Software wear management for persistent memories

Vaibhav Gogte, William Wang, Stephan Diestelhorst, Aasheesh Kolli, Peter M. Chen, Satish Narayanasamy, Thomas F. Wenisch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations


The commercial release of byte-addressable persistent memories (PMs) is imminent. Unfortunately, these devices suffer from limited write endurance—without any wear management, PM lifetime might be as low as 1.1 months. Existing wear-management techniques introduce an additional indirection layer to remap memory across physical frames and require hardware support to track fine-grain wear. These mechanisms incur storage overhead and increase access latency and energy consumption. We present Kevlar, an OS-based wear-management technique for PM that requires no new hardware. Kevlar uses existing virtual memory mechanisms to remap pages, enabling it to perform both wear leveling—shuffling pages in PM to even wear; and wear reduction—transparently migrating heavily written pages to DRAM. Crucially, Kevlar avoids the need for hardware support to track wear at fine grain. Instead, it relies on a novel wear-estimation technique that builds upon Intel’s Precise Event Based Sampling to approximately track processor cache contents via a software-maintained Bloom filter and estimate write-back rates at fine grain. We implement Kevlar in Linux and demonstrate that it achieves lifetime improvement of 18.4× (avg.) over no wear management while incurring 1.2% performance overhead.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th USENIX Conference on File and Storage Technologies, FAST 2019
PublisherUSENIX Association
Number of pages19
ISBN (Electronic)9781939133090
StatePublished - 2019
Event17th USENIX Conference on File and Storage Technologies, FAST 2019 - Boston, United States
Duration: Feb 25 2019Feb 28 2019

Publication series

NameProceedings of the 17th USENIX Conference on File and Storage Technologies, FAST 2019


Conference17th USENIX Conference on File and Storage Technologies, FAST 2019
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Software


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