NVM in Data Storage: A Post-Optane Future

The dynamic evolution of non-volatile memory (NVM) technologies from Read-Only Memory (ROM) to flash memory, and recent innovations in Magnetoresistive RAM (MRAM), Phase Change Memory (PCM), and Resistive RAM (ReRAM) signify a pivotal shift in data storage capabilities and applications. This progression, marked by enhancements in performance and density, fills the gap between Dynamic RAM (DRAM) and flash storage, meeting the demands of data-rich domains such as high-performance computing and databases. The integration of NVM like the formerly commercially available Optane Persistent Memory (PMem) has introduced a paradigm shift in storage system design, addressing challenges related to latency, data proximity, memory constraints, and energy efficiency which are critical in computing. Despite facing hurdles, such as performance asymmetry in Optane PMem, the potential of NVM in data storage systems has been shown with research focusing on speeding up indexing, small data accesses, and cache-less I/O paths. We review existing and emerging NVM technologies based on their physical properties and application in data storage. We underscore the significance of NVM in offering unique access characteristics that address the limitations of traditional storage devices, thereby extending the data storage landscape. We discuss the programming challenges and hardware-software strategies crucial for NVM’s seamless integration and widespread industry adoption. Additionally, we discuss the discontinuation of Optane, the ongoing challenges and the need for storage system designs to adapt to non-block based storage to leverage NVM’s performance benefits. We conclude that while advancements in NVM promise improved performance and endurance, practical availability depends on addressing challenges such as density, heat management, and reliability. Support in the existing software ecosystem is also crucial for its widespread adoption. Furthermore, Compute Express Link (CXL) is highlighted as a significant development that can streamline the memory hierarchy and support the adoption of NVM, enabling more flexible designs and effective usage of storage media in next-generation data storage systems. Overall, CXL offers a promising method to include NVM in future systems. However, in the meantime, data storage systems will rely on NVMe SSDs due to their performance and availability. But, we encourage further research into integrating NVM devices due to data management constraints with conventional block-based data management and advocate for the availability of NVM-ready software to users.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Storage
Published
2025-04-25
DOI
https://doi.org/10.1145/3731454
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

NVM in Data Storage: A Post-Optane Future

Michael Kühn, Sajad Karim, Gunter Saake, David Broneske et al.
1 citations
ACM Transactions on Storage
Advanced Memory and Neural Computing
0.73
article

NVM in Data Storage: A Post-Optane Future

Michael Kühn, Sajad Karim, Gunter Saake, David Broneske, Johannes Wünsche
article en
1 citations

Abstract

The dynamic evolution of non-volatile memory (NVM) technologies from Read-Only Memory (ROM) to flash memory, and recent innovations in Magnetoresistive RAM (MRAM), Phase Change Memory (PCM), and Resistive RAM (ReRAM) signify a pivotal shift in data storage capabilities and applications. This progression, marked by enhancements in performance and density, fills the gap between Dynamic RAM (DRAM) and flash storage, meeting the demands of data-rich domains such as high-performance computing and databases. The integration of NVM like the formerly commercially available Optane Persistent Memory (PMem) has introduced a paradigm shift in storage system design, addressing challenges related to latency, data proximity, memory constraints, and energy efficiency which are critical in computing. Despite facing hurdles, such as performance asymmetry in Optane PMem, the potential of NVM in data storage systems has been shown with research focusing on speeding up indexing, small data accesses, and cache-less I/O paths. We review existing and emerging NVM technologies based on their physical properties and application in data storage. We underscore the significance of NVM in offering unique access characteristics that address the limitations of traditional storage devices, thereby extending the data storage landscape. We discuss the programming challenges and hardware-software strategies crucial for NVM’s seamless integration and widespread industry adoption. Additionally, we discuss the discontinuation of Optane, the ongoing challenges and the need for storage system designs to adapt to non-block based storage to leverage NVM’s performance benefits. We conclude that while advancements in NVM promise improved performance and endurance, practical availability depends on addressing challenges such as density, heat management, and reliability. Support in the existing software ecosystem is also crucial for its widespread adoption. Furthermore, Compute Express Link (CXL) is highlighted as a significant development that can streamline the memory hierarchy and support the adoption of NVM, enabling more flexible designs and effective usage of storage media in next-generation data storage systems. Overall, CXL offers a promising method to include NVM in future systems. However, in the meantime, data storage systems will rely on NVMe SSDs due to their performance and availability. But, we encourage further research into integrating NVM devices due to data management constraints with conventional block-based data management and advocate for the availability of NVM-ready software to users.

ACM Transactions on StorageVol. 21(3)
German Centre for Higher Education Research and Science Studies (DE), Otto-von-Guericke-Universität Magdeburg (DE)
Openalex Percentile: Top 29%
Advanced Memory and Neural Computing
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.