Proceedings ArticleDOI
GViM: GPU-accelerated virtual machines
Vishakha Gupta,Ada Gavrilovska,Karsten Schwan,Harshvardhan Kharche,Niraj Tolia,Vanish Talwar,Parthasarathy Ranganathan +6 more
- pp 17-24
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TLDR
GViM is presented, a system designed for virtualizing and managing the resources of a general purpose system accelerated by graphics processors and how such accelerators can be virtualized without additional hardware support.Abstract:
The use of virtualization to abstract underlying hardware can aid in sharing such resources and in efficiently managing their use by high performance applications. Unfortunately, virtualization also prevents efficient access to accelerators, such as Graphics Processing Units (GPUs), that have become critical components in the design and architecture of HPC systems. Supporting General Purpose computing on GPUs (GPGPU) with accelerators from different vendors presents significant challenges due to proprietary programming models, heterogeneity, and the need to share accelerator resources between different Virtual Machines (VMs).To address this problem, this paper presents GViM, a system designed for virtualizing and managing the resources of a general purpose system accelerated by graphics processors. Using the NVIDIA GPU as an example, we discuss how such accelerators can be virtualized without additional hardware support and describe the basic extensions needed for resource management. Our evaluation with a Xen-based implementation of GViM demonstrate efficiency and flexibility in system usage coupled with only small performance penalties for the virtualized vs. non-virtualized solutions.read more
Citations
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Proceedings Article
TimeGraph: GPU scheduling for real-time multi-tasking environments
TL;DR: TimeGraph is presented, a real-time GPU scheduler at the device-driver level for protecting important GPU workloads from performance interference and supports two priority-based scheduling policies in order to address the tradeoff between response times and throughput introduced by the asynchronous and non-preemptive nature of GPU processing.
Journal ArticleDOI
vCUDA: GPU-Accelerated High-Performance Computing in Virtual Machines
TL;DR: The vCUDA as discussed by the authors is a general-purpose graphics processing unit (GPGPU) computing solution for virtual machines (VMs) that allows applications executing within VMs to leverage hardware acceleration, which can be beneficial to the performance of a class of highperformance computing (HPC) applications.
Book ChapterDOI
A GPGPU transparent virtualization component for high performance computing clouds
TL;DR: The GPU Virtualization Service (gVirtuS) tries to fill the gap between in-house hosted computing clusters, equipped with GPGPUs devices, and pay-for-use high performance virtual clusters deployed via public or private computing clouds.
Proceedings ArticleDOI
vCUDA: GPU accelerated high performance computing in virtual machines
Lin Shi,Hao Chen,Jianhua Sun +2 more
TL;DR: The vCUDA framework as mentioned in this paper is a GPGPU (General Purpose Graphics Processing Unit) computing solution for virtual machines that allows applications executing within virtual machines (VMs) to leverage hardware acceleration, which can be beneficial to the performance of a class of HPC applications.
Proceedings ArticleDOI
Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework
TL;DR: A framework to enable applications executing within virtual machines to transparently share one or more GPUs is presented and it is found that even when contention is high the consolidation algorithm is effective in improving the throughput, and that the runtime overhead of the framework is low.
References
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