Acceleration-as-a-Service : exploiting virtualised GPUs for a financial application
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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.
Varghese , B , Prades , J , Reaño , C & Silla , F 2015 , Acceleration-as-a-Service : exploiting virtualised GPUs for a financial application . in 2015 IEEE 11th International Conference on e-Science (e-Science) (2015) . IEEE Computer Society , 11th IEEE International Conference on eScience , Munich , Germany , 31-4 September .conference
2015 IEEE 11th International Conference on e-Science (e-Science) (2015)
© 2015. IEEE. This is the accepted manuscript of a conference paper originally submitted to the 11th IEEE International Conference on eScience available from http://www.computer.org/csdl/proceedings/escience/index.html
This research was supported by an NVIDIA award, the EPSRC EP/K015745/1 grant, and the Generalitat Valenciana PROMETEOII/2013/009 grant of the PROMETEO program phase II. The authors acknowledge the generous support of Mellanox Technologies.
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