Dynamic sharing of GPUs in cloud systems

The use of computational accelerators, specifically programmable GPUs, is becoming popular in cloud computing environments. Cloud vendors currently provide GPUs as dedicated resources to cloud users, which may result in under-utilization of the expensive GPU resources. In this work, we propose gCloud, a framework to provide GPUs as on-demand computing resources to cloud users. gCloud allows on-demand access to local and remote GPUs to cloud users only when the target GPU kernel is ready for execution. In order to improve the utilization of GPUs, gCloud efficiently shares the GPU resources among concurrent applications from different cloud users. Moreover, it reduces the inter-Application interference of concurrent kernels for GPU resources by considering the local and global memory, number of threads, and the number of thread blocks of each kernel. It schedules concurrent kernels on available GPUs such that the overall inter-Application interference across the cluster is minimal. We implemented gCloud as an independent module, and integrated it with the Open Stack cloud computing platform. Evaluation of gCloud using representative applications shows that it improves the utilization of GPU resources by 56.3% on average compared to the current state-of-The-Art systems that serialize GPU kernel executions. Moreover, gCloud significantly reduces the completion time of GPU applications, e.g., in our experiments of running a mix of 8 to 28 GPU applications on 4 NVIDIA Tesla GPUs, gCloud achieves up to 430% reduction in the total completion time.