Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application

Blesson Varghese, Javier Prades, Carlos Reano, Federico Silla

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

11 Citations (Scopus)
345 Downloads (Pure)


How can GPU acceleration be obtained as a service in a cluster? This question has become increasingly significant due to the inefficiency of installing GPUs on all nodes of a cluster. The research reported in this paper is motivated to address the above question by employing rCUDA (remote CUDA), a framework that facilitates Acceleration-as-a-Service (AaaS), such that the nodes of a cluster can request the acceleration of a set of remote GPUs on demand. The rCUDA framework exploits virtualisation and ensures that multiple nodes can share the same GPU. In this paper we test the feasibility of the rCUDA framework on a real-world application employed in the financial risk industry that can benefit from AaaS in the production setting. The results confirm the feasibility of rCUDA and highlight that rCUDA achieves similar performance compared to CUDA, provides consistent results, and more importantly, allows for a single application to benefit from all the GPUs available in the cluster without loosing efficiency.
Original languageEnglish
Title of host publication2015 11th IEEE International Conference on e-Science (e-Science)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Print)978-1-4673-9325-6
Publication statusPublished - Aug 2015
Event2015 IEEE 11th International Conference on e-Science (e-Science) - Munich, Germany
Duration: 31 Aug 201504 Sept 2015


Conference2015 IEEE 11th International Conference on e-Science (e-Science)

Bibliographical note

Acceptance rate: 25%


  • cloud computing
  • financial data processing
  • graphics processing units
  • parallel architectures
  • virtualisation
  • Acceleration-as-a-Service
  • GPU acceleration
  • financial application
  • financial risk industry
  • remote CUDA
  • virtualised GPU
  • Acceleration
  • Graphics processing units
  • Hardware
  • Kernel
  • Memory management
  • Servers
  • CUDA
  • GPU computing
  • rCUDA


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