BibTex format
@unpublished{Papaphilippou:2019,
author = {Papaphilippou, P and Kelly, PHJ and Luk, W},
publisher = {arXiv},
title = {Pangloss: a novel Markov chain prefetcher.},
url = {https://arxiv.org/abs/1906.00877},
year = {2019}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - UNPB
AB - We present Pangloss, an efficient high-performance data prefetcher that approximates a Markov chain on delta transitions. With a limited information scope and space/logic complexity, it is able to reconstruct a variety of both simple and complex access patterns. This is achieved by a highly-efficient representation of the Markov chain to provide accurate values for transition probabilities. In addition, we have added a mechanism to reconstruct delta transitions originally obfuscated by the out-of-order execution or page transitions, such as when streaming data from multiple sources. Our single-level (L2) prefetcher achieves a geometric speedup of 1.7% and 3.2% over selected state-of-the-art baselines (KPCP and BOP). When combined with an equivalent for the L1 cache (L1 & L2), the speedups rise to 6.8% and 8.4%, and 40.4% over non-prefetch. In the multi-core evaluation, there seems to be a considerable performance improvement as well.
AU - Papaphilippou,P
AU - Kelly,PHJ
AU - Luk,W
PB - arXiv
PY - 2019///
TI - Pangloss: a novel Markov chain prefetcher.
UR - https://arxiv.org/abs/1906.00877
UR - http://hdl.handle.net/10044/1/75779
ER -