49 research outputs found

    Enabling Hyperscale Web Services

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    Modern web services such as social media, online messaging, web search, video streaming, and online banking often support billions of users, requiring data centers that scale to hundreds of thousands of servers, i.e., hyperscale. In fact, the world continues to expect hyperscale computing to drive more futuristic applications such as virtual reality, self-driving cars, conversational AI, and the Internet of Things. This dissertation presents technologies that will enable tomorrow’s web services to meet the world’s expectations. The key challenge in enabling hyperscale web services arises from two important trends. First, over the past few years, there has been a radical shift in hyperscale computing due to an unprecedented growth in data, users, and web service software functionality. Second, modern hardware can no longer support this growth in hyperscale trends due to a decline in hardware performance scaling. To enable this new hyperscale era, hardware architects must become more aware of hyperscale software needs and software researchers can no longer expect unlimited hardware performance scaling. In short, systems researchers can no longer follow the traditional approach of building each layer of the systems stack separately. Instead, they must rethink the synergy between the software and hardware worlds from the ground up. This dissertation establishes such a synergy to enable futuristic hyperscale web services. This dissertation bridges the software and hardware worlds, demonstrating the importance of that bridge in realizing efficient hyperscale web services via solutions that span the systems stack. The specific goal is to design software that is aware of new hardware constraints and architect hardware that efficiently supports new hyperscale software requirements. This dissertation spans two broad thrusts: (1) a software and (2) a hardware thrust to analyze the complex hyperscale design space and use insights from these analyses to design efficient cross-stack solutions for hyperscale computation. In the software thrust, this dissertation contributes uSuite, the first open-source benchmark suite of web services built with a new hyperscale software paradigm, that is used in academia and industry to study hyperscale behaviors. Next, this dissertation uses uSuite to study software threading implications in light of today’s hardware reality, identifying new insights in the age-old research area of software threading. Driven by these insights, this dissertation demonstrates how threading models must be redesigned at hyperscale by presenting an automated approach and tool, uTune, that makes intelligent run-time threading decisions. In the hardware thrust, this dissertation architects both commodity and custom hardware to efficiently support hyperscale software requirements. First, this dissertation characterizes commodity hardware’s shortcomings, revealing insights that influenced commercial CPU designs. Based on these insights, this dissertation presents an approach and tool, SoftSKU, that enables cheap commodity hardware to efficiently support new hyperscale software paradigms, improving the efficiency of real-world web services that serve billions of users, saving millions of dollars, and meaningfully reducing the global carbon footprint. This dissertation also presents a hardware-software co-design, uNotify, that redesigns commodity hardware with minimal modifications by using existing hardware mechanisms more intelligently to overcome new hyperscale overheads. Next, this dissertation characterizes how custom hardware must be designed at hyperscale, resulting in industry-academia benchmarking efforts, commercial hardware changes, and improved software development. Based on this characterization’s insights, this dissertation presents Accelerometer, an analytical model that estimates gains from hardware customization. Multiple hyperscale enterprises and hardware vendors use Accelerometer to make well-informed hardware decisions.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169802/1/akshitha_1.pd

    Improving Performance and Energy Efficiency of Heterogeneous Systems with rCUDA

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    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

    Proceedings of the 5th bwHPC Symposium

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    In modern science, the demand for more powerful and integrated research infrastructures is growing constantly to address computational challenges in data analysis, modeling and simulation. The bwHPC initiative, founded by the Ministry of Science, Research and the Arts and the universities in Baden-Württemberg, is a state-wide federated approach aimed at assisting scientists with mastering these challenges. At the 5th bwHPC Symposium in September 2018, scientific users, technical operators and government representatives came together for two days at the University of Freiburg. The symposium provided an opportunity to present scientific results that were obtained with the help of bwHPC resources. Additionally, the symposium served as a platform for discussing and exchanging ideas concerning the use of these large scientific infrastructures as well as its further development

    Arquitetura MultiGPU Distribuída Utilizando rCUDA: Um Estudo de Caso através da Avaliação de Desempenho de Estênceis Computacionais

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    O desenvolvimento de plataformas computacionais heterogêneas tem tornado mais acessíveis os recursos capazes de aumentar o desempenho de aplicações, porém, tais recursos, por vezes, não oferecem a escalabilidade desejada, tornando necessário o uso de soluções distribuídas. No entanto, a implementação de soluções de computação heterogênea distribuída torna necessária a combinação de ferramentas de programação específicas, o que aumenta a complexidade e dificulta a implementação. O presente trabalho apresenta a utilização de uma arquitetura MultiGPU distribuída utilizando o rCUDA, bem como, os ganhos de desempenho obtidos por meio da mesma, em uma aplicação baseada em estênceis computacionais

    On the Effect of using rCUDA to Provide CUDA Acceleration to Xen Virtual Machines

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    [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. Cluster Computing. 22(1):185-204. https://doi.org/10.1007/s10586-018-2845-0185204221Kernel-Based Virtual Machine, KVM. http://www.linux-kvm.org (2015). Accessed 19 Oct 2015Xen Project. http://www.xenproject.org/ (2015). Accessed 19 Oct 2015VMware Virtualization. http://www.vmware.com/ (2015). Accessed 19 Oct 2015Oracle VM VirtualBox. http://www.virtualbox.org/ (2015). Accessed 19 Oct 2015Semnanian, A., Pham, J., Englert, B., Wu, X.: Virtualization technology and its impact on computer hardware architecture. In: Proceedings of the Information Technology: New Generations, ITNG, pp. 719–724 (2011)Felter, W., Ferreira, A., Rajamony, R., Rubio, J.: An updated performance comparison of virtual machines and linux containers. In: IBM Research Report (2014)Zhang, J., Lu, X., Arnold, M., Panda, D.: MVAPICH2 over OpenStack with SR-IOV: an efficient approach to build HPC Clouds. In: Proceedings of the IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid, pp. 71–80 (2015)Wu, H., Diamos, G., Sheard, T., Aref, M., Baxter, S., Garland, M., Yalamanchili, S.: Red Fox: an execution environment for relational query processing on GPUs. In: Proceedings of the International Symposium on Code Generation and Optimization, CGO (2014)Playne, D.P., Hawick, K.A.: Data parallel three-dimensional Cahn-Hilliard field equation simulation on GPUs with CUDA. In: Proceedings of the Parallel and Distributed Processing Techniques and Applications, PDPTA, pp. 104–110 (2009)Yamazaki, I., Dong, T., Solcà, R., Tomov, S., Dongarra, J., Schulthess, T.: Tridiagonalization of a dense symmetric matrix on multiple GPUs and its application to symmetric eigenvalue problems. Concurr. Comput.: Pract. Exp. 26(16), 2652–2666 (2014)Luo, D.Y.: Canny edge detection on NVIDIA CUDA. In: Proceedings of the Computer Vision and Pattern Recognition Workshops, CVPR Workshops, pp. 1–8 (2008)Surkov, V.: Parallel option pricing with Fourier space time-stepping method on graphics processing units. Parallel Comput. 36(7), 372–380 (2010)Agarwal, P.K., Hampton, S., Poznanovic, J., Ramanthan, A., Alam, S.R., Crozier, P.S.: Performance modeling of microsecond scale biological molecular dynamics simulations on heterogeneous architectures. Concurr. Comput.: Pract. Exp. 25(10), 1356–1375 (2013)Luo, G.H., Huang, S.K., Chang, Y.S., Yuan, S.M.: A parallel bees algorithm implementation on GPU. J. Syst. Arch. 60(3), 271–279 (2014)NVIDIA GRID Technology. http://www.nvidia.com/object/grid-technology.html (2015). Accessed 19 Oct 2015Song, J., et al: KVMGT: a full GPU virtualization solution. In: KVM Forum (2014)AMD Multiuser GPU, Hardware-Based Virtualized Solution. http://www.amd.com/Documents/Multiuser-GPU-Datasheet.pdf (2015). Accessed 19 Oct 2015V-GPU: GPU Virtualization. https://github.com/zillians/platform_manifest_vgpu (2015). Accessed 19 Oct 2015Oikawa, M., Kawai, A., Nomura, K., Yasuoka, K., Yoshikawa, K., Narumi, T.: DS-CUDA: a middleware to use many GPUs in the cloud environment. In: Proceedings of the SC Companion: High Performance Computing, Networking Storage and Analysis, SCC, pp. 1207–1214 (2012)Reaño, C., Silla, F., Shainer, G., Schultz, S.: Local and remote GPUs perform similar with EDR 100G InfiniBand. In: Proceedings of the Industrial Track of the 16th International Middleware Conference, ACM, Middleware Industry ’15, pp. 4:1–4:7 (2015)Reaño, C., Silla, F., Duato, J.: Enhancing the rCUDA remote GPU virtualization framework: from a prototype to a production solution. In: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE Press, CCGrid ’17, pp. 695–698 (2017)Shi, L., Chen, H., Sun, J.: vCUDA: GPU accelerated high performance computing in virtual machines. In: Proceedings of the IEEE Parallel and Distributed Processing Symposium, IPDPS, pp. 1–11 (2009)Liang, T.Y., Chang, Y.W.: GridCuda: A grid-enabled CUDA programming toolkit. In: Proceedings of the IEEE Advanced Information Networking and Applications Workshops, WAINA, pp. 141–146 (2011)Giunta, G., Montella, R., Agrillo, G., Coviello, G.: A GPGPU transparent virtualization component for high performance computing clouds. In: Proceedings of the Euro-Par Parallel Processing, Euro-Par, pp. 379–391 (2010)Gupta, V., Gavrilovska, A., Schwan, K., Kharche, H., Tolia, N., Talwar, V., Ranganathan, P. GViM: GPU-accelerated virtual machines. In: Proceedings of the ACM Workshop on System-level Virtualization for High Performance Computing, HPCVirt, pp. 17–24 (2009)Merritt, A.M., Gupta, V., Verma, A., Gavrilovska, A., Schwan, K.: Shadowfax: scaling in heterogeneous cluster systems via GPGPU assemblies. In: Proceedings of the International Workshop on Virtualization Technologies in Distributed Computing, VTDC, pp. 3–10 (2011)Shadowfax II—Scalable Implementation of GPGPU Assemblies. http://keeneland.gatech.edu/software/keeneland/kidron (2015). Accessed 19 Oct 2015Walters, J.P., Younge, A.J., Kang, D.I., Yao, K.T., Kang, M., Crago, S.P., Fox, G.C.: GPU-passthrough performance: a comparison of KVM, Xen, VMWare ESXi, and LXC for CUDA and OpenCL applications. In: Proceedings of the IEEE International Conference on Cloud Computing, CLOUD (2014)Yang, C.T., Wang, H.Y., Ou, W.S., Liu, Y.T., Hsu, C.H.: On implementation of GPU virtualization using PCI pass-through. In: Proceedings of the IEEE Cloud Computing Technology and Science, CloudCom, pp. 711–716 (2012)Jo, H., Jeong, J., Lee, M., Choi, D.H.: Exploiting GPUs in virtual machine for BioCloud. BioMed Res. Int. 2013, 11 (2013). https://doi.org/10.1155/2013/939460NVIDIA: CUDA C Programming Guide 7.5. http://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide.pdf (2015a). Accessed 19 Oct 2015NVIDIA: CUDA Runtime API Reference Manual 7.5. http://docs.nvidia.com/cuda/pdf/CUDA_Runtime_API.pdf (2015b). Accessed 19 Oct 2015NVIDIA: The NVIDIA GPU Computing SDK Version 5.5 (2013)iperf3: A TCP, UDP, and SCTP Network Bandwidth Measurement Tool. https://github.com/esnet/iperf (2015). Accessed 19 Oct 2015Reaño, C., Silla, F.: Reducing the performance gap of remote GPU virtualization with InfiniBand Connect-IB. In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp. 920–925 (2016)Mellanox: Connect-IB Single and Dual QSFP+ Port PCI Express Gen3 x16 Adapter Card User Manual. http://www.mellanox.com/related-docs/user_manuals/Connect-IB_Single_and_Dual_QSFP+_Port_PCI_Express_Gen3_%20x16_Adapter_Card_User_Manual.pdf (2014a). Accessed 19 Oct 2015Mellanox: ConnectX-3 VPI Single and Dual QSFP+ Port Adapter Card User Manual 1.7. http://www.mellanox.com/related-docs/user_manuals/ConnectX-3_VPI_Single_and_Dual_QSFP_Port_Adapter_Card_User_Manual.pdf (2013). Accessed 19 Oct 2015Pérez, F., Reaño, C., Silla, F.: Providing CUDA acceleration to KVM virtual machines in InfiniBand clusters with rCUDA. In: 16th International Conference Distributed Applications and Interoperable Systems (DAIS), pp. 82–95. Springer International Publishing (2016)Mellanox: Mellanox OFED for Linux User Manual. http://www.mellanox.com/related-docs/prod_software/Mellanox_OFED_Linux_User_Manual_v2.3-1.0.1.pdf (2014b). Accessed 19 Oct 2015Reaño, C., Mayo, R., Quintana-Ortí, E., Silla, F., Duato, J., Peña, A.: Influence of InfiniBand FDR on the performance of remote GPU virtualization. In: Proceedings of the IEEE International Conference on Cluster Computing, CLUSTER, pp. 1–8 (2013)Laboratories, S.N.: LAMMPS Molecular Dynamics Simulator. http://lammps.sandia.gov/ (2013). Accessed 19 Oct 2015Liu, Y., Schmidt, B., Liu, W., Maskell, D.L.: CUDA-MEME: accelerating motif discovery in biological sequences using CUDA-enabled graphics processing units. Pattern Recognit. Lett. 31(14), 2170–2177 (2010)Liu, Y., Wirawan, A., Schmidt, B.: CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions. BMC Bioinformat. 14(1), 1–10 (2013)Vouzis, P.D., Sahinidis, N.V.: GPU-BLAST: using graphics processors to accelerate protein sequence alignment. Bioinformatics 27(2), 182–188 (2011)NVIDIA: NVIDIA Popular GPU-Accelerated Applications Catalog. http://www.nvidia.com/content/gpu-applications/PDF/GPU-apps-catalog-mar2015.pdf (2015c). Accessed 19 Oct 2015Liu, Y. CUDA-MEME. https://sites.google.com/site/yongchaosoftware/mcuda-meme (2014). Accessed 19 Oct 2015Polak, A.: Counting triangles in large graphs on GPU. In: IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 740–746 (2016)Prades, J., Silla, F.: Turning GPUs into floating devices over the cluster: the Beauty of GPU Migration. In: Proceedings of the 6th Workshop on Heterogeneous and Unconventional Cluster Architectures and Applications (HUCAA) (2017

    Evaluación del uso de GPUs virtualizadas en sistemas de bajo consumo

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    [ES] El trabajo que a continuación se presenta, estudia el rendimiento y las prestaciones de ejecutar ciertas aplicaciones en distintos equipos, entre ellos uno de bajo consumo, utilizando su hardware local, así como su ejecución utilizando una GPU (Graphics Processing Unit) remota ubicada en la misma red local, mediante virtualización. Se quiere comprobar las diferencias de rendimiento dependiendo del hardware que contiene cada uno de los equipos elegidos. Además, se quiere demostrar si la utilización de una GPU remota puede influir en el rendimiento y resultados de estas ejecuciones. Se utiliza un software llamado rCUDA, diseñado por el fabricante de GPU Nvidia, el cual es el encargado de poder usar una tarjeta gráfica de manera remota, ubicada en cualquier punto de una red, de esta manera poder aprovecharse de las prestaciones que ofrece esta tarjeta gráfica.[EN] The work that is presented below, studies the performance and benefits of certain applications in different equipment, including one of low consumption, using its local hardware, as well as its execution using a remote GPU (Graphics Processing Unit) located in the same local network, through virtualization. You want to check the performance differences depending on the hardware that each of the chosen equipment contains. In addition, we want to show if the use of a remote GPU can influence the performance and results of these executions. It uses a software called rCUDA, designed by the manufacturer of GPU Nvidia, which is responsible for being able to use a graphic card remotely, located at any point of a network, in this way to take advantage of the benefits offered this graphics card.[CA] El treball que a continuació es presenta, estudia el rendiment y les prestacions de executar certes aplicacions en distints equips, entre ells un de baix consum, utilitzant el seu hardware local, aixi com la seua execució utilitzant una GPU (Graphics Processing Unit) remota ubicada en la mateixa xarxa local, a través de la virtualizació. Es volen comprobar les diferencies de rendiment depenent dels components que contenen cada un dels equips de baix consum elegits. A mes es vol demostrar si utilittzar una GPU remota pot influir en el rendiment y resultats d`aquestes execucions. Es utilitza un software anomenat rCUDA, disenyat per el fabricant de GPU Nvidia, el cual es el encarregat de poder utilizar una tarjeta gráfica de forma remota, ubicada en qualsevol lloc de una xarxa, d`aquesta manera poder aprofitar les prestacions que oferix aquesta tarjeta gráfica.Palop Campos, J. (2018). Evaluación del uso de GPUs virtualizadas en sistemas de bajo consumo. http://hdl.handle.net/10251/110187TFG

    Proceedings of the 4th bwHPC Symposium

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    The bwHPC Symposium 2017 took place on October 4th, 2017, Alte Aula, Tübingen. It focused on the presentation of scientific computing projects as well as on the progress and the success stories of the bwHPC realization concept. The event offered a unique opportunity to engage in an active dialogue between scientific users, operators of bwHPC sites, and the bwHPC support team

    HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

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    High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
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