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

Space-division multiplexing for fiber-wireless communications

We envision the application of optical Space-division Multiplexing (SDM) to the next generation fiber-wireless communications as a firm candidate to increase the end user capacity and provide adaptive radiofrequency-photonic interfaces. This approach relies on the concept of fiber-distributed signal processing, where the SDM fiber provides not only radio access distribution but also broadband microwave photonics signal processing. In particular, we present two different SDM fiber technologies: dispersion-engineered heterogeneous multicore fiber links and multicavity devices built upon the selective inscription of gratings in homogeneous multicore fibers.Comment: 4 pages, 20th International Conference on Transparent Optical Networks (ICTON), Girona (Spain), 2017. arXiv admin note: text overlap with arXiv:1810.1213

Instalación, configuración y evaluación de la red de interconexión EXTOLL en un entorno de memoria distribuida

EXTOLL es una nueva arquitectura de red de interconexión, desarrollada por la Universidad de Heidelberg, que pretende establecerse en el sector del HPCC como una alternativa altamente eficiente y económica. EXTOLL proporciona mecanismos de comunicación eficaces tanto para mensajes de pequeño tamaño como para la transmisión de grandes cantidades de datos mediante mecanismos de Remote Direct Memory Access (RDMA). Cabe destacar que el diseño de EXTOLL, actualmente en fase de desarrollo, está implementado en una Field Programmable Gate Array (FPGA), lo que permite realizar modificaciones pero resta prestaciones. Se espera que, para abaratar costes y aumentar prestaciones, el diseño completo acabe integrándose en un chip Application-Specific Integrated Circuit (ASIC) en un futuro cercano. Los resultados obtenidos muestran que el rendimiento usando EXTOLL es superior al de Gigabit Ethernet, obteniendo un speedup medio de 4x. Para finalizar la comparación de prestaciones, hemos adaptado una rutina paralela, capaz de generar imágenes pertenecientes al conjunto fractal de Mandelbrot, para poder apreciar el diferente nivel de prestaciones existente entre EXTOLL y Gigabit Ethernet en tiempo real. Esto es así porque la modificación realizada en esta aplicación genera muchas comunicaciones por tanto las diferencias de rendimiento de los sistemas de interconexión se ven reflejados en la velocidad de generación de las imágenes.Prades Gasulla, J. (2014). Instalación, configuración y evaluación de la red de interconexión EXTOLL en un entorno de memoria distribuida. http://hdl.handle.net/10251/39652.Archivo delegad

An intracellular redox sensor for reactive oxygen species at the M3-M4 linker of GABAArho1 receptors

Background and Purpose: Reactive oxygen species (ROS) are normally involved in cell oxidative stress but also play a role as cellular messengers in redox signalling; for example, modulating the activity of neurotransmitter receptors and ion channels. However, the direct actions of ROS on GABAA receptors were not previously demonstrated. In the present work, we studied the effects of ROS on GABAAρ1 receptor function. Experimental Approach: GABAAρ1 receptors were expressed in oocytes and GABA-evoked responses electrophysiologically recorded in the presence or absence of ROS. Chemical protection of cysteines by selective sulfhydryl reagents and site-directed mutagenesis studies were used to identify protein residues involved in ROS actions. Key Results: GABAAρ1 receptor-mediated responses were significantly enhanced in a concentration-dependent and reversible manner by H2O2. Potentiating effects were attenuated by a free radical scavenger, lipoic acid or an inhibitor of the Fenton reaction, deferoxamine. Each ρ1 subunit contains only three cysteine residues, two extracellular at the Cys-loop (C177 and C191) and one intracellular (C364) at the M3-M4 linker. Mutant GABAAρ1 receptors in which C364 was exchanged by alanine were completely insensitive to modulation, implying that this site, rather than a cysteine in the Cys-loop, is essential for ROS modulation. Conclusion and Implications: Our results show that the function of GABAAρ1 receptors is enhanced by ROS and that the intracellular C364 is the sensor for ROS actions.Fil: Beltrán González, Andrea Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular; ArgentinaFil: Calvo, Daniel Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular; ArgentinaFil: Gasulla, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular; Argentin

Towards a goal-oriented agent-based simulation framework for high-performance computing

Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning capabilities (at the expense of a limited number of agents per simulation). This paper describes a first attempt at putting goal-oriented agents into large agentbased (micro-)simulations. We discuss a model for goal-oriented agents in HighPerformance Computing (HPC) and then briefly discuss its implementation in PyCOMPSs (a library that eases the parallelisation of tasks) to build such a platform that benefits from a large number of agents with the capacity to execute complex cognitive agents.Peer ReviewedPostprint (author's final draft

A visual embedding for the unsupervised extraction of abstract semantics

Vector-space word representations obtained from neural network models have been shown to enable semantic operations based on vector arithmetic. In this paper, we explore the existence of similar information on vector representations of images. For that purpose we define a methodology to obtain large, sparse vector representations of image classes, and generate vectors through the state-of-the-art deep learning architecture GoogLeNet for 20 K images obtained from ImageNet. We first evaluate the resultant vector-space semantics through its correlation with WordNet distances, and find vector distances to be strongly correlated with linguistic semantics. We then explore the location of images within the vector space, finding elements close in WordNet to be clustered together, regardless of significant visual variances (e.g., 118 dog types). More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge (e.g., living things). Afterwards, we consider vector arithmetics. Although we are unable to obtain meaningful results on this regard, we discuss the various problem we encountered, and how we consider to solve them. Finally, we discuss the impact of our research for cognitive systems, focusing on the role of the architecture being used.This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051), and by the Core Research for Evolutional Science and Technology (CREST) program of Japan Science and Technology Agency (JST).Peer ReviewedPostprint (published version

Modeling optical fiber space division multiplexed quantum key distribution systems

© 2019 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited[EN] We report a model to use to evaluate the performance of multiple quantum key distribution (QKD) channel transmission using spatial division multiplexing (SDM) in multicore (MCF) and few-mode fibers (FMF). This model is then used to analyze the feasibility of QKD transmission in 7-core MCFs in two practical scenarios involving the (1) transmission of only QKD channels and (2) simultaneous transmission of QKD and classical channels. In the first case, standard homogeneous MCFs enable transmission distances per core compatible with transmission parameters (distance and net key rate) very close to those of single-core single-mode fibers. For the second case, heterogeneous MCFs must be employed to make this option feasible.European Regional Development Fund (ERDF); Galician Regional Government (project GRC2015/018 and agreement for funding AtlantTIC (Atlantic Research Center for Information and Communication Technologies)); European Research Council (ERC) (Consolidator Grant 724663); Spanish MINECO (TEC2016-80150-R project and Ramon y Cajal fellowship RYC-2014-16247 for I. Gasulla).Ureña-Gisbert, M.; Gasulla Mestre, I.; Fraile, FJ.; Capmany Francoy, J. (2019). Modeling optical fiber space division multiplexed quantum key distribution systems. Optics Express. 27(5):7047-7063. https://doi.org/10.1364/OE.27.00704770477063275Gisin, N., Ribordy, G., Tittel, W., & Zbinden, H. (2002). Quantum cryptography. Reviews of Modern Physics, 74(1), 145-195. doi:10.1103/revmodphys.74.145Scarani, V., Bechmann-Pasquinucci, H., Cerf, N. J., Dušek, M., Lütkenhaus, N., & Peev, M. (2009). The security of practical quantum key distribution. Reviews of Modern Physics, 81(3), 1301-1350. doi:10.1103/revmodphys.81.1301Ekert, A., & Renner, R. (2014). The ultimate physical limits of privacy. Nature, 507(7493), 443-447. doi:10.1038/nature13132Tomamichel, M., Lim, C. C. W., Gisin, N., & Renner, R. (2012). Tight finite-key analysis for quantum cryptography. Nature Communications, 3(1). doi:10.1038/ncomms1631Lucamarini, M., Patel, K. A., Dynes, J. F., Fröhlich, B., Sharpe, A. W., Dixon, A. R., … Shields, A. J. (2013). Efficient decoy-state quantum key distribution with quantified security. Optics Express, 21(21), 24550. doi:10.1364/oe.21.024550Yuan, Z. L., Kardynal, B. E., Sharpe, A. W., & Shields, A. J. (2007). High speed single photon detection in the near infrared. Applied Physics Letters, 91(4), 041114. doi:10.1063/1.2760135Namekata, N., Adachi, S., & Inoue, S. (2010). Ultra-Low-Noise Sinusoidally Gated Avalanche Photodiode for High-Speed Single-Photon Detection at Telecommunication Wavelengths. IEEE Photonics Technology Letters, 22(8), 529-531. doi:10.1109/lpt.2010.2042054Sasaki, M., Fujiwara, M., Ishizuka, H., Klaus, W., Wakui, K., Takeoka, M., … Zeilinger, A. (2011). Field test of quantum key distribution in the Tokyo QKD Network. Optics Express, 19(11), 10387. doi:10.1364/oe.19.010387Peev, M., Pacher, C., Alléaume, R., Barreiro, C., Bouda, J., Boxleitner, W., … Dynes, J. F. (2009). The SECOQC quantum key distribution network in Vienna. New Journal of Physics, 11(7), 075001. doi:10.1088/1367-2630/11/7/075001Chen, T.-Y., Wang, J., Liang, H., Liu, W.-Y., Liu, Y., Jiang, X., … Pan, J.-W. (2010). Metropolitan all-pass and inter-city quantum communication network. Optics Express, 18(26), 27217. doi:10.1364/oe.18.027217Ciurana, A., Martínez-Mateo, J., Peev, M., Poppe, A., Walenta, N., Zbinden, H., & Martín, V. (2014). Quantum metropolitan optical network based on wavelength division multiplexing. Optics Express, 22(2), 1576. doi:10.1364/oe.22.001576Fröhlich, B., Dynes, J. F., Lucamarini, M., Sharpe, A. W., Yuan, Z., & Shields, A. J. (2013). A quantum access network. Nature, 501(7465), 69-72. doi:10.1038/nature12493Winzer, P. J., Neilson, D. T., & Chraplyvy, A. R. (2018). Fiber-optic transmission and networking: the previous 20 and the next 20 years [Invited]. Optics Express, 26(18), 24190. doi:10.1364/oe.26.024190Shariati, B., Mastropaolo, A., Diamantopoulos, N.-P., Rivas-Moscoso, J. M., Klonidis, D., & Tomkos, I. (2018). Physical-Layer-Aware Performance Evaluation of SDM Networks Based on SMF Bundles, MCFs, and FMFs. Journal of Optical Communications and Networking, 10(9), 712. doi:10.1364/jocn.10.000712Galve, J. M., Gasulla, I., Sales, S., & Capmany, J. (2016). Reconfigurable Radio Access Networks Using Multicore Fibers. IEEE Journal of Quantum Electronics, 52(1), 1-7. doi:10.1109/jqe.2015.2497244Dynes, J. F., Kindness, S. J., Tam, S. W.-B., Plews, A., Sharpe, A. W., Lucamarini, M., … Shields, A. J. (2016). Quantum key distribution over multicore fiber. Optics Express, 24(8), 8081. doi:10.1364/oe.24.008081Cañas, G., Vera, N., Cariñe, J., González, P., Cardenas, J., Connolly, P. W. R., … Lima, G. (2017). High-dimensional decoy-state quantum key distribution over multicore telecommunication fibers. Physical Review A, 96(2). doi:10.1103/physreva.96.022317Lo, H.-K., Ma, X., & Chen, K. (2005). Decoy State Quantum Key Distribution. Physical Review Letters, 94(23). doi:10.1103/physrevlett.94.230504Capmany, J. (2009). Photon nonlinear mixing in subcarrier multiplexed quantum key distribution systems. Optics Express, 17(8), 6457. doi:10.1364/oe.17.006457Koshiba, M., Saitoh, K., Takenaga, K., & Matsuo, S. (2012). Analytical Expression of Average Power-Coupling Coefficients for Estimating Intercore Crosstalk in Multicore Fibers. IEEE Photonics Journal, 4(5), 1987-1995. doi:10.1109/jphot.2012.2221085Tu, J., Saitoh, K., Koshiba, M., Takenaga, K., & Matsuo, S. (2012). Design and analysis of large-effective-area heterogeneous trench-assisted multi-core fiber. Optics Express, 20(14), 15157. doi:10.1364/oe.20.015157Hayashi, T., Taru, T., Shimakawa, O., Sasaki, T., & Sasaoka, E. (2011). Design and fabrication of ultra-low crosstalk and low-loss multi-core fiber. Optics Express, 19(17), 16576. doi:10.1364/oe.19.016576Choi, I., Young, R. J., & Townsend, P. D. (2010). Quantum key distribution on a 10Gb/s WDM-PON. Optics Express, 18(9), 9600. doi:10.1364/oe.18.009600Mora, J., Amaya, W., Ruiz-Alba, A., Martinez, A., Calvo, D., Muñoz, V. G., & Capmany, J. (2012). Simultaneous transmission of 20x2 WDM/SCM-QKD and 4 bidirectional classical channels over a PON. Optics Express, 20(15), 16358. doi:10.1364/oe.20.016358Mora, J., Ruiz-Alba, A., Amaya, W., Martínez, A., García-Muñoz, V., Calvo, D., & Capmany, J. (2012). Experimental demonstration of subcarrier multiplexed quantum key distribution system. Optics Letters, 37(11), 2031. doi:10.1364/ol.37.002031Gleim, A. V., Egorov, V. I., Nazarov, Y. V., Smirnov, S. V., Chistyakov, V. V., Bannik, O. I., … Buller, G. S. (2016). Secure polarization-independent subcarrier quantum key distribution in optical fiber channel using BB84 protocol with a strong reference. Optics Express, 24(3), 2619. doi:10.1364/oe.24.002619Yoshino, K., Ochi, T., Fujiwara, M., Sasaki, M., & Tajima, A. (2013). Maintenance-free operation of WDM quantum key distribution system through a field fiber over 30 days. Optics Express, 21(25), 31395. doi:10.1364/oe.21.03139

Enhancing large-scale docking simulation on heterogeneous systems: An MPI vs rCUDA study

[EN] Virtual Screening (VS) methods can considerably aid clinical research by predicting how ligands interact with pharmacological targets, thus accelerating the slow and critical process of finding new drugs. VS methods screen large databases of chemical compounds to find a candidate that interacts with a given target. The computational requirements of VS models, along with the size of the databases, containing up to millions of biological macromolecular structures, means computer clusters are a must. However, programming current clusters of computers is no easy task, as they have become heterogeneous and distributed systems where various programming models need to be used together to fully leverage their resources. This paper evaluates several strategies to provide peak performance to a GPU-based molecular docking application called METADOCK in heterogeneous clusters of computers based on CPU and NVIDIA Graphics Processing Units (GPUs). Our developments start with an OpenMP, MPI and CUDA METADOCK version as a baseline case of cluster utilization. Next, we explore the virtualized GPUs provided by the rCUDA framework in order to facilitate the programming process. rCUDA allows us to use remote GPUs, i.e. installed in other nodes of the cluster, as if they were installed in the local node, so enabling access to them using only OpenMP and CUDA. Finally, several load balancing strategies are analyzed in a search to enhance performance. Our results reveal that the use of middleware like rCUDA is a convincing alternative to leveraging heterogeneous clusters, as it offers even better performance than traditional approaches and also makes it easier to program these emerging clusters.This work is jointly supported by the Fundacion Seneca (Agencia Regional de Ciencia y Tecnologia, Region de Murcia) under grant 18946/JLI/13, and by the Spanish MEC and European Commission FEDER under grants TIN2015-66972-C5-3-R and TIN2016-78799-P (AEI/FEDER, UE). We also thank NVIDIA for hardware donation under GPU Educational Center 2014-2016 and Research Center 2015-2016. Furthermore, researchers from Universitat Politecnica de Valencia are supported by the Generalitat Valenciana under Grant PROMETEO/2017/077. Authors are also grateful for the generous support provided by Mellanox Technologies Inc.Imbernón, B.; Prades Gasulla, J.; Gimenez Canovas, D.; Cecilia, JM.; Silla Jiménez, F. (2018). Enhancing large-scale docking simulation on heterogeneous systems: An MPI vs rCUDA study. Future Generation Computer Systems. 79:26-37. https://doi.org/10.1016/j.future.2017.08.050S26377

Social network data analysis for event detection

Cities concentrate enough Social Network (SN) activity to empower rich models. We present an approach to event discovery based on the information provided by three SN, minimizing the data properties used to maximize the total amount of usable data. We build a model of the normal city behavior which we use to detect abnormal situations (events). After collecting half a year of data we show examples of the events detected and introduce some applications.Peer ReviewedPostprint (published version