Motivating Next-Generation OS Physical Memory Management for Terabyte-Scale NVMMs
Shivank Garg, Aravinda Prasad, Debadatta Mishra, Sreenivas Subramoney

TL;DR
This paper analyzes how traditional OS physical memory management is inadequate for modern hybrid memory systems with large, high-latency NVMMs, and argues for fundamental redesigns to optimize performance.
Contribution
It provides a comprehensive evaluation of Linux's memory management on hybrid DRAM-NVMM systems and highlights the need for new OS strategies tailored for terabyte-scale NVMMs.
Findings
Page allocation is negatively impacted by NVMM latency.
Conventional fragmentation management is inadequate for NVMMs.
Some traditional memory management functionalities remain unaffected.
Abstract
Software managed byte-addressable hybrid memory systems consisting of DRAMs and NVMMs offer a lot of flexibility to design efficient large scale data processing applications. Operating systems (OS) play an important role in enabling the applications to realize the integrated benefits of DRAMs' low access latency and NVMMs' large capacity along with its persistent characteristics. In this paper, we comprehensively analyze the performance of conventional OS physical memory management subsystems that were designed only based on the DRAM memory characteristics in the context of modern hybrid byte-addressable memory systems. To study the impact of high access latency and large capacity of NVMMs on physical memory management, we perform an extensive evaluation on Linux with Intel's Optane NVMM. We observe that the core memory management functionalities such as page allocation are negatively…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Data Storage Technologies · Parallel Computing and Optimization Techniques · Cloud Computing and Resource Management
