An Empirical Guide to the Behavior and Use of Scalable Persistent Memory
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Abstract (arXiv)
After nearly a decade of anticipation, scalable nonvolatile memory DIMMs are finally commercially available with the release of Intel's 3D XPoint DIMM. This new nonvolatile DIMM supports byte-granularity accesses with access times on the order of DRAM, while also providing data storage that survives power outages. Researchers have not idly waited for real nonvolatile DIMMs (NVDIMMs) to arrive. Over the past decade, they have written a slew of papers proposing new programming models, file systems, libraries, and applications built to exploit the performance and flexibility that NVDIMMs promised to deliver. Those papers drew conclusions and made design decisions without detailed knowledge of how real NVDIMMs would behave or how industry would integrate them into computer architectures. Now that 3D XPoint NVDIMMs are actually here, we can provide detailed performance numbers, concrete guidance for programmers on these systems, reevaluate prior art for performance, and reoptimize persistent memory software for the real 3D XPoint DIMM. In this paper, we explore the performance properties and characteristics of Intel's new 3D XPoint DIMM at the micro and macro level. First, we investigate the basic characteristics of the device, taking special note of the particular ways in which its performance is peculiar relative to traditional DRAM or other past methods used to emulate NVM. From these observations, we recommend a set of best practices to maximize the performance of the device. With our improved understanding, we then explore the performance of prior art in application-level software for persistent memory, taking note of where their performance was influenced by our guidelines.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Aug 2019.
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Citing papers per year: 2021: 1; 2022: 4.
5 papers.
- 2021Plinius: Secure and Persistent Machine Learning Model Trainingcited by 3
- 2022Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analyticscited by 1
- 2022Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memorycited by 0
- 2022Accelerating Transfer Learning with Near-Data Computation on Cloud Object Storescited by 0
- 2022Learning to Rank Graph-based Application Objects on Heterogeneous Memoriescited by 0
- 2022Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics10 Dec 2022
- 2022Learning to Rank Graph-based Application Objects on Heterogeneous Memories4 Nov 2022
- 2022Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores16 Oct 2022
- 2022Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory25 Jul 2022
- 2021Plinius: Secure and Persistent Machine Learning Model Training7 Apr 2021