Affiliation:
1. Norwegian University of Science and Technology (NTNU), Trondheim, Norway
2. University of Edinburgh, Edinburgh, United Kingdom
Abstract
The front-end bottleneck is a well-established problem in server workloads owing to their deep software stacks and large instruction footprints. Despite years of research into effective L1-I and BTB prefetching, state-of-the-art techniques force a trade-off between metadata storage cost and performance. Temporal Stream prefetchers deliver high performance but require a prohibitive amount of metadata to accommodate the temporal history. Meanwhile, BTB-directed prefetchers incur low cost by using the existing in-core branch prediction structures but fall short on performance due to BTB’s inability to capture the massive control flow working set of server applications. This work overcomes the fundamental limitation of BTB-directed prefetchers, which is capturing a large control flow working set within an affordable BTB storage budget. We re-envision the BTB organization to maximize its control flow coverage by observing that an application’s instruction footprint can be mapped as a combination of its unconditional branch working set and, for each unconditional branch, a spatial encoding of the cache blocks around the branch target. Effectively capturing a map of the application’s instruction footprint in the BTB enables highly effective BTB-directed prefetching that outperforms the state-of-the-art prefetchers by up to 10% for equivalent storage budget.
Funder
Research Council of Norway
Publisher
Association for Computing Machinery (ACM)
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Database management system performance comparisons: A systematic literature review;Journal of Systems and Software;2024-02
2. Protean: Resource-efficient Instruction Prefetching;Proceedings of the International Symposium on Memory Systems;2023-10-02
3. A Storage-Effective BTB Organization for Servers;2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2023-02