Basic Performance Measurements of the Intel Optane DC Persistent Memory Module

By Joseph Izraelevitz, Jian Yang, Lu Zhang, Juno Kim, Xiao Liu, Amirsaman Memaripour, Yun Joon Soh, Zixuan Wang, Yi Xu, Subramanya R. Dulloor and 2 more. arXiv.org, 2019

524 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/ab8bb5bc0f99f92d851c4402e540dc46c180a436

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Citations in the corpus

An Empirical Guide to the Behavior and Use of Scalable Persistent Memory (2019, unknown): “[30] have explored general performance characteristics.”
RecSSD: near data processing for solid state drive based recommendation inference (2021, background): “with SSD-like density, we anticipate the type of applications that leverage SSD based storage systems to widen [24].”
Swift: Delay is Simple and Effective for Congestion Control in the Datacenter (2020, background): “Flash access latency is 100 µ s, making milliseconds of network latency unacceptable, and NVMe is even more demanding [25].”
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing (2019, background): “In practice, a higher level of bandwidth saturation beyond this point becomes undesirable as memory latency starts to increase significantly [37].”
Roadmapping the next generation of silicon photonics (2023, unknown): “For O-band operations, modulators utilizing the quantum-confined”
AutoTM: Automatic Tensor Movement in Heterogeneous Memory Systems using Integer Linear Programming (2020, methodology): “Recently, this technology as been made available in the form of a standard byte-addressable DDR4 DIMM on the CPU memory bus, just like DRAM DIMMs [25] with the new Cascade lake based chipsets and is called Optane DC PMM (different from Optane SSD).”