Improving the Accuracy of Data Prefetching via Depth Estimation<br/>

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Improving the Accuracy of Data Prefetching via Depth Estimation

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 53460 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Sarbazi Azad, Hamid
Abstract:
Data Prefetcher is a central component in most processors. Different methods have been proposed with varying degrees of complexity and effectiveness. Recent research revisits pairwise-correlating data prefetching due to its extremely low overhead. Pairwise-correlating data prefetching, however, cannot accurately detect where data streams end. As a result, pairwise-correlating data prefetchers either expose low accuracy or they lose timeliness when they are performing multi degree prefetching. In this work, we propose a novel technique to detect where data streams end and hence, control the multi-degree prefetching in the context of pairwise-correlated prefetchers. The key idea is to have a separate metadata table that operates one step ahead of the main metadata table. This way, the runahead metadata table harnesses the degree of prefetching by allowing/disallowing the main metadata table to issue prefetch requests. We evaluate our proposal in the context of a four-core chip multiprocessor and show that it significantly reduces erroneous prefetches, providing up to 16.1% performance improvement on top of a state-of the-art pairwise-correlating prefetcher
Keywords:
Cache Memory ; Performance ; Data Prefetching ; Overprediction ; Pairwise-Correlating Data Prefetching ; Increasing Efficiency

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