Relevant bibliographies by topics / Dynamics, Distributed Computing

Academic literature on the topic 'Dynamics, Distributed Computing'

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Published: 11 March 2023

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Journal articles on the topic "Dynamics, Distributed Computing"

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Alistarh, Dan. "Distributed Computing Column 77 Consensus Dynamics." ACM SIGACT News 51, no. 1 (March 12, 2020): 57. http://dx.doi.org/10.1145/3388392.3388402.

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Duggan, Jim. "A distributed computing approach to system dynamics." System Dynamics Review 18, no. 1 (2002): 87–98. http://dx.doi.org/10.1002/sdr.228.

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Tang, Gang, Wei Jian Mi, Dao Fang Chang, Cheng Tao Wang, and Xue Ling Bai. "Distributed Computing for Mechanical Virtual Human." Advanced Materials Research 341-342 (September 2011): 695–99. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.695.

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To improve the efficiencies in kinematics, dynamics analysis and finite element (FE) calculation, distributed computing is used in the project of Chinese mechanical virtual human (CMVH). A three-dimensional (3D) musculoskeletal model of a male human and its finite element model have been constructed according to the male dataset of Chinese visible human (CVH). Many servers and software have been architected by using the method of distributed computing. Finally, a distributed computing platform by using these models to solving the parameters has been established. This distributed computing platform will provide wide applications in the areas such as medical engineering, robot design, physical and art education, sport, ergonomics and traffic accident analysis etc.
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Alnasir, Jamie. "Distributed Computing in a Pandemic." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 11, no. 1 (June 6, 2022): 19–43. http://dx.doi.org/10.14201/adcaij.27337.

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The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks.
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SCHEININE, ALAN LOUIS. "PARALLEL COMPUTING AT CRS4." International Journal of Modern Physics C 04, no. 06 (December 1993): 1315–21. http://dx.doi.org/10.1142/s0129183193001038.

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An overview is given of parallel computing work being done at CRS4 (Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna). Parallel computation projects include: parallelization of a simulation of the interaction of high energy particles with matter (GEANT), domain decomposition for numerical solution of partial differential equations, seismic migration for oil prospecting, finite-element structural analysis, parallel molecular dynamics, a C++ library for distributed processing of specific functions, and real-time visualization of a computer simulation that runs as distributed processes.
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Buch, I., M. J. Harvey, T. Giorgino, D. P. Anderson, and G. De Fabritiis. "High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing." Journal of Chemical Information and Modeling 50, no. 3 (March 3, 2010): 397–403. http://dx.doi.org/10.1021/ci900455r.

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Borgese, Gianluca, Calogero Pace, Pietro Pantano, and Eleonora Bilotta. "FPGA-Based Distributed Computing Microarchitecture for Complex Physical Dynamics Investigation." IEEE Transactions on Neural Networks and Learning Systems 24, no. 9 (September 2013): 1390–99. http://dx.doi.org/10.1109/tnnls.2013.2252924.

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Jaggard, Aaron D., Neil Lutz, Michael Schapira, and Rebecca N. Wright. "Dynamics at the Boundary of Game Theory and Distributed Computing." ACM Transactions on Economics and Computation 5, no. 3 (August 9, 2017): 1–20. http://dx.doi.org/10.1145/3107182.

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Wiredu, Gamel O., and Carsten Sørensen. "The dynamics of control and mobile computing in distributed activities." European Journal of Information Systems 15, no. 3 (June 2006): 307–19. http://dx.doi.org/10.1057/palgrave.ejis.3000577.

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Starostin, Igor, Sergey Khalyutin, Victoria Pavlova, and Elena Punt. "Distributed computing system for creating digital portraits of complex systems." MATEC Web of Conferences 341 (2021): 00046. http://dx.doi.org/10.1051/matecconf/202134100046.

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Various methods of operation of complex transport systems imply knowledge of mathematical models of their components. To obtain adequate models of such components, it is necessary to take into account the physical and chemical processes occurring in them. Previously, the authors developed a potential-flow method within the framework of modern nonequilibrium thermodynamics – a unified approach to the analysis and modeling of processes of various physical and chemical nature. In accordance with this approach, as well as with the methods of mechanics, the theory of electric and magnetic circuits, electrodynamics, etc., state functions for the properties of substances and the processes under consideration are set up to the experimentally studied constant coefficients. The system of equations of the considered processes dynamics is obtained from the given state functions. The desired model (digital portrait) of the considered component is constructed by numerical-analytical transformation of the dynamic equations system based on the use of experimental data. The need to automate the proposed method of obtaining digital portraits is due to its complexity and the need to process a large amount of data. An information and computing system is proposed, which implies the construction of a block diagram of the processes in the component under consideration (model-oriented approach). Modeling these processes using a block diagram at different values of unknown parameters allows us to approximate the model (digital portrait) a component based on the resulting set of output characteristic dynamics using machine learning libraries. Process modeling and further approximation of the model is parallelized. This paper is devoted to a distributed information-computing system that implements the creation of various complex systems digital portraits.
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Dissertations / Theses on the topic "Dynamics, Distributed Computing"

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Weed, Richard Allen. "Computational strategies for three-dimensional flow simulations on distributed computing systems." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/12154.

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RIZZO, SARA. "Simple Dynamics as Algorithms and Models." Doctoral thesis, Gran Sasso Science Institute, 2021. http://hdl.handle.net/20.500.12571/21452.

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The theory of Distributed Computing copes with systems composed of computa- tional entities able to interact with each other in order to reach a common goal in the most ecient way. Distributed models are used to study many phenomena that come from di↵erent disciplines such as computer science, physics, modern social sci- ence and biology. Common features of such systems are the lack of central control, a huge number of involved individuals, limited communication and computational power, presence of communication noise and faults propensity. Natural systems are able to solve very challenging tasks, relying on limited communication and com- putational resources, with undistinguishable entities. Moreover, at the right level of abstraction, natural and artificial systems solve the same problems: consensus, synchronization, fault tolerance and noise overcoming are some of them. Recently, these observations led researchers in the field to focus on the design and the analysis of simple and light-weight distributed protocols. This line of research includes the analysis of those processes that go by the name of Dynamics. Dynamics are simple stochastic processes on anonymous networks that evolve in rounds and in which each node has an initial state and updates it over time according to a function of its state and the states of its neighbors. Measures of interest in this kind of processes are the number of rounds needed to achieve the desired configuration, the storage capacity of every node and the size of the exchanged messages. In this thesis, we move a step forward in the analysis of dynamics and their ability to solve community detection and consensus problems. In the first part, we formally prove the e↵ectiveness of the Averaging dynamics in solving the community detection problem on a class of graphs containing a hidden k partition and characterized by a mild form of regularity. In the second part, we study the consensus time of some dynamics based on majority rules, introducing di↵erent forms of bias. In particular, in the context of opinion dynamics we define a unified framework to investigate di↵erent update rules when a bias toward one of the two possible opinions exists. The results show that the consensus time is extremely a↵ected by the underlying network structure and opinion dynamics and their interplay may elicit quite di↵erent collective behaviors. Finally, we analyze the k-Majority dynamics in a biased communication model where nodes have some probability to see a fixed state in their neighbors, as if in presence of binary asymmetric communication channels. In this setting, we identify sharp phase transitions on the bias and on the initial configuration.
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Bangalore, Ashok K. "Computational fluid dynamic studies of high lift rotor systems using distributed computing." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/12949.

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Liu, Xing. "High-performance algorithms and software for large-scale molecular simulation." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53487.

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Molecular simulation is an indispensable tool in many different disciplines such as physics, biology, chemical engineering, materials science, drug design, and others. Performing large-scale molecular simulation is of great interest to biologists and chemists, because many important biological and pharmaceutical phenomena can only be observed in very large molecule systems and after sufficiently long time dynamics. On the other hand, molecular simulation methods usually have very steep computational costs, which limits current molecular simulation studies to relatively small systems. The gap between the scale of molecular simulation that existing techniques can handle and the scale of interest has become a major barrier for applying molecular simulation to study real-world problems. In order to study large-scale molecular systems using molecular simulation, it requires developing highly parallel simulation algorithms and constantly adapting the algorithms to rapidly changing high performance computing architectures. However, many existing algorithms and codes for molecular simulation are from more than a decade ago, which were designed for sequential computers or early parallel architectures. They may not scale efficiently and do not fully exploit features of today's hardware. Given the rapid evolution in computer architectures, the time has come to revisit these molecular simulation algorithms and codes. In this thesis, we demonstrate our approach to addressing the computational challenges of large-scale molecular simulation by presenting both the high-performance algorithms and software for two important molecular simulation applications: Hartree-Fock (HF) calculations and hydrodynamics simulations, on highly parallel computer architectures. The algorithms and software presented in this thesis have been used by biologists and chemists to study some problems that were unable to solve using existing codes. The parallel techniques and methods developed in this work can be also applied to other molecular simulation applications.
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Ward, Koeck Alan. "Modeling and distributed computing of snow transport and delivery on meso-scale in a complex orography." Doctoral thesis, Universitat Oberta de Catalunya, 2015. http://hdl.handle.net/10803/327598.

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Aquest estudi descriu els principis de funcionament i validació d'un model d'ordinador de dinàmica de fluids computacional del procés de caiguda de neu sobre una orografia complexa. Es discretitza el domini espacial amb l'èmfasi principal sobre una topografia dificultosa que tendeix a produir volums deformes en la graella de càlcul. Es defineix una nova mesura de la deformació dels elements de la graella, i s'aplica per la discussió de diferents estratègies d'optimització de la graella per reduir el cost del càlcul paral·lel per ordinador de solucions a les equacions de transport de fluids de Navier-Stokes. Es dissenya un model per ordinador que resolgui les equacions Navier-Stokes per un fluid incompressible i torbulent. Es debat de l'eficiència de la caixa d'eines CFD. Es treballa el grau de connexió necessari entre les dues fases de neu i d'aire del fluid durant la modelització de la caiguda de neu mitjançant ordinador. S'implementa una metodologia Euler-Lagrangian de dos fluids. Es presenten aplicacions de caiguda de neu en relació amb la planificació de pistes d'esquí, la treta de neu de carreteres d'alta muntanya, i la planificació de la producció d'energia eòlica.
El estudio describe los principios de funcionamiento ya validación de un modelo para ordenador de dinámica de fluidos computacional del proceso de caída de nieve sobre una orografía compleja. Se discretea el dominio espacial con énfasis principal sobre una topografía dificultosa que tiende a producir volúmenes deformes en la cuadrícula de cálculo. Se define una nueva mesura de la deformación de los elementos de la cuadrícula, y se aplica en la discusión de diferentes estrategias de optimización de la cuadrícula para reducir el coste del cálculo paralelo por ordenador de soluciones de las ecuaciones de transporte de fluidos de Navier-Stokes. Se diseña un modelo por ordenador que resuelve las ecuaciones Navier-Stokes para un fluido incomprensible y turbulento. Se discute la eficiencia de la caja de herramientas CFD. Se trabaja el grado de conexión necesario entre las dos fases de nieve y de aire del fluido durante la modelización de la caída de nieve por ordenador. Se implementa una metodología Euler-Lagrangian de dos fluidos. Se presentan aplicaciones de caída de nieve en relación con la planificación de pistas de esquí, sacar la nieve de carreteras de alta momntaña, y la planificación de la producción de energía eólica.
This study describes the working principles and validation of a Computational Fluid Dynamics computer model of snowfall over a complex orography, for optimizing ski slope or other installations according to local weather patterns. The spatial domain is discretized, focusing on challenging topography that tends to produce deformed mesh volumes. A novel measure od mesh deformation is defined and applied to discuss different strategies of mesh optimization with the goal of facilitating parallel computer solutions of the Navier-Stokes fluid transport equations. A computer model is designed to solve the Navier-Stokes incompressible turbulent fluid equations. The efficiency of the CFD computational toolkit is discussed. The degree od coupling required between the snow – and air-phases of the fluid during the computer modeling of snowfall is discussed. A two-fluid (Euler-Lagrangian) methodology is implemented. Applications of such snowfall models are discussed in relation to ski-slope planning and high-altitude road snow clearing. An application of the model to wind energy production planning is presented.
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Buch, Mundó Ignasi 1984. "Investigation of protein-ligand interactions using high-throughput all-atom molecular dynamics simulations." Doctoral thesis, Universitat Pompeu Fabra, 2012. http://hdl.handle.net/10803/101407.

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Investigation of protein-ligand interactions has been a long-standing application for molecular dynamics (MD) simulations given its importance to drug design. However, relevant timescales for biomolecular motions are orders of magnitude longer than the commonly accessed simulation times. Adequate sampling of biomolecular phase-space has therefore been a major challenge in computational modeling that has limited its applicability. The primary objective for this thesis has been the brute-force simulation of costly protein-ligand binding modeling experiments on a large computing infrastructure. We have built and developed GPUGRID: a peta-scale distributed computing infrastructure for high-throughput MD simulations. We have used GPUGRID for the calculation of protein-ligand binding free energies as well as for the reconstruction of binding processes through unguided ligand binding simulations. The promising results presented herein, may have set the grounds for future applications of high-throughput MD simulations to drug discovery programs.
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Gao, Yiran. "Dynamic inter-domain distributed computing." Thesis, Queen Mary, University of London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.510898.

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Kelley, Ian Robert. "Data management in dynamic distributed computing environments." Thesis, Cardiff University, 2012. http://orca.cf.ac.uk/44477/.

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Data management in parallel computing systems is a broad and increasingly important research topic. As network speeds have surged, so too has the movement to transition storage and computation loads to wide-area network resources. The Grid, the Cloud, and Desktop Grids all represent different aspects of this movement towards highly-scalable, distributed, and utility computing. This dissertation contends that a peer-to-peer (P2P) networking paradigm is a natural match for data sharing within and between these heterogeneous network architectures. Peer-to-peer methods such as dynamic discovery, fault-tolerance, scalability, and ad-hoc security infrastructures provide excellent mappings for many of the requirements in today’s distributed computing environment. In recent years, volunteer Desktop Grids have seen a growth in data throughput as application areas expand and new problem sets emerge. These increasing data needs require storage networks that can scale to meet future demand while also facilitating expansion into new data-intensive research areas. Current practices are to mirror data from centralized locations, a technique that is not practical for growing data sets, dynamic projects, or data-intensive applications. The fusion of Desktop and Service Grids provides an ideal use-case to research peer-to-peer data distribution strategies in a hybrid environment. Desktop Grids have a data management gap, while integration with Service Grids raises new challenges with regard to cross-platform design. The work undertaken here is two-fold: first it explores how P2P techniques can be leveraged to meet the data management needs of Desktop Grids, and second, it shows how the same distribution paradigm can provide migration paths for Service Grid data. The result of this research is a Peer-to-Peer Architecture for Data-Intensive Cycle Sharing (ADICS) that is capable not only of distributing volunteer computing data, but also of providing a transitional platform and storage space for migrating Service Grid jobs to Desktop Grid environments.
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Fletcher, Luke. "A Dynamic Networked Browser Environment for Distributed Computing." Thesis, Honours thesis, University of Tasmania, 2002. https://eprints.utas.edu.au/38/1/Java_Distributed_Net_Thesis.pdf.

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Many organisations have a large number of computers with varying usage patterns. Some of these machines at different locations are often free from time to time leaving them to do very little useful computation or none at all. It is at these times that this dynamically changing environment of machines can be used for a more useful task. This project reports the development and feasibility testing of a dynamic distributed computing environment. This is achieved by making use of ubiquitous web browsers to harness these underutilised computers. Therefore taking the idea of distributed computing away from the traditional paradigm of fixed hosts to which it is often associated.
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Lepler, Joerg. "Creating dynamic application behavior for distributed performance analysis." Thesis, Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/8201.

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Books on the topic "Dynamics, Distributed Computing"

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Markus, Endler, ed. Context management for distributed and dynamic context-aware computing. London: Springer, 2012.

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da Rocha, Ricardo Couto Antunes, and Markus Endler. Context Management for Distributed and Dynamic Context-Aware Computing. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4020-7.

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1974-, Wang Lizhe, Chen Jinjun, and Jie Wei, eds. Quantitative quality of service for grid computing: Applications for heterogeneity, large-scale distribution, and dynamic environments. Hershey PA: Information Science Reference, 2009.

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1952-, Reinhardt J., ed. Neural networks: An introduction. Berlin: Springer-Verlag, 1990.

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Müller, Berndt. Neural networks: An introduction. 2nd ed. Berlin: Springer-Verlag, 1991.

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Müller, Berndt. Neural networks: An introduction. 2nd ed. Berlin: Springer, 1995.

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Crichton, Michael. Prey. New York: Harper, 2013.

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Crichton, Michael. Prey. New York, USA: Harper Collins Publishers, 2002.

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Crichton, Michael. Prey. New York: Harper Large Print, 2002.

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Crichton, Michael. Prey. London: HarperCollins Publishers, 2002.

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Book chapters on the topic "Dynamics, Distributed Computing"

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Sycara, Katia. "Dynamics of Information Propagation in Large Heterogeneous Networks." In Intelligent Distributed Computing V, 3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24013-3_1.

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Ramamritham, Krithi. "Tracking Dynamics Using Sensor Networks: Some Recurring Themes." In Distributed Computing and Networking, 1–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92295-7_1.

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Ramamritham, Krithi. "Taming the Dynamics of Disributed Data." In Distributed Computing and Internet Technology, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30555-2_1.

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Rana, Chhavi, and Sanjay Kumar Jain. "A Recommendation Model for Handling Dynamics in User Profile." In Distributed Computing and Internet Technology, 231–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28073-3_20.

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Mikhailov, V. V., Alexandr V. Spesivtsev, and Andrey Yu Perevaryukha. "Evaluation of the Dynamics of Phytomass in the Tundra Zone Using a Fuzzy-Opportunity Approach." In Intelligent Distributed Computing XIII, 449–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32258-8_53.

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Boryczko, K., J. Kitowski, and J. Mościński. "Load-balancing procedure for distributed short-range molecular dynamics." In Parallel Scientific Computing, 100–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/bfb0030140.

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Pospichal, Jiri. "Migration and Population Dynamics in Distributed Coevolutionary Algorithm." In Soft Computing in Industrial Applications, 265–80. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0509-1_22.

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He, Kaikai, and Yan Chen. "Urban Traffic Congestion Based on System Dynamics: Taking Wuhan City as an Example." In Internet and Distributed Computing Systems, 372–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45940-0_34.

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Kitowski, J. "Distributed and parallel computing of short-range molecular dynamics." In Lecture Notes in Computer Science, 345–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-60902-4_37.

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Bubak, M., J. Mościński, M. Pogoda, and W. Zdechlikiewicz. "Parallel distributed 2-D short-range molecular dynamics on networked workstations." In Parallel Scientific Computing, 127–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/bfb0030142.

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Conference papers on the topic "Dynamics, Distributed Computing"

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Heyn, Toby, Andrew Seidl, Hammad Mazhar, David Lamb, Alessandro Tasora, and Dan Negrut. "Enabling Computational Dynamics in Distributed Computing Environments Using a Heterogeneous Computing Template." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48347.

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This paper describes a software infrastructure made up of tools and libraries designed to assist developers in implementing computational dynamics applications running on heterogeneous and distributed computing environments. Together, these tools and libraries compose a so called Heterogeneous Computing Template (HCT). The heterogeneous and distributed computing hardware infrastructure is assumed herein to be made up of a combination of CPUs and GPUs. The computational dynamics applications targeted to execute on such a hardware topology include many-body dynamics, smoothed-particle hydrodynamics (SPH) fluid simulation, and fluid-solid interaction analysis. The underlying theme of the solution approach embraced by HCT is that of partitioning the domain of interest into a number of sub-domains that are each managed by a separate core/accelerator (CPU/GPU) pair. Five components at the core of HCT enable the envisioned distributed computing approach to large-scale dynamical system simulation: (a) a method for the geometric domain decomposition and mapping onto heterogeneous hardware; (b) methods for proximity computation or collision detection; (c) support for moving data among the corresponding hardware as elements move from subdomain to subdomain; (d) numerical methods for solving the specific dynamics problem of interest; and (e) tools for performing visualization and post-processing in a distributed manner. In this contribution the components (a) and (c) of the HCT are demonstrated via the example of the Discrete Element Method (DEM) for rigid body dynamics with friction and contact. The collision detection task required in frictional-contact dynamics; i.e., task (b) above, is discussed separately and in the context of GPU computing. This task is shown to benefit of a two order of magnitude gain in efficiency when compared to traditional sequential implementations. Note: Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not imply its endorsement, recommendation, or favoring by the US Army. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Army, and shall not be used for advertising or product endorsement purposes.
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Wang, Kung-Juin, Ming-Chieh Chuang, Chao-Hsien Li, and Keh-Chyuan Tsai. "A DISTRIBUTED COMPUTING PLATFORM FOR CONVENTIONAL HYBRID SIMULATION." In 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering Methods in Structural Dynamics and Earthquake Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2019. http://dx.doi.org/10.7712/120119.7052.18910.

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Lee, Bo-Sung, Dong Lee, Bo-Sung Lee, and Dong Lee. "Data parallel symmetric Gauss-Seidel algorithm for efficient distributed computing using massively parallel supercomputers." In 13th Computational Fluid Dynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-2138.

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Celis, L. Elisa, Peter M. Krafft, and Nisheeth K. Vishnoi. "A Distributed Learning Dynamics in Social Groups." In PODC '17: ACM Symposium on Principles of Distributed Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3087801.3087820.

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Alistarh, Dan, Martin Töpfer, and Przemysław Uznański. "Comparison Dynamics in Population Protocols." In PODC '21: ACM Symposium on Principles of Distributed Computing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3465084.3467915.

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Sarwate, Anand D., and Tara Javidi. "Opinion dynamics and distributed learning of distributions." In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2011. http://dx.doi.org/10.1109/allerton.2011.6120297.

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Guodong Shi, Alexandre Proutiere, and Karl Henrik Johansson. "Continuous-time distributed optimization of homogenous dynamics." In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2013. http://dx.doi.org/10.1109/allerton.2013.6736569.

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Tang, Jing, Bin Li, Jiangtao Chen, and Xiaoquan Gong. "Large Scale Parallel Computing for Fluid Dynamics on Unstructured Grid." In 2016 15th International Symposium on Parallel and Distributed Computing (ISPDC). IEEE, 2016. http://dx.doi.org/10.1109/ispdc.2016.17.

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Yan, Guanhua, and Stephan Eidenbenz. "Modeling Propagation Dynamics of Bluetooth Worms." In 27th International Conference on Distributed Computing Systems (ICDCS '07). IEEE, 2007. http://dx.doi.org/10.1109/icdcs.2007.121.

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Kim, Seung Jo, Chang Sung Lee, and Ji Duck Choi. "Finite Element Analysis by Piggyback Concept in Distributed Computing Environment." In 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-1218.

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Reports on the topic "Dynamics, Distributed Computing"

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Murphy, S. P. The NOSC (Naval Ocean Systems Center) Code 911 Digital Dynamics Processor (DDP). A Mildly Coupled Distributed-Computing System. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada210148.

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