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ProF: Probabilistic Hybrid Main Memory Management for High Performance and Fairness

Dmitry Knyaginin (Institutionen för data- och informationsteknik, Datorteknik (Chalmers)) ; Per Stenström (Institutionen för data- och informationsteknik, Datorteknik (Chalmers)) ; Vasileios Papaefstathiou (Institutionen för data- och informationsteknik, Datorteknik (Chalmers))
Göteborg : Chalmers University of Technology, 2016. - 12 s.
[Rapport]

Emerging Non-Volatile Memory (NVM) technologies revolutionize main memory design by enabling hybrid main memory with two partitions: M1 and M2. Such hybrid main memory is built from fast and expensive DRAM (M1) and slower but less expensive NVM (M2) realizing a large, cost-effective, and still high-performance main memory. We consider in this paper a flat, migrating hybrid memory in which hot data blocks are moved from M2 to M1. A challenging issue to manage such a hybrid memory is to achieve both high system-level performance and high fairness among individual programs in a multiprogrammed workload. This paper introduces ProF: Probabilistic hybrid main memory management for high performance and Fairness – a novel approach using the Bayes rule to classify which blocks to migrate to M1. ProF comprises i) a Probabilistic Data migration Mechanism (PDM) that decides which data to move between M1 and M2 to achieve high system performance, and ii) a Slowdown Estimation Mechanism (SEM ) that monitors individual program slowdown and guides PDM towards high fairness. We show that for the multiprogrammed workloads evaluated ProF improves fairness by 9% on average and up to 27% compared to the state-of-the-art, while out- performing it by 9% on average and up to 25%.


Technical Report 2016:05, ISSN 1652-926X



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Denna post skapades 2016-12-20.
CPL Pubid: 246387