Abstract:

With the enrichment of hardware functions and the gradual maturity of software development environment, GPU is widely used in the field of general purpose computing, and GPU clusters are more and more used for scientific computing on huge amounts of data. However, GPU consumes more power than CPU, so the GPU clusters have large power consumption if every cluster node hosts a GPU. Virtualization technology makes it possible that GPU is used for general purpose computing in a virtual machine. For the sake of using GPU efficiently, according to the features of GPU, a multitask oriented GPU virtualization solution is proposed, which can support dynamic scheduling and multi-user concurrency. Based on the existed solutions of GPU virtualization, we establish CUDA manage end to manage the GPU resources by taking into account the virtual machine communication between domain generality and task’s turnaround time. In order to achieve load balance and shorten the turnaround time, we set a value of integrated load evaluation. Through designing large scale matrix operations, we verify the feasibility and efficiency of GPU virtualization applied in the designed system.

Key words: general purpose computing on GPU;virtualization;CUDA;load balance