Regresar

Toward a transparent and efficient GPU cloudification architecture

Abstract:

The cloud model allows the access to a vast amount of computational resources, alleviating the need for acquisition and maintenance costs on a pay-per-use basis. However, other resources, such as (GPUs), have not been fully adapted to this model. Many areas would benefit from suitable cloud solutions based on GPUs: video encoding, sequencing in bioinformatics, scene rendering in remote gaming, or machine learning. Cloud providers offer local and exclusive access to GPUs by using PCI passthrough. This limitation can be overcome by integrating new virtual GPUs (vGPUs) in cloud infrastructures or by providing mechanisms to cloudify existing GPUs, cloudified GPUs (cGPUs), which do not support native virtualization. The proposed architecture enables an effective and transparent integration of cGPUs in public cloud infrastructures. Our solution offers several access modes (local/remote and exclusive/shared) and configures autonomously its components by integrating with the message middleware of the cloud infrastructure. A prototype of the proposed architecture has been evaluated in a real cloud deployment. Experiments assess overhead in the infrastructure and performance of GPU-based applications by considering three different programs: matrix multiplication, sequencing read alignment, and Monte-Carlo on multiple GPUs. Results show that our solution introduces low impact both on the infrastructure and the performance of applications.