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Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application
'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.Comment: 11th IEEE International Conference on eScience (IEEE eScience) -
Munich, Germany, 201
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