Skip to main content
SpringerLink
Log in

Usage Scenarios for Byte-Addressable Persistent Memory in High-Performance and Data Intensive Computing

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Byte-addressable persistent memory (B-APM) presents a new opportunity to bridge the performance gap between main memory and storage. In this paper, we present the usage scenarios for this new technology, based on the capabilities of Intel’s DCPMM. We outline some of the basic performance characteristics of DCPMM, and explain how it can be configured and used to address the needs of memory and I/O intensive applications in the HPC (high-performance computing) and data intensive domains. Two decision trees are presented to advise on the configuration options for B-APM; their use is illustrated with two examples. We show that the flexibility of the technology has the potential to be truly disruptive, not only because of the performance improvements it can deliver, but also because it allows systems to cater for wider range of applications on homogeneous hardware.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Jackson A, Weiland M, Parsons M, Homölle B. An architecture for high performance computing and data systems using byte-addressable persistent memory. In Proc. the 2019 ISC High Performance International Workshops, June 2019, pp.258-274. 10.1007/978-3-030-34356-9 21.

  2. Izraelevitz J, Yang J, Zhang L et al. Basic performance measurements of the Intel Optane DC persistent memory module. arXiv:1903.05714, 2019. http://arxiv.org/abs/1903.05714, March 2020.

  3. Patil O, Ionkov L, Lee J et al. Performance characterization of a DRAM-NVM hybrid memory architecture for HPC applications using Intel Optane DC persistent memory modules. In Proc. the International Symposium on Memory Systems, September 2019, pp.288-303. https://doi.org/10.1145/3357526.3357541.

  4. Mason T, Doudali T D, Seltzer M, Gavrilovska A. Unexpected performance of Intel® Optane™ DC persistent memory. IEEE Computer Architecture Letters, 2020, 19(1): 55-58. https://doi.org/10.1109/LCA.2020.2987303.

    Article  Google Scholar 

  5. Clark S J, Segall M D, Pickard C J et al. First principles methods using CASTEP. Zeitschrift für Kristallographie, 2005, 220: 567-570. https://doi.org/10.1524/zkri.220.5.567.65075.

  6. Weiland M, Brunst H, Quintino T et al. An early evaluation of Intel’s Optane DC persistent memory module and its impact on high-performance scientific applications. In Proc. the International Conference for High Performance Computing, Networking, Storage and Analysis, Nov. 2019, Article No. 76. https://doi.org/10.1145/3295500.3356159.

  7. Vef M A, Moti N, Süß T et al. GekkoFS—A temporary burst buffer file system for HPC applications. J. Comput. Sci. Technol., 2020, 35(1): 72-91. https://doi.org/10.1007/s11390-020-9797-6.

  8. Brinkmann A, Mohror K, Yu W et al. Ad hoc file systems for high-performance computing. J. Comput. Sci. Technol., 2020, 35(1): 4-26. https://doi.org/10.1007/s11390-020-9801-1.

  9. Smart S, Quintino T, Raoult B. A scalable object store for meteorological and climate data. In Proc. the Platform for Advanced Scientific Computing Conference, June 2017, Article No. 13. https://doi.org/10.1145/3093172.3093238.

  10. Smart S, Quintino T, Raoult B. A high-performance distributed object-store for exascale numerical weather prediction and climate. In Proc. the Platform for Advanced Scientific Computing Conference, June 2019, Article No. 16. https://doi.org/10.1145/3324989.3325726.

  11. Weiland M, Jackson A, Johnson N, Parsons M. Exploiting the performance benefits of storage class memory for HPC and HPDA workows. Journal of Supercomputing Frontiers and Innovations, 2018, 5(1): 79-94. https://doi.org/10.14529/jsfi180105.

  12. Miranda A, Jackson A, Tocci T, Panourgias I, Nou R. NORNS: Extending slurm to support data-driven workflows through asynchronous data staging. In Proc. the 2019 IEEE International Conference on Cluster Computing, Sept. 2019. https://doi.org/10.1109/CLUSTER.2019.8891014.

  13. Brown N, Weiland M, Hill A et al. A highly scalable Met Office NERC Cloud model. In Proc. the 3rd International Conference on Exascale Applications and Software, April 2015, pp.132-137. https://doi.org/10.5555/2820083.2820108.

Download references

Author information

Authors and Affiliations

  1. EPCC, The University of Edinburgh, Edinburgh, EH8 9BT, U.K.

    Michèle Weiland

  2. System Vertrieb Alexander GmbH, 33106, Paderborn, Germany

    Bernhard Homölle

Authors
  1. Michèle Weiland
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernhard Homölle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michèle Weiland.

Supplementary Information

ESM 1

(PDF 187 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weiland, M., Homölle, B. Usage Scenarios for Byte-Addressable Persistent Memory in High-Performance and Data Intensive Computing. J. Comput. Sci. Technol. 36, 110–122 (2021). https://doi.org/10.1007/s11390-020-0776-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-020-0776-8

Keywords

Search

Navigation