Relevant bibliographies by topics / Computing clusters

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computing clusters'

Author: Grafiati
Published: 30 May 2022
Last updated: 25 July 2025

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Journal articles on the topic "Computing clusters"

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ROSENBERG, ARNOLD L., and RON C. CHIANG. "HETEROGENEITY IN COMPUTING: INSIGHTS FROM A WORKSHARING SCHEDULING PROBLEM." International Journal of Foundations of Computer Science 22, no. 06 (2011): 1471–93. http://dx.doi.org/10.1142/s0129054111008829.

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Heterogeneity complicates the use of multicomputer platforms. Can it also enhance their performance? How can one measure the power of a heterogeneous assemblage of computers ("cluster"), in absolute terms (how powerful is this cluster) and relative terms (which cluster is more powerful)? Is a cluster that has one super-fast computer and the rest of "average" speed more/less powerful than one all of whose computers are "moderately" fast? If you can replace just one computer in a cluster with a faster one, should you replace the fastest? the slowest? A result concerning "worksharing" in heteroge
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WEBER, MICHAEL. "WORKSTATION CLUSTERS: ONE WAY TO PARALLEL COMPUTING." International Journal of Modern Physics C 04, no. 06 (1993): 1307–14. http://dx.doi.org/10.1142/s0129183193001026.

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The feasibility and constraints of workstation clusters for parallel processing are investigated. Measurements of latency and bandwidth are presented to fix the position of clusters in comparison to massively parallel systems. So it becomes possible to identify the kind of applications that seem to be suited for running on a cluster.
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Tripathy, Minakshi, and C. R. Tripathy. "A Comparative Analysis of Performance of Shared Memory Cluster Computing Interconnection Systems." Journal of Computer Networks and Communications 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/128438.

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In recent past, many types of shared memory cluster computing interconnection systems have been proposed. Each of these systems has its own advantages and limitations. With the increase in system size of the cluster interconnection systems, the comparative analysis of their various performance measures becomes quite inevitable. The cluster architecture, load balancing, and fault tolerance are some of the important aspects, which need to be addressed. The comparison needs to be made in order to choose the best one for a particular application. In this paper, a detailed comparative study on four
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М.Ю., Воробьев, Рыбаков А.А. та Сальников А.Н. "Инструмент для моделирования балансировки потока задач пользователей между несколькими независимыми вычислительными кластерами". Труды НИИСИ РАН 9, № 6 (2020): 142–47. http://dx.doi.org/10.25682/niisi.2019.6.0018.

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Работа посвящена организации программного окружения для анализа алгоритмов балансировки нагрузки на ресурсы группы вычислительных кластеров, используемых для высокопроизводительных вычислений. Моделирование производилось путем одновременного запуска нескольких систем ведения очередей SLURM, работающих на нескольких виртуальных машинах в режиме мультиузла. Поток задач создавался посредством инструмента моделирования pseudo-cluster, модифицированного для поддержки одновременной работы с несколькими кластерами. Система апробирована на модельном журнале вычислительных задач пользователей, состояще
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Stone, J., and F. Ercal. "Workstation clusters for parallel computing." IEEE Potentials 20, no. 2 (2001): 31–33. http://dx.doi.org/10.1109/45.954655.

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Singhania, Shrinkhala, and Monika Tak. "Workstation Clusters for Parallel Computing." International Journal of Engineering Trends and Technology 28, no. 1 (2015): 13–14. http://dx.doi.org/10.14445/22315381/ijett-v28p203.

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Regassa, Dereje, Heonyoung Yeom, and Yongseok Son. "Harvesting the Aggregate Computing Power of Commodity Computers for Supercomputing Applications." Applied Sciences 12, no. 10 (2022): 5113. http://dx.doi.org/10.3390/app12105113.

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Distributed supercomputing is becoming common in different companies and academia. Most of the parallel computing researchers focused on harnessing the power of commodity processors and even internet computers to aggregate their computation powers to solve computationally complex problems. Using flexible commodity cluster computers for supercomputing workloads over a dedicated supercomputer and expensive high-performance computing (HPC) infrastructure is cost-effective. Its scalable nature can make it better employed to the available organizational resources, which can benefit researchers who
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Radchenko, I., O. Shekhovtsov, A. Kovalenko, and O. Sytnyk. "FORMATION OF CLUSTERS ON SINGLE-BOARD COMPUTERS IN IOT NETWORKS." Системи управління, навігації та зв’язку. Збірник наукових праць 2, no. 76 (2024): 141–43. http://dx.doi.org/10.26906/sunz.2024.2.141.

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The article looks at the problem of using single -board computers for Internet technology . An analysis of current single-board computers in various countries was carried out. Single-board computers and clusters of single-board computers have found their place in the concept of edge computing, allowing optimization of hard computing by placing computing resources closer to the core. The idea of a “virtual cluster” lies in the unification and organization of disparate heterogeneous devices for the development of various complex computing tasks from the available resources of the existing infras
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S., Shreyanth, and Niveditha S. "CLUSTER-BASED GRID COMPUTING ON WIRELESS NETWORK DATA TRANSMISSION WITH ROUTING ANALYSIS PROTOCOL AND DEEP LEARNING." International Journal of Advanced Research 11, no. 06 (2023): 517–34. http://dx.doi.org/10.21474/ijar01/17096.

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Grid computing based on clusters has emerged as a promising strategy for improving the efficacy of wireless network data transmission. This study examines the incorporation of cluster-based grid computing, routing analysis protocols, and deep learning techniques to optimize data transmission in wireless networks. The proposed method utilizes clusters to distribute computing duties and enhance resource utilization, resulting in efficient data transmission. To further improve the routing process, a novel routing analysis protocol is introduced, which dynamically adapts to network conditions and
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Sheng, Chong Chong, Wei Song Hu, Xin Ming Hu, and Bai Feng Wu. "StreamMAP: Automatic Task Assignment System on GPU Cluster." Advanced Materials Research 926-930 (May 2014): 2414–17. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2414.

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GPU Clusters which use General-Purpose GPUs (GPGPUs) as accelerators are becoming more and more popular in high performance computing area. Currently the mainly used programming model for GPU cluster is hybrid MPI/CUDA. However, when using this model, programmers tend to need detailed knowledge of the hardware resources, which makes the program more complicated and less portable. In this paper, we present StreamMAP, an automatic task assignment system on GPU Clusters. The main contributions of StreamMAP are (1) It provides powerful yet concise language extension suitable to describe the comput
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Dissertations / Theses on the topic "Computing clusters"

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Shum, Kam Hong. "Adaptive parallelism for computing on heterogeneous clusters." Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627563.

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HEMMATPOUR, MASOUD. "High Performance Computing using Infiniband-based clusters." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2750549.

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Aji, Ashwin M. "Programming High-Performance Clusters with Heterogeneous Computing Devices." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52366.

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Today's high-performance computing (HPC) clusters are seeing an increase in the adoption of accelerators like GPUs, FPGAs and co-processors, leading to heterogeneity in the computation and memory subsystems. To program such systems, application developers typically employ a hybrid programming model of MPI across the compute nodes in the cluster and an accelerator-specific library (e.g.; CUDA, OpenCL, OpenMP, OpenACC) across the accelerator devices within each compute node. Such explicit management of disjointed computation and memory resources leads to reduced productivity and performance. Thi
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周志賢 and Chi-yin Edward Chow. "Adaptive recovery with hierarchical checkpointing on workstation clusters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B29812914.

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Chow, Chi-yin Edward. "Adaptive recovery with hierarchical checkpointing on workstation clusters /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20792700.

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Melas, Panagiotis. "The performance evaluation of workstation clusters." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326395.

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Ribeiro, Tiago Filipe Rodrigues. "Developing and evaluating clopencl applications for heterogeneous clusters." Master's thesis, Instituto Politécnico de Bragança, Escola Superior de Tecnologia e Gestão, 2012. http://hdl.handle.net/10198/7948.

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In the last few years, the computing systems processing capabilities have increased significantly, changing from single-core to multi-core and even many-core systems. Accompanying this evolution, local networks have also become faster, with multi-gigabit technologies like Infiniband, Myrinet and 10G Ethernet. Parallel/distributed programming tools and standards, like POSIX Threads, OpenMP and MPI, have helped to explore these technologies and have been frequently combined, giving rise to Hybrid Programming Models. Recently, co-processors like GPUs and FPGAs, started to be used as accelerators
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Rough, Justin, and mikewood@deakin edu au. "A Platform for reliable computing on clusters using group communications." Deakin University. School of Computing and Mathematics, 2001. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20060412.141015.

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Shared clusters represent an excellent platform for the execution of parallel applications given their low price/performance ratio and the presence of cluster infrastructure in many organisations. The focus of recent research efforts are on parallelism management, transport and efficient access to resources, and making clusters easy to use. In this thesis, we examine reliable parallel computing on clusters. The aim of this research is to demonstrate the feasibility of developing an operating system facility providing transport fault tolerance using existing, enhanced and newly built operati
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Daillidis, Christos. "Establishing Linux Clusters for high-performance computing (HPC) at NPS." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sept%5FDaillidis.pdf.

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Nakad, Zahi Samir. "High Performance Applications on Reconfigurable Clusters." Thesis, Virginia Tech, 2000. http://hdl.handle.net/10919/35682.

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Many problems faced in the engineering world are computationally intensive. Filtering using FIR (Finite Impulse Response) filters is an example to that. This thesis discusses the implementation of a fast, reconfigurable, and scalable FIR (Finite Impulse Response) digital filter. Constant coefficient multipliers and a Fast FIFO implementation are also discussed in connection with the FIR filter. This filter is used in two of its structures: the direct-form and the lattice structure. The thesis describes several configurations that can be created with the different components available and repor
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Books on the topic "Computing clusters"

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Pfister, Gregory F. In search of clusters. 2nd ed. Prentice Hall PTR, 1998.

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Corporation, International Business Machines, ed. HPC clusters using Infiniband on IBM Power systems servers. Vervante, 2009.

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Lawrence, Sterling Thomas, ed. Beowulf cluster computing with Linux. MIT Press, 2002.

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International Business Machines Corporation. International Technical Support Organization, ed. Configuring and managing AIX clusters using xCAT 2. International Technical Support Organization, 2009.

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Lawrence, Sterling Thomas, ed. Beowulf cluster computing with Windows. MIT Press, 2002.

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Geoffrey, Fox, and Dongarra J. J, eds. Distributed and cloud computing: Clusters, grids, clouds, and the future Internet. Morgan Kaufmann, 2012.

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Kim, Jong Yul. On SIP Server Clusters and the Migration to Cloud Computing Platforms. [publisher not identified], 2016.

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IEEE, International Conference on Cluster Computing (7th 2005 Burlington Mass ). 2005 IEEE International Conference on Cluster Computing (CLUSTER): Burlington, MA, 27-30 September, 2005. IEEE, 2006.

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William, Gropp, IEEE Computer Society, and IEEE Computer Society. Task Force on Cluster Computing., eds. Proceedings: 2002 IEEE International Conference on Cluster Computing, 23-26 September 2002, Chicago, Illinois. IEEE Computer Society, 2002.

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IEEE International Conference on Cluster Computing (3rd 2001 Newport Beach, California, USA). 2001 IEEE International Conference on Cluster Computing: Proceedings : 8-11 October 2001, Newport Beach, California, USA. IEEE Computer Society, 2001.

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Book chapters on the topic "Computing clusters"

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Steele, Guy L., Xiaowei Shen, Josep Torrellas, et al. "Clusters." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_18.

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Kielmann, Thilo, Sergei Gorlatch, Utpal Banerjee, et al. "Beowulf Clusters." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2119.

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Steele, Guy L., Xiaowei Shen, Josep Torrellas, et al. "Commodity Clusters." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2121.

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Dongarra, Jack, Piotr Luszczek, Paul Feautrier, et al. "Linux Clusters." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2123.

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Leasure, Bruce, David J. Kuck, Sergei Gorlatch, et al. "PC Clusters." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2125.

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Kwiatkowski, Jan, Marcin Pawlik, Gerard Frankowski, Kazimierz Balos, Roman Wyrzykowski, and Konrad Karczewski. "Dynamic Clusters Available Under Clusterix Grid." In Applied Parallel Computing. State of the Art in Scientific Computing. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75755-9_99.

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Kielmann, Thilo, Sergei Gorlatch, Utpal Banerjee, et al. "Beowulf-Class Clusters." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2118.

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Narang, Tulika. "Finding Clusters of Data: Cluster Analysis in R." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3153-3_63.

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Dolz, Manuel F., Juan C. Fernández, Rafael Mayo, and Enrique S. Quintana-Ortí. "EnergySaving Cluster Roll: Power Saving System for Clusters." In Architecture of Computing Systems - ARCS 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11950-7_15.

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Rao, Abhijit, Rajat Upadhyay, Nirav Shah, Sagar Arlekar, Jayanth Raghothamma, and Shrisha Rao. "Cluster Performance Forecasting Using Predictive Modeling for Virtual Beowulf Clusters." In Distributed Computing and Networking. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92295-7_55.

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Conference papers on the topic "Computing clusters"

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Dugerdil, Philippe, and Sebastien Jossi. "Computing dynamic clusters." In Proceeding of the 2nd annual conference. ACM Press, 2009. http://dx.doi.org/10.1145/1506216.1506228.

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Nakata, Maho. "All about RICC: RIKEN Integrated Cluster of Clusters." In 2011 Second International Conference on Networking and Computing (ICNC). IEEE, 2011. http://dx.doi.org/10.1109/icnc.2011.14.

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Németh, Gábor. "Session details: Clusters." In CF04: Computing Frontiers Conference. ACM, 2004. http://dx.doi.org/10.1145/3244174.

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Lapshina, S. Yu. "The Optimal Processor Cores' Number choice for the Parallel Cluster Multiple Labeling Technique on HighPerformance Computing Systems." In Всероссийская научная конференция "Единое цифровое пространство научных знаний: проблемы и решения". Москва, Берлин: Директмедиа Паблишинг, 2021. http://dx.doi.org/10.51218/978-5-4499-1905-2-2021-311-319.

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The article is about the research of a optimum number
 of processor cores for launching the Parallel Cluster Multiple Labeling Technique on modern supercomputer systems installed in the JSCC RAS.
 This technique may be used in any field as a tool for differentiating large lattice clusters, because it is given input in a format independent of the application. At the JSCC RAS, this tool was used to study the problem of the spread of epidemics, for which an appropriate multiagent model was developed.
 In the course of imitation experiments, a variant of the Parallel Cluster Multipl
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Roush, Ellard, and Zoram Thanga. "Zone Clusters: A virtual cluster based upon solaris containers." In 2009 IEEE International Conference on Cluster Computing and Workshops. IEEE, 2009. http://dx.doi.org/10.1109/clustr.2009.5289197.

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Kindratenko, Volodymyr V., Jeremy J. Enos, Guochun Shi, et al. "GPU clusters for high-performance computing." In 2009 IEEE International Conference on Cluster Computing and Workshops. IEEE, 2009. http://dx.doi.org/10.1109/clustr.2009.5289128.

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Sahba, Amin, and John J. Prevost. "Hypercube based clusters in Cloud Computing." In 2016 World Automation Congress (WAC). IEEE, 2016. http://dx.doi.org/10.1109/wac.2016.7582974.

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Böhm, Christian, Karin Kailing, Peer Kröger, and Arthur Zimek. "Computing Clusters of Correlation Connected objects." In the 2004 ACM SIGMOD international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1007568.1007620.

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Anedda, P., M. Gaggero, G. Busonera, O. Schiaratura, and G. Zanetti. "Flexible Clusters for High-Performance Computing." In 2010 IEEE 12th International Conference on High Performance Computing and Communications (HPCC 2010). IEEE, 2010. http://dx.doi.org/10.1109/hpcc.2010.34.

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Pramanick, Mauro, and Zhu. "A system recovery benchmark for clusters." In Proceedings IEEE International Conference on Cluster Computing CLUSTR-03. IEEE, 2003. http://dx.doi.org/10.1109/clustr.2003.1253338.

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Reports on the topic "Computing clusters"

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Garlick, J. I/O Forwarding on Livermore Computing Commodity Linux Clusters. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1070157.

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Dongarra, Jack, Thomas Sterling, Horst Simon, and Erich Strohmaier. High performance computing: Clusters, constellations, MPPs, and future directions. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/813392.

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Abu-Ghazaleh, Nael. Optimized Parallel Discrete Event Simulation (PDES) for High Performance Computing (HPC) Clusters. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada438052.

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Alsing, Paul, Michael Fanto, and A. M. Smith. Cluster State Quantum Computing. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada572237.

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Richards, Mark A., and Daniel P. Campbell. Rapidly Reconfigurable High Performance Computing Cluster. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada438586.

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Walizer, Laura, Robert Haehnel, Luke Allen, and Yonghu Wenren. Application of multi-fidelity methods to rotorcraft performance assessment. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48474.

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We present a Python-based multi-fidelity tool to estimate rotorcraft performance metrics. We use Gaussian-Process regression (GPR) methods to adaptively build a surrogate model using a small number of high-fidelity CFD points to improve estimates of performance metrics from a medium-fidelity comprehensive analysis model. To include GPR methods in our framework, we used the EmuKit Python package. Our framework adaptively chooses new high-fidelity points to run in regions where the model variance is high. These high-fidelity points are used to update the GPR model; convergence is reached when mo
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Duke, D. W., and T. P. Green. [Research toward a heterogeneous networked computing cluster]. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/674884.

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Li, Haoyuan, Ali Ghodsi, Matei Zaharia, Scott Shenker, and Ion Stoica. Reliable, Memory Speed Storage for Cluster Computing Frameworks. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada611854.

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Chen, H. Y., J. M. Brandt, and R. C. Armstrong. ATM-based cluster computing for multi-problem domains. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/415338.

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Eric Burger, Eric Burger. Studying Building Energy Use with a Micro Computing Cluster. Experiment, 2014. http://dx.doi.org/10.18258/3777.

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