with improved predictive accuracy and reduced level of associated costs.
机译：缓存是内存层次结构中最快的介质，对于充分利用可用资源，隐藏10个操作中的延迟，减轻这些延迟的影响并从而改善系统响应时间至关重要。尽管付出了很多努力，但如果不进行预取，仅凭缓存就无法理解更大的存储需求。缓存预取是推测性地获取数据以限制所有延迟。但是，有效的预取需要强大的预测机制才能以更高的准确度加载相关数据。为了改善高速缓存预取的预测性能，我们应用了两种基于案例推理（CBR）和人工神经网络（ANN）的Al方法的混合。 CBR保留了过去的经验，并且在CBR的适应阶段中使用了ANN，而不是采用静态规则库。由于两种方法的混合以及后缀树在填充CBR案例库中的使用，该领域中的技术新颖性受到重视。后缀树为填充案例库提供了丰富的数据模式，并大大提高了整体性能。提出了许多来自不同方面的评估，这些评估具有不同的参数（以及一些发现），在此方面，我们的技术效力得到了肯定，并提高了预测准确性，并降低了相关成本。

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