There is a trend towards using graphics processing units (GPUs) not only for graphics visualization, but also for accelerating scientific applications. But their use for this purpose is not without disadvantages: GPUs increase costs and energy consumption. Furthermore, GPUs are generally underutilized. Using virtual machines could be a possible solution to address these problems, however, current solutions for providing GPU acceleration to virtual machines environments, such as KVM or Xen, present some issues. In this paper we propose the use of remote GPUs to accelerate scientific applications running inside KVM virtual machines. Our analysis shows that this approach could be a possible solution, with low overhead when used over InfiniBand networks.
|Title of host publication||Distributed Applications and Interoperable Systems|
|Subtitle of host publication||16th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS)|
|Publication status||Published - 24 May 2016|
|Name||Lecture Notes in Computer Science|
Perez, F., Reaño, C., & Silla, F. (2016). Providing CUDA Acceleration to KVM Virtual Machines in InfiniBand Clusters with rCUDA. In Distributed Applications and Interoperable Systems : 16th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS) (pp. 82-95). (Lecture Notes in Computer Science; Vol. 9687). Springer. https://doi.org/10.1007/978-3-319-39577-7_7