Basic Performance Measurements of the Intel Optane DC Persistent Memory Module
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Abstract (arXiv)
Scalable nonvolatile memory DIMMs will finally be commercially available with the release of the Intel Optane DC Persistent Memory Module (or just "Optane DC PMM"). 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. This work comprises the first in-depth, scholarly, performance review of Intel's Optane DC PMM, exploring its capabilities as a main memory device, and as persistent, byte-addressable memory exposed to user-space applications. This report details the technologies performance under a number of modes and scenarios, and across a wide variety of macro-scale benchmarks. Optane DC PMMs can be used as large memory devices with a DRAM cache to hide their lower bandwidth and higher latency. When used in this Memory (or cached) mode, Optane DC memory has little impact on applications with small memory footprints. Applications with larger memory footprints may experience some slow-down relative to DRAM, but are now able to keep much more data in memory. When used under a file system, Optane DC PMMs can result in significant performance gains, especially when the file system is optimized to use the load/store interface of the Optane DC PMM and the application uses many small, persistent writes. For instance, using the NOVA-relaxed NVMM file system, we can improve the performance of Kyoto Cabinet by almost 2x. Optane DC PMMs can also enable user-space persistence where the application explicitly controls its writes into persistent Optane DC media. In our experiments, modified applications that used user-space Optane DC persistence generally outperformed their file system counterparts. For instance, the persistent version of RocksDB performed almost 2x faster than the equivalent program utilizing an NVMM-aware file system.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Mar 2019.
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Citing papers per year: 2021: 3; 2022: 4; 2023: 1; 2024: 0; 2025: 1.
9 papers.
- 2021RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inferencecited by 35
- 2021FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Acceleratorcited by 12
- 2021Plinius: Secure and Persistent Machine Learning Model Trainingcited by 3
- 2025Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storagecited by 3
- 2022Non-Volatile Memory Accelerated Posterior Estimationcited by 0
- 2022Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysiscited by 0
- 2022Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memorycited by 0
- 2022Learning to Rank Graph-based Application Objects on Heterogeneous Memoriescited by 0
- 2023MTrainS: Improving DLRM training efficiency using heterogeneous memoriescited by 0
- 2025Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage6 Jun 2025
- 2023MTrainS: Improving DLRM training efficiency using heterogeneous memories19 Apr 2023
- 2022Learning to Rank Graph-based Application Objects on Heterogeneous Memories4 Nov 2022
- 2022Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory25 Jul 2022
- 2022Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis21 Feb 2022
- 2022Non-Volatile Memory Accelerated Posterior Estimation21 Feb 2022
- 2021FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator16 Jun 2021
- 2021Plinius: Secure and Persistent Machine Learning Model Training7 Apr 2021
- 2021RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference29 Jan 2021