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

Shoaib Akram, Jennifer Sartor, Kathryn S McKinley, Lieven Eeckhout

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Non-volatile memories (NVM) offer greater capacity than DRAM but suffer from high latency and low write endurance. Hybrid memories combine DRAM and NVM to form scalable memory systems with the promise of high capacity, low energy consumption, and high endurance. Automatically managing hybrid NVM-DRAM memories to achieve their promise without changing user applications or their programming models remains an open question. This paper uses garbage collection in managed languages to exploit NVM capacity while preventing NVM wear out in hybrid memories with no changes to the programming model. We introduce profile-driven write-rationing garbage collection. Allocation sites that produce frequently written objects are predicted based on previous program executions. Objects are initially allocated in a DRAM nursery space. The collector copies surviving nursery objects from highly written sites to a mature DRAM space and read-mostly objects to a mature NVM space.Write-intensity prediction for 15 Java benchmarks accurately places objects in the correct space, eliminating expensive object monitoring from prior write-rationing garbage collectors. Furthermore, our technique exposes a Pareto tradeoff between DRAM usage and NVM lifetime, unlike prior work. Experimental results on NUMA hardware that emulates hybrid NVM-DRAM memory demonstrates that profile-driven write-rationing garbage collection reduces the number of writes to NVM compared to prior work to extend its lifetime, maximizes the use of NVM for its capacity, and achieves good performance.
Original languageEnglish
Article number9
Pages (from-to)1-27
Number of pages27
JournalJournal Proceedings of the ACM on Measurement and Analysis of Computing Systems - SIGMETRICS
Issue number1
Publication statusPublished - Mar 2019


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