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 Jiménez, F. (2019). On the Effect of using rCUDA to Provide CUDA Acceleration to Xen Virtual Machines. 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Performance assessment of 40 Gbit/s off-the-shelf network cards for virtual network probes in 5G networks

Incoming 5G networks will evolve regarding how they operate due to the use of virtualization technologies. Network functions that are necessary for communication will be virtual and will run on top of commodity servers. Among these functions, it will be essential to deploy monitoring probes, which will provide information regarding how the network is behaving, which will be later analyzed for self-management purposes. However, to date, the network probes have needed to be physical to perform at link-rates in high-speed networks, and it is challenging to deploy them in virtual environments. Thus, it will be necessary to rely on bare-metal accelerators to deal with existing input/output (I/O) performance problems. Next, to control the costs of implementing these virtual network probes, our approach is to leverage the capabilities that current commercial off-the-shelf network cards provide for virtual environments. Specifically, to this end, we have implemented HPCAP40vf, which is a driver that is GPL-licensed and available for download, for network capture in virtual machines. This driver handles the communication with an Intel XL710 40 Gbit/s commercial network card to enable a network monitoring application run within a virtual machine. To store the captured traffic, we have relied on NVMe drives due to their high transference rate, as they are directly connected to the PCIe bus. We have assessed the performance of this approach and compared it with DPDK, in terms of both capturing and storing the network traffic by measuring the achieved data rates. The evaluation has taken into account two virtualization technologies, namely, KVM and Docker, and two access methods to the underlying hardware, namely, VirtIO and PCI passthrough. With this methodology, we have identified bottlenecks and determined the optimal solution in each case to reduce overheads due to virtualization. This approach can also be applied to the development of other performance-hungry virtual network functions. The obtained results demonstrate the feasibility of our proposed approach: when we correctly use the capabilities that current commercial network cards provide, our virtual network probe can monitor at 40 Gbit/s with full packet capture and storage and simultaneously track the traffic among other virtual network functions inside the host and with the external networkThis work has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under the project TRÁFICA (MINECO/FEDER TEC2015-69417-C2-1-R),and by the European Commission under the project H2020METRO-HAUL (Project ID:761727

Evaluation of messaging middleware for high-performance cloud computing

This is a post-peer-review, pre-copyedit version of an article published in Personal and Ubiquitous Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00779-012-0605-3[Abstract] Cloud computing is posing several challenges, such as security, fault tolerance, access interface singularity, and network constraints, both in terms of latency and bandwidth. In this scenario, the performance of communications depends both on the network fabric and its efficient support in virtualized environments, which ultimately determines the overall system performance. To solve the current network constraints in cloud services, their providers are deploying high-speed networks, such as 10 Gigabit Ethernet. This paper presents an evaluation of high-performance computing message-passing middleware on a cloud computing infrastructure, Amazon EC2 cluster compute instances, equipped with 10 Gigabit Ethernet. The analysis of the experimental results, confronted with a similar testbed, has shown the significant impact that virtualized environments still have on communication performance, which demands more efficient communication middleware support to get over the current cloud network limitations.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación y Ciencia; AP2010-434

General‐purpose computation on GPUs for high performance cloud computing

This is the peer reviewed version of the following article: Expósito, R. R., Taboada, G. L., Ramos, S., Touriño, J., & Doallo, R. (2013). General‐purpose computation on GPUs for high performance cloud computing. Concurrency and Computation: Practice and Experience, 25(12), 1628-1642., which has been published in final form at https://doi.org/10.1002/cpe.2845. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.[Abstract] Cloud computing is offering new approaches for High Performance Computing (HPC) as it provides dynamically scalable resources as a service over the Internet. In addition, General‐Purpose computation on Graphical Processing Units (GPGPU) has gained much attention from scientific computing in multiple domains, thus becoming an important programming model in HPC. Compute Unified Device Architecture (CUDA) has been established as a popular programming model for GPGPUs, removing the need for using the graphics APIs for computing applications. Open Computing Language (OpenCL) is an emerging alternative not only for GPGPU but also for any parallel architecture. GPU clusters, usually programmed with a hybrid parallel paradigm mixing Message Passing Interface (MPI) with CUDA/OpenCL, are currently gaining high popularity. Therefore, cloud providers are deploying clusters with multiple GPUs per node and high‐speed network interconnects in order to make them a feasible option for HPC as a Service (HPCaaS). This paper evaluates GPGPU for high performance cloud computing on a public cloud computing infrastructure, Amazon EC2 Cluster GPU Instances (CGI), equipped with NVIDIA Tesla GPUs and a 10 Gigabit Ethernet network. The analysis of the results, obtained using up to 64 GPUs and 256‐processor cores, has shown that GPGPU is a viable option for high performance cloud computing despite the significant impact that virtualized environments still have on network overhead, which still hampers the adoption of GPGPU communication‐intensive applications. CopyrightMinisterio de Ciencia e Innovación; TIN2010-1673

Continuous and Concurrent Network Connection for Hardware Virtualization

This project addresses the network connectivity in virtualization for cloud computing. Each Virtual Machine will be able to access the network concurrently and obtains continuous internet connectivity without any disruption. This project proposes a new method of resource sharing which is the Network Interface Card (NIC) among the Virtual Machines with each of them having the full access to it with near-native bandwidth. With this, could computing can perform resource allocation more effectively. This will be essential to migrate the each Operating System (Virtual Machine) that resides on one physical machine to another without disrupting its internet or network connection

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). CUDA–MEME: Accelerating motif discovery in biological sequences using CUDA-enabled graphics processing units. Pattern Recognition Letters, 31(14), 2170-2177. doi:10.1016/j.patrec.2009.10.009Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., … Lindahl, E. (2013). GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29(7), 845-854. doi:10.1093/bioinformatics/btt055Klus, P., Lam, S., Lyberg, D., Cheung, M., Pullan, G., McFarlane, I., … Lam, B. Y. (2012). BarraCUDA - a fast short read sequence aligner using graphics processing units. BMC Research Notes, 5(1), 27. doi:10.1186/1756-0500-5-27Kurtz, S., Phillippy, A., Delcher, A. L., Smoot, M., Shumway, M., Antonescu, C., & Salzberg, S. L. (2004). Genome Biology, 5(2), R12. doi:10.1186/gb-2004-5-2-r12Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199Phillips, J. 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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

Direct I/O solution for Containerized HPUX

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