Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Databases

arXiv:2304.00953 (cs)
[Submitted on 3 Apr 2023]

Title:Energy Consumption Evaluation of Optane DC Persistent Memory for Indexing Data Structures

Authors:Manolis Katsaragakis, Christos Baloukas, Lazaros Papadopoulos, Verena Kantere, Francky Catthoor, Dimitrios Soudris
View PDF
Abstract:The Intel Optane DC Persistent Memory (DCPM) is an attractive novel technology for building storage systems for data intensive HPC applications, as it provides lower cost per byte, low standby power and larger capacities than DRAM, with comparable latency. This work provides an in-depth evaluation of the energy consumption of the Optane DCPM, using well-established indexes specifically designed to address the challenges and constraints of the persistent memories. We study the energy efficiency of the Optane DCPM for several indexing data structures and for the LevelDB key-value store, under different types of YCSB workloads. By integrating an Optane DCPM in a memory system, the energy drops by 71.2% and the throughput increases by 37.3% for the LevelDB experiments, compared to a typical SSD storage solution.
Comments: 10 pages Has been accepted and presented to IEEE International Conference on High Performance Computing 2022(HiPC), Bengaluru, India
Subjects: Databases (cs.DB)
Cite as: arXiv:2304.00953 [cs.DB]
  (or arXiv:2304.00953v1 [cs.DB] for this version)
  https://doi.org/10.48550/arXiv.2304.00953

Submission history

From: Manolis Katsaragakis [view email]
[v1] Mon, 3 Apr 2023 13:16:46 UTC (2,225 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.DB
< prev   |   next >
new | recent | 2023-04
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)