On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework

Montella, Raffaele; Giunta, Giulio; Laccetti, Giuliano; Lapegna, Marco; Palmieri, Carlo; Ferraro, Carmine; Pelliccia, Valentina; Hong, Cheol-Ho; Spence, Ivor; Nikolopoulos, Dimitrios S.

doi:10.1007/s10766-016-0462-1

Cited by 29 publications

(25 citation statements)

References 36 publications

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“…Full virtualization completely emulates the CPU, memory, and I/O devices in order to provide a guest OS with an environment iden- [Becchi et al 2012;Duato et al 2009;Duato et al 2010a;Duato et al 2010b;Giunta et al 2010;Gupta et al 2009;Hansen 2007;Humphreys et al 2002;Jang et al 2013;Kato et al 2012;Kuzkin and Tormasov 2011;Laccetti et al 2013;Lagar-Cavilla et al 2007;Lama et al 2013;Lee et al 2016;Li et al 2012;Liang and Chang 2011;Merritt et al 2011;Montella et al 2014;Montella et al 2016a;Montella et al 2016b;Niederauer et al 2003;Oikawa et al 2012;Peña et al 2014;Pérez et al 2016;Prades et al 2016;Ravi et al 2011;Reaño et al 2012;Reaño et al 2013;Reaño et al 2015a;Reaño et al 2015b;Rossbach et al 2011;Sengupta et al 2013;Sengupta et al 2014;Shi et al 2009;Shi et al 2011;Shi et al 2012;Tien and You 2014;Vinaya et al 2012;Xiao et al 2012;You et al 2015;...…”

Section: System Virtualizationmentioning

confidence: 99%

“…However, these frameworks only focus on utilizing GPUs and need to adopt sophisticated scheduling algorithms that can utilize both processors by partitioning and load-balancing workloads differently for fused CPU-GPU architectures. Montella et al [2016b] explored NVIDIA's unified memory to simplify memory management in GPU virtualization. However, the sole use of unified memory incurs non-negligible performance degradation in data-intensive applications [Li et al 2015] because NVIDIA maintains its discrete GPU design and automatically migrates data between the host and the GPU.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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GPU Virtualization and Scheduling Methods

2017

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The integration of graphics processing units (GPUs) on high-end compute nodes has established a new accelerator-based heterogeneous computing model, which now permeates high performance computing. The same paradigm nevertheless has limited adoption in cloud computing or other large-scale distributed computing paradigms. Heterogeneous computing with GPUs can benefit the Cloud by reducing operational costs and improving resource and energy efficiency. However, such a paradigm shift would require effective methods for virtualizing GPUs, as well as other accelerators. In this survey paper, we present an extensive and in-depth survey of GPU virtualization techniques and their scheduling methods. We review a wide range of virtualization techniques implemented at the GPU library, driver, and hardware levels. Furthermore, we review GPU scheduling methods that address performance and fairness issues between multiple virtual machines sharing GPUs. We believe that our survey delivers a perspective on the challenges and opportunities for virtualization of heterogeneous computing environments.

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Section: System Virtualizationmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

GPU Virtualization and Scheduling Methods

2017

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“…As described in Montella et al with a remarkable level of details, GVirtuS leverages on the split‐driver model: a back‐end , a communicator , and one or more front‐end (s).…”

Section: The Rapid Offloading Service Architecturementioning

confidence: 99%

Accelerating Linux and Android applications on low‐power devices through remote GPGPU offloading

Montella

Kosta

Oro³

et al. 2017

Concurrency and Computation

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Summary Low‐power devices are usually highly constrained in terms of CPU computing power, memory, and GPGPU resources for real‐time applications to run. In this paper, we describe RAPID, a complete framework suite for computation offloading to help low‐powered devices overcome these limitations. RAPID supports CPU and GPGPU computation offloading on Linux and Android devices. Moreover, the framework implements lightweight secure data transmission of the offloading operations. We present the architecture of the framework, showing the integration of the CPU and GPGPU offloading modules. We show by extensive experiments that the overhead introduced by the security layer is negligible. We present the first benchmark results showing that Java/Android GPGPU code offloading is possible. Finally, we show the adoption of the GPGPU offloading into BioSurveillance, a commercial real‐time face recognition application. The results show that, thanks to RAPID, BioSurveillance is being successfully adapted to run on low‐power devices. The proposed framework is highly modular and exposes a rich application programming interface to developers, making it highly versatile while hiding the complexity of the underlying networking layer.

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“…We select GVirtuS [7] for our exploration of ideal fairness and high utilization of virtualized accelerators because it is the only GPU virtualization framework available that is both open source and supports the latest version of CUDA and OpenCL [18]. In GVirtuS, when the connection between the frontend and the backend is first established, the backend spawns a child process to differentiate the GPU context from those of other applications.…”

Section: Background and Related Workmentioning

confidence: 99%

FairGV: Fair and Fast GPU Virtualization

Hong

Spence

Nikolopoulos

2017

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Increasingly high performance computing (HPC) application developers are opting to use cloud resources due to higher availability. Virtualized GPUs would be an obvious and attractive option for HPC application developers using cloud hosting services. Unfortunately, existing GPU virtualization software is not ready to address fairness, utilization, and performance limitations associated with consolidating mixed HPC workloads. This paper presents FairGV, a radically redesigned GPU virtualization system that achieves system-wide weighted fair sharing and strong performance isolation in mixed workloads that use GPUs with variable degrees of intensity. To achieve its objectives, FairGV introduces a trap-less GPU processing architecture, a new fair queuing method integrated with work-conserving and GPU-centric coscheduling polices, and a collaborative scheduling method for non-preemptive GPUs. Our prototype implementation achieves near ideal fairness (≥ 0.97 Min-Max Ratio) with little performance degradation (≤ 1.02 aggregated overhead) in a range of mixed HPC workloads that leverage GPUs.

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On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework

Cited by 29 publications

References 36 publications

GPU Virtualization and Scheduling Methods

GPU Virtualization and Scheduling Methods

Accelerating Linux and Android applications on low‐power devices through remote GPGPU offloading

FairGV: Fair and Fast GPU Virtualization

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