Leveraging Existing Microarchitectural Structures to Improve First-Level Caching Efficiency

Detta är en avhandling från Uppsala : Acta Universitatis Upsaliensis

Sammanfattning: Low-latency data access is essential for performance. To achieve this, processors use fast first-level caches combined with out-of-order execution, to decrease and hide memory access latency respectively. While these approaches are effective for performance, they cost significant energy, leading to the development of many techniques that require designers to trade-off performance and efficiency.Way-prediction and filter caches are two of the most common strategies for improving first-level cache energy efficiency while still minimizing latency. They both have compromises as way-prediction trades off some latency for better energy efficiency, while filter caches trade off some energy efficiency for lower latency. However, these strategies are not mutually exclusive. By borrowing elements from both, and taking into account SRAM memory layout limitations, we proposed a novel MRU-L0 cache that mitigates many of their shortcomings while preserving their benefits. Moreover, while first-level caches are tightly integrated into the cpu pipeline, existing work on these techniques largely ignores the impact they have on instruction scheduling. We show that the variable hit latency introduced by way-misspredictions causes instruction replays of load dependent instruction chains, which hurts performance and efficiency. We study this effect and propose a variable latency cache-hit instruction scheduler, that identifies potential misschedulings, reduces instruction replays, reduces negative performance impact, and further improves cache energy efficiency.Modern pipelines also employ sophisticated execution strategies to hide memory latency and improve performance. While their primary use is for performance and correctness, they require intermediate storage that can be used as a cache as well. In this work we demonstrate how the store-buffer, paired with the memory dependency predictor, can be used to efficiently cache dirty data; and how the physical register file, paired with a value predictor, can be used to efficiently cache clean data. These strategies not only improve both performance and energy, but do so with no additional storage and minimal additional complexity, since they recycle existing cpu structures to detect reuse, memory ordering violations, and misspeculations.

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