Improving the user experience of the rCUDA remote GPU virtualization framework

Reaño, Carlos; Silla, Federico; Castelló, Adrián; Peña, Antonio J.; Mayo, Rafael; Quintana–Ort́ı, Enrique S.; Duato, J.

doi:10.1002/cpe.3409

Cited by 13 publications

(20 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another aspect which influences the execution time is the frequency used to poll the network for work completions, usually referred to as the network polling interval. This period seems to be lower in rCUDA than the one used by CUDA to poll the PCIe link, as demonstrated in [14]. In this manner, for short tests where the sum of these small waits becomes an important part of the total execution time, rCUDA runs faster than CUDA.…”

Section: Impact Of the Improvements On Rodinia Benchmarkmentioning

confidence: 92%

“…For example, a single call to cudaDeviceSynchronize takes approximately 40 microseconds in rCUDA, and about 530 microseconds in CUDA. The cause of this variance resides in the internal algorithm used in rCUDA to determine the end of the call, which benefits rCUDA in these short tests [14]. Still, the time saved in these calls does not completely explain the better performance of rCUDA in these tests.…”

Section: Impact Of the Improvements On Rodinia Benchmarkmentioning

confidence: 99%

“…This amount of GPUs is usually much larger than the number of GPUs that can fit in a single box. Consequently, applications can be further accelerated [14]. -HPC clusters can be easily updated.…”

Section: Why Using Remote Gpu-accelerators In Hpc Clustersmentioning

confidence: 99%

See 2 more Smart Citations

Tuning remote GPU virtualization for InfiniBand networks

Reaño

Silla

2016

J Supercomput

Self Cite

View full text Add to dashboard Cite

In the past few years, a tendency towards using InfiniBand networks to interconnect high performance computing clusters can be observed. Thus, most of the supercomputers appearing in the TOP500 list either use Ethernet or InfiniBand interconnects. Regarding the latter, the complexity of the InfiniBand programming API (i.e., InfiniBand Verbs) makes it difficult for applications to get the maximum performance of these networks. In this paper we expose how we have tuned a remote GPU virtualization framework whose communications module is implemented using InfiniBand Verbs. The net result is a noticeable increase in the performance of this framework, significantly reducing the gap between remote and local GPUs.

show abstract

Section: Impact Of the Improvements On Rodinia Benchmarkmentioning

confidence: 92%

Section: Impact Of the Improvements On Rodinia Benchmarkmentioning

confidence: 99%

See 1 more Smart Citation

Tuning remote GPU virtualization for InfiniBand networks

Reaño

Silla

2016

J Supercomput

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

GPU Virtualization and Scheduling Methods

2017

View full text Add to dashboard Cite

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.

show abstract

“…If it is successful, the synchronization call is thus avoided. A detailed analysis can be found in [22].…”

Section: Dealing With Synchronizationmentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Improving the user experience of the rCUDA remote GPU virtualization framework

Cited by 13 publications

References 16 publications

Tuning remote GPU virtualization for InfiniBand networks

Tuning remote GPU virtualization for InfiniBand networks

GPU Virtualization and Scheduling Methods

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

Contact Info

Product

Resources

About