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Title: Opportunities for nonvolatile memory systems in extreme-scale high-performance computing

Abstract

For extreme-scale high-performance computing systems, system-wide power consumption has been identified as one of the key constraints moving forward, where DRAM main memory systems account for about 30 to 50 percent of a node's overall power consumption. As the benefits of device scaling for DRAM memory slow, it will become increasingly difficult to keep memory capacities balanced with increasing computational rates offered by next-generation processors. However, several emerging memory technologies related to nonvolatile memory (NVM) devices are being investigated as an alternative for DRAM. Moving forward, NVM devices could offer solutions for HPC architectures. Researchers are investigating how to integrate these emerging technologies into future extreme-scale HPC systems and how to expose these capabilities in the software stack and applications. In addition, current results show several of these strategies could offer high-bandwidth I/O, larger main memory capacities, persistent data structures, and new approaches for application resilience and output postprocessing, such as transaction-based incremental checkpointing and in situ visualization, respectively.

Authors:
 [1];  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1331077
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Computing in Science and Engineering
Additional Journal Information:
Journal Volume: 17; Journal Issue: 2; Journal ID: ISSN 1521-9615
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; high performance computing; energy use; computer software; data visualization; researchers

Citation Formats

Vetter, Jeffrey S., and Mittal, Sparsh. Opportunities for nonvolatile memory systems in extreme-scale high-performance computing. United States: N. p., 2015. Web. doi:10.1109/MCSE.2015.4.
Vetter, Jeffrey S., & Mittal, Sparsh. Opportunities for nonvolatile memory systems in extreme-scale high-performance computing. United States. doi:10.1109/MCSE.2015.4.
Vetter, Jeffrey S., and Mittal, Sparsh. Mon . "Opportunities for nonvolatile memory systems in extreme-scale high-performance computing". United States. doi:10.1109/MCSE.2015.4. https://www.osti.gov/servlets/purl/1331077.
@article{osti_1331077,
title = {Opportunities for nonvolatile memory systems in extreme-scale high-performance computing},
author = {Vetter, Jeffrey S. and Mittal, Sparsh},
abstractNote = {For extreme-scale high-performance computing systems, system-wide power consumption has been identified as one of the key constraints moving forward, where DRAM main memory systems account for about 30 to 50 percent of a node's overall power consumption. As the benefits of device scaling for DRAM memory slow, it will become increasingly difficult to keep memory capacities balanced with increasing computational rates offered by next-generation processors. However, several emerging memory technologies related to nonvolatile memory (NVM) devices are being investigated as an alternative for DRAM. Moving forward, NVM devices could offer solutions for HPC architectures. Researchers are investigating how to integrate these emerging technologies into future extreme-scale HPC systems and how to expose these capabilities in the software stack and applications. In addition, current results show several of these strategies could offer high-bandwidth I/O, larger main memory capacities, persistent data structures, and new approaches for application resilience and output postprocessing, such as transaction-based incremental checkpointing and in situ visualization, respectively.},
doi = {10.1109/MCSE.2015.4},
journal = {Computing in Science and Engineering},
number = 2,
volume = 17,
place = {United States},
year = {Mon Jan 12 00:00:00 EST 2015},
month = {Mon Jan 12 00:00:00 EST 2015}
}

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Cited by: 21 works
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