Abstract
Many data centers currently use virtual machines (VMs) to achieve a more efficient usage of hardware resources. However, current virtualization solutions, such as Xen, do not easily provide graphics processing unit (GPU) accelerators to applications running in the virtualized domain with the flexibility usually required in data centers (i.e., managing virtual GPU instances and concurrently sharing them among several VMs). Remote GPU virtualization frameworks such as the rCUDA solution may address this problem.
In this work we analyze the use of the rCUDA framework to accelerate scientific applications running inside Xen VMs. Results show that the use of the rCUDA framework is a feasible approach, featuring a very low overhead if an InfiniBand fabric is already present in the cluster.
- NVIDIA GRID Technology. www.nvidia.com/object/grid-technology.html, 2015.Google Scholar
- J. Song et al. KVMGT: a full GPU virtualization solution. 2014.Google Scholar
- S. N. Laboratories. Lammps molecular dynamics simulator. lammps.sandia.gov/, 2013.Google Scholar
- Y. Liu et al. CUDA-MEME: Accelerating motif discovery in biological sequences using GPUs. Pattern Recognition Letters, 31(14), 2010. Google Scholar
Digital Library
- Y. Liu et al. CUDASWw++ 3.0: accelerating smith-waterman protein database search by GPUs. BMC Bioinformatics, 14(1), 2013.Google Scholar
- C. Reaño et al. Local and Remote GPUs Perform Similar with EDR 100G InfiniBand. Middleware Conference, 2015. Google Scholar
Digital Library
- P. D. Vouzis el at. GPU-BLAST: Using graphics processors to accelerate protein sequence alignment. Bioinformatics, 2010. Google Scholar
Digital Library
Recommendations
CUDA acceleration for Xen virtual machines in infiniband clusters with rCUDA
PPoPP '16: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel ProgrammingMany data centers currently use virtual machines (VMs) to achieve a more efficient usage of hardware resources. However, current virtualization solutions, such as Xen, do not easily provide graphics processing unit (GPU) accelerators to applications ...
On the effect of using rCUDA to provide CUDA acceleration to Xen virtual machines
Nowadays, many data centers use virtual machines (VMs) in order to achieve a more efficient use of hardware resources. The use of VMs provides a reduction in equipment and maintenance expenses as well as a lower electricity consumption. Nevertheless, ...
Providing CUDA Acceleration to KVM Virtual Machines in InfiniBand Clusters with rCUDA
Distributed Applications and Interoperable SystemsAbstractThere is a trend towards using graphics processing units (GPUs) not only for graphics visualization, but also for accelerating scientific applications. But their use for this purpose is not without disadvantages: GPUs increase costs and energy ...






Comments