Enhancing Cloud-Based Servers by GPU/CPU Virtualization Management

This paper proposes to add the multithreaded Graphic Processing Units (GPUs) to some virtual machines (VMs) in the existing cloud-based VM groups. To handle the multidimensional or multithreaded computing that a CPU cannot process quickly by a GPU that has hundreds of Arithmetic Logic Units (ALUs), and to regulate the time for initiating physical servers by real-time thermal migration, our proposed scheme can enhance the system performance and reduce the energy consumption of long-term computing. Four major techniques in this paper include: (1) GPU virtualization, (2) Hypervisor for GPU, (3) Thermal migration implementation, and (4) Estimation of multithreaded tasks. In no matter quantum mechanics, astronomy, fluid mechanics, or atmospheric simulation and prediction, a GPU suits not only parallel multithreaded computing for its tens of times performance than a CPU, but also multidimensional array operations for its excellent efficiency. Therefore, how to distribute the computing performance of CPUs and GPUs appropriately becomes a significant issue. In general cloud computing applications, it is rarely seen that GPUs can outperform CPUs. Furthermore, for groups of virtual servers, many tasks actually can be completed by CPUs without the support of GPUs. Thus, it is a waste of resources to implement GPUs to all physical servers. For this reason, by integrating with the migration characteristic of VMs, our proposed scheme can estimate whether to compute tasks by physical machines with GPUs or not. In estimating tasks, we use Amdahl’s law to estimate the overall performance include communication delays, Synchronization overhead and me possible additional burden.