VOCL: An optimized environment for transparent virtualization of graphics processing units

Xiao, Shiyu; Balaji, Pavan; Zhu, Quing; Thakur, Rajeev; Coghlan, Susan; Lin, Heshan; Wen, Gongjian; Hong, Ju; Feng, Wu-chun

doi:10.1109/inpar.2012.6339609

Cited by 59 publications

(40 citation statements)

References 18 publications

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“…On the OpenCL side, the authors of the VOCL framework tested the SHOC benchmark suite and several application kernels, including the BLAS GEMM, matrix transpose, n-body, and Smith-Waterman [12]. Moreover, dOpenCL was released with the evaluation of a tomography [18] production application.…”

Section: Related Workmentioning

confidence: 99%

“…With this approach, GPU-equipped nodes act as GPGPU (general-purpose GPU) servers for the rest of the compute nodes of the cluster, accomodating a CUDAsharing solution that potentially achieves a higher overall GPU load in the system. Compared with other CUDA and OpenCL virtualization frameworks (e.g., DS-CUDA [10], vCUDA [11], VOCL [12], or SnuCL [13]), rCUDA is a mature, production-ready framework that offers support for the latest CUDA revisions and provides wide coverage of current GPGPU APIs.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the interoperability of remote GPGPU virtualization using rCUDA and directive-based programming models

et al. 2016

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Directive-based programming models, such as OpenMP, OpenACC, and OmpSs, enable users to accelerate applications by using coprocessors with little effort. These devices offer significant computing power, but their use can introduce two problems: an increase in the total cost of ownership and their underutilization because not all codes match their architecture. Remote accelerator virtualization frameworks address those problems. In particular, rCUDA provides transparent access to any graphic processor unit installed in a cluster, reducing the number of accelerators and increasing their utilization ratio. Joining these two technologies, directive-based programming models and rCUDA, is thus highly appealing. In this work, we study the integration of OmpSs and OpenACC with rCUDA, describing and analyzing several applications over three different hardware configurations that include two InfiniBand interconnections and three NVIDIA accelerators. Our evaluation reveals favorable performance results, showing low overhead and similar scaling factors when using remote accelerators instead of local devices.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the interoperability of remote GPGPU virtualization using rCUDA and directive-based programming models

et al. 2016

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“…In our prior work, we presented the Virtual OpenCL framework [5]. VOCL is a reimplementation of the OpenCL programming model that enables the programmer to utilize both local and remote accelerators through device virtualization.…”

Section: B the Virtual Opencl Frameworkmentioning

confidence: 99%

“…Since OpenCL handles sharing, the same handle values are translated to different VOCL handles; VOCL is able to differentiate them and send function calls to the correct device. More details about the VOCL framework and the VOCL abstraction are described in our prior work [5].…”

Section: B the Virtual Opencl Frameworkmentioning

confidence: 99%

“…In our prior work, we created Virtual OpenCL (VOCL) [5], an implementation of the OpenCL programming model that provides the ability to utilize and share nonlocal computation accelerators through device virtualization. VOCL establishes device proxies that manage the mapping of virtual to physical OpenCL contexts and forward OpenCL commands from the application to the physical device.…”

Section: Introductionmentioning

confidence: 99%

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Transparent Accelerator Migration in a Virtualized GPU Environment

Xiao

Balaji

Dinan

et al. 2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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Abstract-This paper presents a framework to support transparent, live migration of virtual GPU accelerators in a virtualized execution environment. Migration is a critical capability in such environments because it provides support for fault tolerance, ondemand system maintenance, resource management, and load balancing in the mapping of virtual to physical GPUs. Techniques to increase responsiveness and reduce migration overhead are explored. The system is evaluated by using four application kernels and is demonstrated to provide low migration overheads. Through transparent load balancing, our system provides a speedup of 1.7 to 1.9 for three of the four application kernels.

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Improving the user experience of the rCUDA remote GPU virtualization framework

Reaño

Silla

Castelló

et al. 2014

Concurrency and Computation

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VOCL: An optimized environment for transparent virtualization of graphics processing units

Cited by 59 publications

References 18 publications

Exploring the interoperability of remote GPGPU virtualization using rCUDA and directive-based programming models

Exploring the interoperability of remote GPGPU virtualization using rCUDA and directive-based programming models

Transparent Accelerator Migration in a Virtualized GPU Environment

Improving the user experience of the rCUDA remote GPU virtualization framework

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