Improving the performance of physics applications in atom-based clusters with rCUDA

Federico Silla, Javier Prades*, Elvira Baydal, Carlos Reaño

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
150 Downloads (Pure)


Traditionally, High-Performance Computing (HPC) has been associated with large power requirements. The reason was that chip makers of the processors typically employed in HPC deployments have always focused on getting the highest performance from their designs, regardless of the energy their processors may consume. Actually, for many years only heat dissipation was the real barrier for achieving higher performance, at the cost of higher energy consumption. However, a new trend has recently appeared consisting on the use of low-power processors for HPC purposes. The MontBlanc and Isambard projects are good examples of this trend. These proposals, however, do not consider the use of GPUs. In this paper we propose to use GPUs in this kind of low-power processor based HPC deployments by making use of the remote GPU virtualization mechanism. To that end, we leverage the rCUDA middleware in a hybrid cluster composed of low-power Atom-based nodes and regular Xeon-based nodes equipped with GPUs. Our experiments show that, by making use of rCUDA, the execution time of applications belonging to the physics domain is noticeably reduced, achieving a speed up of up to 140x with just one remote NVIDIA V100 GPU with respect to the execution of the same applications using 8 Atom-based nodes. Additionally, a rough energy consumption estimation reports improvements in energy demands of up to 37x.

Original languageEnglish
Pages (from-to)160-178
Number of pages19
JournalJournal of Parallel and Distributed Computing
Early online date20 Nov 2019
Publication statusPublished - Mar 2020


  • GPU virtualization
  • InfiniBand
  • Low-power processors
  • Physics applications
  • rCUDA

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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