Abstract
The increasing demand for machine learning computation contributes to the convergence of high-performance computing and cloud computing, in which the virtualization of Graphics Processing Units (GPUs) becomes a critical issue. Although many GPGPU virtualization frameworks have been proposed, their performance is limited by the bandwidth of data transactions between the virtual machine (VM) and host. In this paper, we present a virtualization framework, qCUDA, to improve the performance of compute unified device architecture (CUDA) programs. qCUDA is based on the virtio framework, providing the para-virtualized driver and the device module for performing the interaction with the API remoting and memory management methods. In our test environment, qCUDA can achieve above 95% of the bandwidth efficiency for most results by comparing it with the native. Also, qCUDA has the features of flexibility and interposition. It can execute CUDA-compatible programs in the Linux and Windows VMs, respectively, on QEMU-KVM hypervisor for GPGPU virtualization.