Acceleration-as-a-Service: exploiting virtualised GPUs for a financial application

Blesson Varghese, Javier Prades, Carlos Reaño, Federico Silla

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

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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 IEEE 11th International Conference on e-Science (e-Science) (2015)
PublisherIEEE Computer Society
Number of pages10
Publication statusPublished - 1 Sept 2015
Event11th IEEE International Conference on eScience - Ludwig-Maximilians-Universität , Munich, Germany
Duration: 31 Aug 20154 Sept 2015


Conference11th IEEE International Conference on eScience
Internet address


  • rCUDA
  • GPU computing
  • Virtualisation
  • Acceleration-as-a-service
  • CUDA


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