Improving the Efficiency of Future Exascale Systems with rCUDA

Carlos Reaño, Javier Prades, Federico Silla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The computing power of supercomputers and data centers has noticeably grown during the last decades at the cost of an ever increasing energy demand. The need for energy (and power) of these facilities has finally limited the evolution of high performance computing, making that many researchers are concerned not only about performance but also about energy efficiency. However, despite the many concerns about energy consumption, the search for computing power continues. In this regard, the research on exascale systems, able to deliver 10^18 floating point operations per second, has reached a widely consensus that these systems should operate within a maximum power budget of 20 megawatts. Many efficiency improvements are necessary for achieving this goal. One of these improvements is the usage of ARM low-power processors, as the Mont-Blanc proposes. In this paper we propose the combined use of ARM processors with the remote GPU virtualization rCUDA framework as a way to improve efficiency even more. Results show that it is possible to speed up applications by more than 12x when rCUDA is used to access high-end GPUs.
Original languageEnglish
Title of host publication4th IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era (HiPINEB 2018): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages40-47
Number of pages8
ISBN (Electronic)978-1-5386-5088-2
ISBN (Print)978-1-5386-5089-9
DOIs
Publication statusPublished - 29 Mar 2018

Fingerprint Dive into the research topics of 'Improving the Efficiency of Future Exascale Systems with rCUDA'. Together they form a unique fingerprint.

  • Cite this

    Reaño, C., Prades, J., & Silla, F. (2018). Improving the Efficiency of Future Exascale Systems with rCUDA. In 4th IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era (HiPINEB 2018): Proceedings (pp. 40-47). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HiPINEB.2018.00014