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

On the Benefits of the Remote GPU Virtualization Mechanism: the rCUDA Case

[EN] Graphics processing units (GPUs) are being adopted in many computing facilities given their extraordinary computing power, which makes it possible to accelerate many general purpose applications from different domains. However, GPUs also present several side effects, such as increased acquisition costs as well as larger space requirements. They also require more powerful energy supplies. Furthermore, GPUs still consume some amount of energy while idle, and their utilization is usually low for most workloads. In a similar way to virtual machines, the use of virtual GPUs may address the aforementioned concerns. In this regard, the remote GPU virtualization mechanism allows an application being executed in a node of the cluster to transparently use the GPUs installed at other nodes. Moreover, this technique allows to share the GPUs present in the computing facility among the applications being executed in the cluster. In this way, several applications being executed in different (or the same) cluster nodes can share 1 or more GPUs located in other nodes of the cluster. Sharing GPUs should increase overall GPU utilization, thus reducing the negative impact of the side effects mentioned before. Reducing the total amount of GPUs installed in the cluster may also be possible. In this paper, we explore some of the benefits that remote GPU virtualization brings to clusters. For instance, this mechanism allows an application to use all the GPUs present in the computing facility. Another benefit of this technique is that cluster throughput, measured as jobs completed per time unit, is noticeably increased when this technique is used. In this regard, cluster throughput can be doubled for some workloads. Furthermore, in addition to increase overall GPU utilization, total energy consumption can be reduced up to 40%. This may be key in the context of exascale computing facilities, which present an important energy constraint. Other benefits are related to the cloud computing domain, where a GPU can be easily shared among several virtual machines. Finally, GPU migration (and therefore server consolidation) is one more benefit of this novel technique.Generalitat Valenciana, Grant/Award Number: PROMETEOII/2013/009; MINECO and FEDER, Grant/Award Number: TIN2014-53495-RSilla Jiménez, F.; Iserte Agut, S.; Reaño González, C.; Prades, J. (2017). On the Benefits of the Remote GPU Virtualization Mechanism: the rCUDA Case. 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Concurrency and Computation: Practice and Experience, 26(16), 2652-2666. doi:10.1002/cpe.3152Yuancheng Luo D Canny edge detection on NVIDIA CUDA IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08 Anchorage, AK, USA IEEE 2008 1 8Surkov, V. (2010). Parallel option pricing with Fourier space time-stepping method on graphics processing units. Parallel Computing, 36(7), 372-380. doi:10.1016/j.parco.2010.02.006Agarwal, P. K., Hampton, S., Poznanovic, J., Ramanthan, A., Alam, S. R., & Crozier, P. S. (2012). Performance modeling of microsecond scale biological molecular dynamics simulations on heterogeneous architectures. Concurrency and Computation: Practice and Experience, 25(10), 1356-1375. doi:10.1002/cpe.2943Yoo, A. B., Jette, M. A., & Grondona, M. (2003). SLURM: Simple Linux Utility for Resource Management. 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Parallel Computing, 40(10), 574-588. doi:10.1016/j.parco.2014.09.011CUDA API Reference Manual 7.5 https://developer.nvidia.com/cuda-toolkit 2016Merritt AM Gupta V Verma A Gavrilovska A Schwan K Shadowfax: Scaling in heterogeneous cluster systems via GPGPU assemblies Proceedings of the 5th International Workshop on Virtualization Technologies in Distributed Computing VTDC '11 ACM New York, NY, USA 2011 3 10Shadowfax II - scalable implementation of GPGPU assemblies http://keeneland.gatech.edu/software/keeneland/kidronNVIDIA The NVIDIA GPU Computing SDK Version 5.5 2013iperf3: A TCP, UDP, and SCTP network bandwidth measurement tool https://github.com/esnet/iperf 2016Reaño C Silla F Shainer G Schultz S Local and remote GPUs perform similar with EDR 100G InfiniBand Proceedings of the Industrial Track of the 16th International Middleware Conference Middleware Industry '15 Vancouver, Canada 2015Reaño, C., Silla, F., Castelló, A., Peña, A. J., Mayo, R., Quintana-Ortí, E. S., & Duato, J. (2014). Improving the user experience of the rCUDA remote GPU virtualization framework. Concurrency and Computation: Practice and Experience, 27(14), 3746-3770. doi:10.1002/cpe.3409Iserte S Castelló A Mayo R Slurm support for remote GPU virtualization: Implementation and performance study 2014 IEEE 26th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) 2014 318 325Vouzis, P. D., & Sahinidis, N. V. (2010). GPU-BLAST: using graphics processors to accelerate protein sequence alignment. Bioinformatics, 27(2), 182-188. doi:10.1093/bioinformatics/btq644Brown, W. M., Kohlmeyer, A., Plimpton, S. J., & Tharrington, A. N. (2012). Implementing molecular dynamics on hybrid high performance computers – Particle–particle particle-mesh. Computer Physics Communications, 183(3), 449-459. doi:10.1016/j.cpc.2011.10.012Liu, Y., Schmidt, B., Liu, W., & Maskell, D. L. (2010). 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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

SLURM Support for Remote GPU Virtualization: Implementation and Performance Study

© 2014 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.SLURM is a resource manager that can be leveraged to share a collection of heterogeneous resources among the jobs in execution in a cluster. However, SLURM is not designed to handle resources such as graphics processing units (GPUs). Concretely, although SLURM can use a generic resource plugin (GRes) to manage GPUs, with this solution the hardware accelerators can only be accessed by the job that is in execution on the node to which the GPU is attached. This is a serious constraint for remote GPU virtualization technologies, which aim at providing a user-transparent access to all GPUs in cluster, independently of the specific location of the node where the application is running with respect to the GPU node. In this work we introduce a new type of device in SLURM, "rgpu", in order to gain access from any application node to any GPU node in the cluster using rCUDA as the remote GPU virtualization solution. With this new scheduling mechanism, a user can access any number of GPUs, as SLURM schedules the tasks taking into account all the graphics accelerators available in the complete cluster. We present experimental results that show the benefits of this new approach in terms of increased flexibility for the job scheduler.The researchers at UPV were supported by the the Generalitat Valenciana under Grant PROMETEOII/2013/009 of the PROMETEO program phase II. Researchers at UJI were supported by MINECO, by FEDER funds under Grant TIN2011-23283, and by the Fundacion Caixa-Castelló Bancaixa (Grant P11B2013-21).Iserte Agut, S.; Castello Gimeno, A.; Mayo Gual, R.; Quintana Ortí, ES.; Silla Jiménez, F.; Duato Marín, JF.; Reaño González, C.... (2014). SLURM Support for Remote GPU Virtualization: Implementation and Performance Study. En Computer Architecture and High Performance Computing (SBAC-PAD), 2014 IEEE 26th International Symposium on. IEEE. 318-325. https://doi.org/10.1109/SBAC-PAD.2014.49S31832

Boosting the performance of remote GPU virtualization using InfiniBand Connect-IB and PCIe 3.0

© 2014 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.[EN] A clear trend has emerged involving the acceleration of scientific applications by using GPUs. However, the capabilities of these devices are still generally underutilized. Remote GPU virtualization techniques can help increase GPU utilization rates, while reducing acquisition and maintenance costs. The overhead of using a remote GPU instead of a local one is introduced mainly by the difference in performance between the internode network and the intranode PCIe link. In this paper we show how using the new InfiniBand Connect-IB network adapters (attaining similar throughput to that of the most recently emerged GPUs) boosts the performance of remote GPU virtualization, reducing the overhead to a mere 0.19% in the application tested.This work was funded by the Generalitat Valenciana under Grant PROMETEOII/2013/009 of the PROMETEO program phase II. This material is based upon work supported by the U. S. Department of Energy, Office of Science, Advanced Scientific Computing Research (SC-21), under Contract No. DE-AC02-06CH11357. Authors from the Universitat Politècnica de València and Universitat Jaume I are grateful for the generous support provided by Mellanox Technologies.Reaño González, C.; Silla Jiménez, F.; Peña Monferrer, AJ.; Shainer, G.; Schultz, S.; Castelló Gimeno, A.; Quintana Orti, ES.... (2014). Boosting the performance of remote GPU virtualization using InfiniBand Connect-IB and PCIe 3.0. En 2014 IEEE International Conference on Cluster Computing (CLUSTER). IEEE. 266-267. doi:10.1109/CLUSTER.2014.6968737S26626

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. 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Improving Performance and Energy Efficiency of Heterogeneous Systems with rCUDA

Tesis por compendio[ES] En la última década la utilización de la GPGPU (General Purpose computing in Graphics Processing Units; Computación de Propósito General en Unidades de Procesamiento Gráfico) se ha vuelto tremendamente popular en los centros de datos de todo el mundo. Las GPUs (Graphics Processing Units; Unidades de Procesamiento Gráfico) se han establecido como elementos aceleradores de cómputo que son usados junto a las CPUs formando sistemas heterogéneos. La naturaleza masivamente paralela de las GPUs, destinadas tradicionalmente al cómputo de gráficos, permite realizar operaciones numéricas con matrices de datos a gran velocidad debido al gran número de núcleos que integran y al gran ancho de banda de acceso a memoria que poseen. En consecuencia, aplicaciones de todo tipo de campos, tales como química, física, ingeniería, inteligencia artificial, ciencia de materiales, etc. que presentan este tipo de patrones de cómputo se ven beneficiadas, reduciendo drásticamente su tiempo de ejecución. En general, el uso de la aceleración del cómputo en GPUs ha significado un paso adelante y una revolución. Sin embargo, no está exento de problemas, tales como problemas de eficiencia energética, baja utilización de las GPUs, altos costes de adquisición y mantenimiento, etc. En esta tesis pretendemos analizar las principales carencias que presentan estos sistemas heterogéneos y proponer soluciones basadas en el uso de la virtualización remota de GPUs. Para ello hemos utilizado la herramienta rCUDA, desarrollada en la Universitat Politècnica de València, ya que multitud de publicaciones la avalan como el framework de virtualización remota de GPUs más avanzado de la actualidad. Los resutados obtenidos en esta tesis muestran que el uso de rCUDA en entornos de Cloud Computing incrementa el grado de libertad del sistema, ya que permite crear instancias virtuales de las GPUs físicas totalmente a medida de las necesidades de cada una de las máquinas virtuales. En entornos HPC (High Performance Computing; Computación de Altas Prestaciones), rCUDA también proporciona un mayor grado de flexibilidad de uso de las GPUs de todo el clúster de cómputo, ya que permite desacoplar totalmente la parte CPU de la parte GPU de las aplicaciones. Además, las GPUs pueden estar en cualquier nodo del clúster, independientemente del nodo en el que se está ejecutando la parte CPU de la aplicación. En general, tanto para Cloud Computing como en el caso de HPC, este mayor grado de flexibilidad se traduce en un aumento hasta 2x de la productividad de todo el sistema al mismo tiempo que se reduce el consumo energético en un 15%. Finalmente, también hemos desarrollado un mecanismo de migración de trabajos de la parte GPU de las aplicaciones que ha sido integrado dentro del framework rCUDA. Este mecanismo de migración ha sido evaluado y los resultados muestran claramente que, a cambio de una pequeña sobrecarga, alrededor de 400 milisegundos, en el tiempo de ejecución de las aplicaciones, es una potente herramienta con la que, de nuevo, aumentar la productividad y reducir el gasto energético del sistema. En resumen, en esta tesis se analizan los principales problemas derivados del uso de las GPUs como aceleradores de cómputo, tanto en entornos HPC como de Cloud Computing, y se demuestra cómo a través del uso del framework rCUDA, estos problemas pueden solucionarse. Además se desarrolla un potente mecanismo de migración de trabajos GPU, que integrado dentro del framework rCUDA, se convierte en una herramienta clave para los futuros planificadores de trabajos en clusters heterogéneos.[CA] En l'última dècada la utilització de la GPGPU(General Purpose computing in Graphics Processing Units; Computació de Propòsit General en Unitats de Processament Gràfic) s'ha tornat extremadament popular en els centres de dades de tot el món. Les GPUs (Graphics Processing Units; Unitats de Processament Gràfic) s'han establert com a elements acceleradors de còmput que s'utilitzen al costat de les CPUs formant sistemes heterogenis. La naturalesa massivament paral·lela de les GPUs, destinades tradicionalment al còmput de gràfics, permet realitzar operacions numèriques amb matrius de dades a gran velocitat degut al gran nombre de nuclis que integren i al gran ample de banda d'accés a memòria que posseeixen. En conseqüència, les aplicacions de tot tipus de camps, com ara química, física, enginyeria, intel·ligència artificial, ciència de materials, etc. que presenten aquest tipus de patrons de còmput es veuen beneficiades reduint dràsticament el seu temps d'execució. En general, l'ús de l'acceleració del còmput en GPUs ha significat un pas endavant i una revolució, però no està exempt de problemes, com ara poden ser problemes d'eficiència energètica, baixa utilització de les GPUs, alts costos d'adquisició i manteniment, etc. En aquesta tesi pretenem analitzar les principals mancances que presenten aquests sistemes heterogenis i proposar solucions basades en l'ús de la virtualització remota de GPUs. Per a això hem utilitzat l'eina rCUDA, desenvolupada a la Universitat Politècnica de València, ja que multitud de publicacions l'avalen com el framework de virtualització remota de GPUs més avançat de l'actualitat. Els resultats obtinguts en aquesta tesi mostren que l'ús de rCUDA en entorns de Cloud Computing incrementa el grau de llibertat del sistema, ja que permet crear instàncies virtuals de les GPUs físiques totalment a mida de les necessitats de cadascuna de les màquines virtuals. En entorns HPC (High Performance Computing; Computació d'Altes Prestacions), rCUDA també proporciona un major grau de flexibilitat en l'ús de les GPUs de tot el clúster de còmput, ja que permet desacoblar totalment la part CPU de la part GPU de les aplicacions. A més, les GPUs poden estar en qualsevol node del clúster, sense importar el node en el qual s'està executant la part CPU de l'aplicació. En general, tant per a Cloud Computing com en el cas del HPC, aquest major grau de flexibilitat es tradueix en un augment fins 2x de la productivitat de tot el sistema al mateix temps que es redueix el consum energètic en aproximadament un 15%. Finalment, també hem desenvolupat un mecanisme de migració de treballs de la part GPU de les aplicacions que ha estat integrat dins del framework rCUDA. Aquest mecanisme de migració ha estat avaluat i els resultats mostren clarament que, a canvi d'una petita sobrecàrrega, al voltant de 400 mil·lisegons, en el temps d'execució de les aplicacions, és una potent eina amb la qual, de nou, augmentar la productivitat i reduir la despesa energètica de sistema. En resum, en aquesta tesi s'analitzen els principals problemes derivats de l'ús de les GPUs com acceleradors de còmput, tant en entorns HPC com de Cloud Computing, i es demostra com a través de l'ús del framework rCUDA, aquests problemes poden solucionar-se. A més es desenvolupa un potent mecanisme de migració de treballs GPU, que integrat dins del framework rCUDA, esdevé una eina clau per als futurs planificadors de treballs en clústers heterogenis.[EN] In the last decade the use of GPGPU (General Purpose computing in Graphics Processing Units) has become extremely popular in data centers around the world. GPUs (Graphics Processing Units) have been established as computational accelerators that are used alongside CPUs to form heterogeneous systems. The massively parallel nature of GPUs, traditionally intended for graphics computing, allows to perform numerical operations with data arrays at high speed. This is achieved thanks to the large number of cores GPUs integrate and the large bandwidth of memory access. Consequently, applications of all kinds of fields, such as chemistry, physics, engineering, artificial intelligence, materials science, and so on, presenting this type of computational patterns are benefited by drastically reducing their execution time. In general, the use of computing acceleration provided by GPUs has meant a step forward and a revolution, but it is not without problems, such as energy efficiency problems, low utilization of GPUs, high acquisition and maintenance costs, etc. In this PhD thesis we aim to analyze the main shortcomings of these heterogeneous systems and propose solutions based on the use of remote GPU virtualization. To that end, we have used the rCUDA middleware, developed at Universitat Politècnica de València. Many publications support rCUDA as the most advanced remote GPU virtualization framework nowadays. The results obtained in this PhD thesis show that the use of rCUDA in Cloud Computing environments increases the degree of freedom of the system, as it allows to create virtual instances of the physical GPUs fully tailored to the needs of each of the virtual machines. In HPC (High Performance Computing) environments, rCUDA also provides a greater degree of flexibility in the use of GPUs throughout the computing cluster, as it allows the CPU part to be completely decoupled from the GPU part of the applications. In addition, GPUs can be on any node in the cluster, regardless of the node on which the CPU part of the application is running. In general, both for Cloud Computing and in the case of HPC, this greater degree of flexibility translates into an up to 2x increase in system-wide throughput while reducing energy consumption by approximately 15%. Finally, we have also developed a job migration mechanism for the GPU part of applications that has been integrated within the rCUDA middleware. This migration mechanism has been evaluated and the results clearly show that, in exchange for a small overhead of about 400 milliseconds in the execution time of the applications, it is a powerful tool with which, again, we can increase productivity and reduce energy foot print of the computing system. In summary, this PhD thesis analyzes the main problems arising from the use of GPUs as computing accelerators, both in HPC and Cloud Computing environments, and demonstrates how thanks to the use of the rCUDA middleware these problems can be addressed. In addition, a powerful GPU job migration mechanism is being developed, which, integrated within the rCUDA framework, becomes a key tool for future job schedulers in heterogeneous clusters.This work jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grants (20524/PDC/18, 20813/PI/18 and 20988/PI/18) and by the Spanish MEC and European Commission FEDER under grants TIN2015-66972-C5-3-R, TIN2016-78799-P and CTQ2017-87974-R (AEI/FEDER, UE). We also thank NVIDIA for hardware donation under GPU Educational Center 2014-2016 and Research Center 2015-2016. The authors thankfully acknowledge the computer resources at CTE-POWER and the technical support provided by Barcelona Supercomputing Center - Centro Nacional de Supercomputación (RES-BCV-2018-3-0008). Furthermore, researchers from Universitat Politècnica de València are supported by the Generalitat Valenciana under Grant PROMETEO/2017/077. Authors are also grateful for the generous support provided by Mellanox Technologies Inc. Prof. Pradipta Purkayastha, from Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, is acknowledged for kindly providing the initial ligand and DNA structures.Prades Gasulla, J. (2021). Improving Performance and Energy Efficiency of Heterogeneous Systems with rCUDA [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/168081TESISCompendi