95 research outputs found
Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application
Graphics Processing Units (GPUs) are becoming popular accelerators in modern
High-Performance Computing (HPC) clusters. Installing GPUs on each node of the
cluster is not efficient resulting in high costs and power consumption as well
as underutilisation of the accelerator. The research reported in this paper is
motivated towards the use of few physical GPUs by providing cluster nodes
access to remote GPUs on-demand for a financial risk application. We
hypothesise that sharing GPUs between several nodes, referred to as
multi-tenancy, reduces the execution time and energy consumed by an
application. Two data transfer modes between the CPU and the GPUs, namely
concurrent and sequential, are explored. The key result from the experiments is
that multi-tenancy with few physical GPUs using sequential data transfers
lowers the execution time and the energy consumed, thereby improving the
overall performance of the application.Comment: Accepted to the Journal of Parallel and Distributed Computing (JPDC),
10 June 201
Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application
'How can GPU acceleration be obtained as a service in a cluster?' This
question has become increasingly significant due to the inefficiency of
installing GPUs on all nodes of a cluster. The research reported in this paper
is motivated to address the above question by employing rCUDA (remote CUDA), a
framework that facilitates Acceleration-as-a-Service (AaaS), such that the
nodes of a cluster can request the acceleration of a set of remote GPUs on
demand. The rCUDA framework exploits virtualisation and ensures that multiple
nodes can share the same GPU. In this paper we test the feasibility of the
rCUDA framework on a real-world application employed in the financial risk
industry that can benefit from AaaS in the production setting. The results
confirm the feasibility of rCUDA and highlight that rCUDA achieves similar
performance compared to CUDA, provides consistent results, and more
importantly, allows for a single application to benefit from all the GPUs
available in the cluster without loosing efficiency.Comment: 11th IEEE International Conference on eScience (IEEE eScience) -
Munich, Germany, 201
GPU-Job Migration: The rCUDA Case
© 2019 IEEE. Personal use of this material is permitted. PermissĂon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisĂng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Virtualization techniques have been shown to report benefits to data centers and other computing facilities. In this regard, not only virtual machines allow to reduce the size of the computing infrastructure while increasing overall resource utilization, but also virtualizing individual components of computers may provide significant benefits. This is the case, for instance, for the remote GPU virtualization technique, implemented in several frameworks during the recent years. The large degree of flexibility provided by the remote GPU virtualization technique can be further increased by applying the migration mechanism to it, so that the GPU part of applications can be live-migrated to another GPU elsewhere in the cluster during execution time in a transparent way. In this paper we present the implementation of the migration mechanism within the rCUDA remote GPU virtualization middleware. Furthermore, we present a thorough performance analysis of the implementation of the migration mechanism within rCUDA. To that end, we leverage both synthetic and real production applications as well as three different generations of NVIDIA GPUs. Additionally, two different versions of the InfiniBand interconnect are used in this study. Several use cases are provided in order to show the extraordinary benefits that the GPU-job migration mechanism can report to data centers.This work was funded by the Generalitat Valenciana under Grant PROMETEO/2017/77. Authors are grateful for the generous support provided by Mellanox Technologies Inc.Prades, J.; Silla JimĂ©nez, F. (2019). GPU-Job Migration: The rCUDA Case. IEEE Transactions on Parallel and Distributed Systems. 30(12):2718-2729. https://doi.org/10.1109/TPDS.2019.292443327182729301
A performance comparison of CUDA remote GPU virtualization frameworks
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Using GPUs reduces execution time of many applications
but increases acquisition cost and power consumption.
Furthermore, GPUs usually attain a relatively low utilization.
In this context, remote GPU virtualization solutions were
recently created to overcome the drawbacks of using GPUs.
Currently, many different remote GPU virtualization frameworks
exist, all of them presenting very different characteristics.
These differences among them may lead to differences in
performance. In this work we present a performance comparison
among the only three CUDA remote GPU virtualization
frameworks publicly available at no cost. Results show that
performance greatly depends on the exact framework used,
being the rCUDA virtualization solution the one that stands
out among them. Furthermore, rCUDA doubles performance
over CUDA for pageable memory copies.This work was funded by the Generalitat Valenciana under
Grant PROMETEOII/2013/009 of the PROMETEO program
phase II. Authors are also grateful for the generous support
provided by Mellanox TechnologiesReaño González, C.; Silla Jiménez, F. (2015). A performance comparison of CUDA remote GPU virtualization frameworks. IEEE. https://doi.org/10.1109/CLUSTER.2015.76
Tuning remote GPU virtualization for InfiniBand networks
The final publication is available at Springer via http://dx.doi.org/ 10.1007/s11227-016-1754-3In 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.This work was funded by the Spanish MINECO and FEDER funds under Grant TIN2012-38341-C04-01. Authors are also grateful for the generous support provided by Mellanox Technologies.Reaño González, C.; Silla Jiménez, F. (2016). Tuning remote GPU virtualization for InfiniBand networks. Journal of Supercomputing. 72(12):4520-4545. https://doi.org/10.1007/s11227-016-1754-3S452045457212InfiniBand Trade Association (IBTA) (2015) [Online]. http://www.infinibandta.orgDAmbrosia J (2014) Ethernet in the TOP500 [Online]. http://www.scientificcomputing.com/blogs/2014/07/ethernet-top500TOP500 Supercomputer Sites (2014) [Online]. http://www.top500.org/InfiniBand Trade Association (IBTA) (2007) The InfiniBand Trade Association SpecificationKerr G (2011) Dissecting a small infiniband application using the verbs API. CoRR abs/1105.1827 [Online]. arxiv:1105.1827Woodruff B, Hefty S, Dreier R, Rosenstock H (2005) Introduction to the infiniband core software. In: Linux symposium, vol 2Bedeir T (2010) Building an RDMA-capable application with ib verbs, Technical report, HPC Advisory Council, Tech. Rep., 2010. http://www.hpcadvisorycouncil.com/pdf/building-an-rdma-capable-application-with-ib-verbs.pdfLiu Q, Russell RD (2014) A performance study of infiniband fourteen data rate (fdr). In: Proceedings of the High performance computing symposium, ser. HPC ’14. San Diego, CA, USA: Society for Computer Simulation International, 2014, pp 16:1–16:10 [Online]. http://dl.acm.org/citation.cfm?id=2663510.2663526Hjelm N (2014) Optimizing one-sided operations in open mpi. In: Proceedings of the 21st European MPI Users’ Group Meeting, ser. EuroMPI/ASIA ’14. New York, NY, USA: ACM, 2014, pp 123:123–123:124 [Online]. http://doi.acm.org/10.1145/2642769.2642792Subramoni H, Hamidouche K, Venkatesh A, Chakraborty S, Panda D (2014) Designing mpi library with dynamic connected transport (dct) of infiniband: Early experiences. In: Kunkel J , Ludwig T, Meuer H (eds) Supercomputing, ser. lecture notes in computer science. Springer International Publishing, 2014, vol 8488, pp 278–295 [Online]. doi: 10.1007/978-3-319-07518-1_18Unified Communication X (UCX), 2015 [Online]. http://www.openucx.orgNVIDIA (2014) CUDA C Programming Guide 6.5Peña AJ, Reaño C, Silla F, Mayo R, Quintana-Ortà ES, Duato J (2014) A complete and efficient cuda-sharing solution for hpc clusters. Parallel Comput 40(10):574– 588 [Online]. http://www.sciencedirect.com/science/article/pii/S0167819114001227Reaño C, Silla F, Gimeno AC, Peña AJ, Mayo R, Quintana-Ortà ES, Duato J (2015) Improving the user experience of the rcuda remote GPU virtualization framework. Concurr Comput Pract Exp 27(14)3746–3770 [Online]. doi: 10.1002/cpe.3409Prades J, Reaño C, Silla F (2016) Flexible access to CUDA accelerators from Xen virtual machines in InfiniBand clusters using rCUDA. In: 21st ACM SIGPLAN symposium on principles and practice of parallel programming, PPoPP 2016Iserte S, Gimeno AC, Mayo R, Quintana-Ortà ES, Silla F, Duato J, Reaño C, Prades J (2014) SLURM support for remote GPU virtualization: implementation and performance study. In: 26th IEEE international symposium on computer architecture and high performance computing, SBAC-PAD, 2014, pp 318–325 [Online]. doi: 10.1109/SBAC-PAD.2014.49NVIDIA (2014) NVIDIA CUDA Samples 6.5Che S, Boyer M, Meng J, Tarjan D, Sheaffer J, Lee S-H, Skadron K (2009) Rodinia: a benchmark suite for heterogeneous computing. In: Workload Characterization, 2009. IISWC 2009. IEEE international symposium on, 2009, pp 44–54University of Tennessee, MAGMA: matrix algebra on GPU and multicore architectures [Online]. http://icl.cs.utk.edu/magmaBosma W, Cannon J, Playoust C (1997) The Magma algebra system. I. The user language. Computational algebra and number theory (London, 1993). J Symbol Comput 24(3–4) 235–265 [Online]. doi: 10.1006/jsco.1996.0125GROMACS web page (2014 ) [Online]. http://www.gromacs.org/Pronk S, Pll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Hess B, Lindahl E (2013) Gromacs 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7)845–854 [Online]. http://bioinformatics.oxfordjournals.org/content/29/7/845.abstractBrown WM, Kohlmeyer A, Plimpton SJ, Tharrington AN (2012) Implementing molecular dynamics on hybrid high performance computers: particle–particle particle–mesh. Comp Phys Commun 183(3):449–459Athanasopoulos A, Dimou A, Mezaris V, Kompatsiaris I (2011) GPU acceleration for support vector machines. In: 12th international workshop on image analysis for multimedia interactive services (WIAMIS
InfiniBand verbs optimizations for remote GPU virtualization
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The use of InfiniBand networks to interconnect
high performance computing clusters has considerably increased
during the last years. So much so that the majority of
the supercomputers included in the TOP500 list either use
Ethernet or InfiniBand interconnects. Regarding the latter,
due to the complexity of the InfiniBand programming API
(i.e., InfiniBand Verbs) and the lack of documentation, there
are not enough recent available studies explaining how to
optimize applications to get the maximum performance from
this fabric. In this paper we expose two different optimizations
to be used when developing applications using InfiniBand
Verbs, each providing an average bandwidth improvement of
3.68% and 217.14%, respectively. In addition, we show that
when combining both optimizations, the average bandwidth
gain is 43.29%. This bandwidth increment is key for remote
GPU virtualization frameworks. Actually, this noticeable gain
translates into a reduction of up to 35% in execution time of
applications using remote GPU virtualization frameworks.This work was funded by the Generalitat Valenciana under
Grant PROMETEOII/2013/009 of the PROMETEO program
phase II. Authors are also grateful for the generous support
provided by Mellanox TechnologiesReaño González, C.; Silla Jiménez, F. (2015). InfiniBand verbs optimizations for remote GPU virtualization. IEEE. https://doi.org/10.1109/CLUSTER.2015.139
Reducing the Costs of Teaching CUDA in Laboratories while Maintaining the Learning Experience Quality
Graphics Processing Units (GPUs) have become widely used to accelerate scientific applications;
therefore, it is important that Computer Science and Computer Engineering curricula include the
fundamentals of parallel computing with GPUs. Regarding the practical part of the training, one
important concern is how to introduce GPUs into a laboratory: installing GPUs in all the computers of
the lab may not be affordable, while sharing a remote GPU server among several students may result
in a poor learning experience because of its associated overhead.
In this paper we propose a solution to address this problem: the use of the rCUDA (remote CUDA)
middleware, which enables programs being executed in a computer to make concurrent use of GPUs
located in remote servers. Hence, students would be able to concurrently and transparently share a
single remote GPU from their local machines in the laboratory without having to log into the remote
server. In order to demonstrate that our proposal is feasible, we present results of a real scenario. The
results show that the cost of the laboratory is noticeably reduced while the learning experience quality
is maintained.Reaño González, C.; Silla Jiménez, F. (2015). Reducing the Costs of Teaching CUDA in Laboratories while Maintaining the Learning Experience Quality. En INTED2015 Proceedings. IATED. 3651-3660. http://hdl.handle.net/10251/70229S3651366
Exploring the use of data compression for accelerating machine learning in the edge with remote virtual graphics processing units
[EN] Internet of Things (IoT) devices are usually low performance nodes connected by low bandwidth networks. To improve performance in such scenarios, some computations could be done at the edge of the network. However, edge devices may not have enough computing power to accelerate applications such as the popular machine learning ones. Using remote virtual graphics processing units (GPUs) can address this concern by accelerating applications leveraging a GPU installed in a remote device. However, this requires exchanging data with the remote GPU across the slow network. To address the problem with the slow network, the data to be exchanged with the remote GPU could be compressed. In this article, we explore the suitability of using data compression in the context of remote GPU virtualization frameworks in edge scenarios executing machine learning applications. We use popular machine learning applications to carry out such exploration. After characterizing the GPU data transfers of these applications, we analyze the usage of existing compression libraries for compressing those data transfers to/from the remote GPU. Our exploration shows that transferring compressed data becomes more beneficial as networks get slower, reducing transfer time by up to 10 times. Our analysis also reveals that efficient integration of compression into remote GPU virtualization frameworks is strongly required.European Union's Horizon 2020 Research and Innovation Programme, Grant/Award Numbers: 101016577, 101017861.Peñaranda-Cebrián, C.; Reaño, C.; Silla, F. (2022). Exploring the use of data compression for accelerating machine learning in the edge with remote virtual graphics processing units. Concurrency and Computation: Practice and Experience. 35(20):1-19. https://doi.org/10.1002/cpe.7328119352
On the Effect of using rCUDA to Provide CUDA Acceleration to Xen Virtual Machines
[EN] 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, current virtualization solutions, such as Xen, do not easily provide graphics processing units (GPUs) 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). Therefore, the execution of GPU-accelerated applications within VMs is hindered by this lack of flexibility. In this regard, remote GPU virtualization solutions may address this concern. In this paper we analyze the use of the remote GPU virtualization mechanism to accelerate scientific applications running inside Xen VMs. We conduct our study with six different applications, namely CUDA-MEME, CUDASW++, GPU-BLAST, LAMMPS, a triangle count application, referred to as TRICO, and a synthetic benchmark used to emulate different application behaviors. Our experiments show that the use of remote GPU virtualization is a feasible approach to address the current concerns of sharing GPUs among several VMs, featuring a very low overhead if an InfiniBand fabric is already present in the cluster.This work was funded by the Generalitat Valenciana under Grant PROMETEO/2017/077. Authors are also grateful for the generous support provided by Mellanox Technologies Inc.Prades, J.; Reaño González, C.; Silla JimĂ©nez, F. (2019). On the Effect of using rCUDA to Provide CUDA Acceleration to Xen Virtual Machines. 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Accelerator Virtualization in Fog Computing: Moving from the Cloud to the Edge
[EN] Hardware accelerators are available on the cloud for enhanced analytics. Next-generation clouds aim to bring enhanced analytics using accelerators closer to user devices at the edge of the network for improving quality of service (QoS) by minimizing end-to-end latencies and response times. The collective computing model that utilizes resources at the cloud-edge continuum in a multi-tier hierarchy comprising the cloud, edge, and user devices is referred to as fog computing. This article identifies challenges and opportunities in making accelerators accessible at the edge. A holistic view of the fog architecture is key to pursuing meaningful research in this area.Varghese, B.; Reaño González, C.; Silla Jiménez, F. (2018). Accelerator Virtualization in Fog Computing: Moving from the Cloud to the Edge. IEEE Cloud Computing. 5(6):28-37. https://doi.org/10.1109/MCC.2018.064181118S28375
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