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Crystal Gazer: Profile-Driven Write-Rationing Garbage Collection for Hybrid Memories

Published: 20 June 2019 Publication History

Abstract

Emerging non-volatile memory (NVM) technologies offer greater capacity than DRAM. Unfortunately, production NVM exhibits high latency and low write endurance. Hybrid memory combines DRAM and NVM to deliver greater capacity, low latency, high en- durance, and low energy consumption. Write-rationing garbage col- lection mitigates NVM wear-out by placing highly-written objects in DRAM and the rest in NVM. Existing write-rationing garbage collectors dynamically monitor object writes to place highly writ- ten objects in DRAM. Unfortunately, monitoring writes incurs a non-negligible performance overhead. This work proposes Crystal Gazer, profile-driven write-rationing garbage collection for hybrid memories. Allocation sites are statically profiled, and highly writ- ten objects are predicted based on previous program executions. Unlike prior work, this paper exposes a Pareto trade-off between DRAM usage and NVM lifetime. Experimental results on an emula- tion platform show that Crystal Gazer eliminates the performance overhead of dynamic monitoring, while reducing more NVM writes than state-of-the-art write-rationing garbage collectors.

Reference

[1]
Shoaib Akram, Jennifer B. Sartor, Kathryn S. McKinley, and Lieven Eeckhout. 2018. Write-rationing Garbage Collection for Hybrid Memories. In Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) .

Cited By

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  • (2021)Sentinel: Efficient Tensor Migration and Allocation on Heterogeneous Memory Systems for Deep Learning2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA51647.2021.00057(598-611)Online publication date: Feb-2021

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cover image ACM Conferences
SIGMETRICS '19: Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
June 2019
113 pages
ISBN:9781450366786
DOI:10.1145/3309697
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2019

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Author Tags

  1. endurance
  2. garbage collection
  3. non-volatile memory (nvm)
  4. profiling
  5. write-intensity prediction

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SIGMETRICS '19
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SIGMETRICS '19 Paper Acceptance Rate 50 of 317 submissions, 16%;
Overall Acceptance Rate 459 of 2,691 submissions, 17%

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Cited By

View all
  • (2021)Sentinel: Efficient Tensor Migration and Allocation on Heterogeneous Memory Systems for Deep Learning2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA51647.2021.00057(598-611)Online publication date: Feb-2021

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