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MICRO2022顶会

Page Size Aware Cache Prefetching

Georgios Vavouliotis, Gino Chacon, Lluc Alvarez, Paul V. Gratz, Daniel A. Jiménez, Marc Casas

2022年份
24被引次数
10顶会引用

摘要

The increase in working set sizes of contemporary applications outpaces the growth in cache sizes, resulting in frequent main memory accesses that deteriorate system performance due to the disparity between processor and memory speeds. Prefetching data blocks into the cache hierarchy ahead of demand accesses has proven successful at attenuating this bottleneck. However, spatial cache prefetchers operating in the physical address space leave significant performance on the table by limiting their pattern detection within 4KB physical page boundaries when modern systems use page sizes larger than 4KB to mitigate the address translation overheads. This paper exploits the high usage of large pages in modern systems to increase the effectiveness of spatial cache prefetching. We design and propose the Page-size Propagation Module (PPM), a μ\muarchitectural scheme that propagates the page size information to the lower-level cache prefetchers, enabling safe prefetching beyond 4KB physical page boundaries when the accessed blocks reside in large pages, at the cost of augmenting the first-level caches’ Miss Status Holding Register (MSHR) entries with one additional bit. PPM is compatible with any cache prefetcher without implying design modifications. We capitalize on PPM’s benefits by designing a module that consists of two page size aware prefetchers that inherently use different page sizes to drive prefetching. The composite module uses adaptive logic to dynamically enable the most appropriate page size aware prefetcher. Finally, we show that the proposed designs are transparent to which cache prefetcher is used. We apply the proposed page size exploitation techniques to four state-of-the-art spatial cache prefetchers. Our evaluation shows that our proposals improve single-core geomean performance by up to 8.1% (2.1% at minimum) over the original implementation of the considered prefetchers, across 80 memory-intensive workloads. In multi-core contexts, we report geomean speedups up to 7.7% across different cache prefetchers and core configurations.

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