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Добірка наукової літератури з теми "High performance scientific computing"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "High performance scientific computing"

Camp, William J., and Philippe Thierry. "Trends for high-performance scientific computing." Leading Edge 29, no. 1 (2010): 44–47. http://dx.doi.org/10.1190/1.3284052.

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Kisel, Ivan. "Scientific and high-performance computing at FAIR." EPJ Web of Conferences 95 (2015): 01007. http://dx.doi.org/10.1051/epjconf/20159501007.

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Fosdick, Lloyd D., Elizabeth R. Jessup, Carolyn J. C. Schauble, Gitta Domik, and Ralph L. Place. "An Introduction to High‐Performance Scientific Computing." Physics Today 49, no. 12 (1996): 55–56. http://dx.doi.org/10.1063/1.881590.

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Biryaltsev, Eugeniy Vasiljevich, Marat Razifovich Galimov, Denis Evgenievich Demidov, and Aleksandr Mikhailovich Elizarov. "The platform approach to research and development using high-performance computing." Program Systems: Theory and Applications 10, no. 2 (2019): 93–119. http://dx.doi.org/10.25209/2079-3316-2019-10-2-93-119.

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In this paper, we analyze the prerequisites and substantiate the relevance for creating an open Internet platform that employs big data technologies, highperformance computing, and multilateral markets in a unified way. Conceived as an ecosystem for the development and use of applied software (including in the field of design and scientific research), the platform should reduce time/costs and improve the quality of software development for solving analytical problems arising in industrial enterprises, scientific research organizations, state bodies and private individuals. The article presents a working prototype of the platform using supercomputer technologies and desktop virtualization systems.

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Bernholdt, David E., Benjamin A. Allan, Robert Armstrong, et al. "A Component Architecture for High-Performance Scientific Computing." International Journal of High Performance Computing Applications 20, no. 2 (2006): 163–202. http://dx.doi.org/10.1177/1094342006064488.

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Kurzak, Jakub, Alfredo Buttari, Piotr Luszczek, and Jack Dongarra. "The PlayStation 3 for High-Performance Scientific Computing." Computing in Science & Engineering 10, no. 3 (2008): 84–87. http://dx.doi.org/10.1109/mcse.2008.85.

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Alexeev, Yuri, Benjamin A. Allan, Robert C. Armstrong, et al. "Component-based software for high-performance scientific computing." Journal of Physics: Conference Series 16 (January 1, 2005): 536–40. http://dx.doi.org/10.1088/1742-6596/16/1/073.

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Davis, Kei, and Jöerg Striegnitz. "Parallel/High Performance Object-Oriented Scientific Computing 2008." International Journal of Parallel, Emergent and Distributed Systems 24, no. 6 (2009): 463–65. http://dx.doi.org/10.1080/17445760902758529.

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Ponce, Marcelo, Erik Spence, Ramses van Zon, and Daniel Gruner. "Scientific Computing, High-Performance Computing and Data Science in Higher Education." Journal of Computational Science Education 10, no. 1 (2019): 24–31. http://dx.doi.org/10.22369/issn.2153-4136/10/1/5.

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Boulle, A., and J. Kieffer. "High-performance Python for crystallographic computing." Journal of Applied Crystallography 52, no. 4 (2019): 882–97. http://dx.doi.org/10.1107/s1600576719008471.

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The Python programming language, combined with the numerical computing library NumPy and the scientific computing library SciPy, has become the de facto standard for scientific computing in a variety of fields. This popularity is mainly due to the ease with which a Python program can be written and executed (easy syntax, dynamical typing, no compilation etc.), coupled with the existence of a large number of specialized third-party libraries that aim to lift all the limitations of the raw Python language. NumPy introduces vector programming, improving execution speeds, whereas SciPy brings a wealth of highly optimized and reliable scientific functions. There are cases, however, where vector programming alone is not sufficient to reach optimal performance. This issue is addressed with dedicated compilers that aim to translate Python code into native and statically typed code with support for the multi-core architectures of modern processors. In the present article it is shown how these approaches can be efficiently used to tackle different problems, with increasing complexity, that are relevant to crystallography: the 2D Laue function, scattering from a strained 2D crystal, scattering from 3D nanocrystals and, finally, diffraction from films and multilayers. For each case, detailed implementations and explanations of the functioning of the algorithms are provided. Different Python compilers (namely NumExpr, Numba, Pythran and Cython) are used to improve performance and are benchmarked against state-of-the-art NumPy implementations. All examples are also provided as commented and didactic Python (Jupyter) notebooks that can be used as starting points for crystallographers curious to enter the Python ecosystem or wishing to accelerate their existing codes.

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Дисертації з теми "High performance scientific computing"

Balakrishnan, Suresh Reuben A/L. "Hybrid High Performance Computing (HPC) + Cloud for Scientific Computing." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/89123.

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The HPC+Cloud framework has been built to enable on-premise HPC jobs to use resources from cloud computing nodes. As part of designing the software framework, public cloud providers, namely Amazon AWS, Microsoft Azure and NeCTAR were benchmarked against one another, and Microsoft Azure was determined to be the most suitable cloud component in the proposed HPC+Cloud software framework. Finally, an HPC+Cloud cluster was built using the HPC+Cloud software framework and then was validated by conducting HPC processing benchmarks.

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Bentz, Jonathan Lee. "Hybrid programming in high performance scientific computing." [Ames, Iowa : Iowa State University], 2006.

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Calatrava, Arroyo Amanda. "High Performance Scientific Computing over Hybrid Cloud Platforms." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/75265.

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Scientific applications generally require large computational requirements, memory and data management for their execution. Such applications have traditionally used high-performance resources, such as shared memory supercomputers, clusters of PCs with distributed memory, or resources from Grid infrastructures on which the application needs to be adapted to run successfully. In recent years, the advent of virtualization techniques, together with the emergence of Cloud Computing, has caused a major shift in the way these applications are executed. However, the execution management of scientific applications on high performance elastic platforms is not a trivial task. In this doctoral thesis, Elastic Cloud Computing Cluster (EC3) has been developed. EC3 is an open-source tool able to execute high performance scientific applications by creating self-managed cost-efficient virtual hybrid elastic clusters on top of IaaS Clouds. These self-managed clusters have the capability to adapt the size of the cluster, i.e. the number of nodes, to the workload, thus creating the illusion of a real cluster without requiring an investment beyond the actual usage. They can be fully customized and migrated from one provider to another, in an automatically and transparent process for the users and jobs running in the cluster. EC3 can also deploy hybrid clusters across on-premises and public Cloud resources, where on-premises resources are supplemented with public Cloud resources to accelerate the execution process. Different instance types and the use of spot instances combined with on-demand resources are also cluster configurations supported by EC3. Moreover, using spot instances, together with checkpointing techniques, the tool can significantly reduce the total cost of executions while introducing automatic fault tolerance. EC3 is conceived to facilitate the use of virtual clusters to users, that might not have an extensive knowledge about these technologies, but they can benefit from them. Thus, the tool offers two different interfaces for its users, a web interface where EC3 is exposed as a service for non-experienced users and a powerful command line interface. Moreover, this thesis explores the field of light-weight virtualization using containers as an alternative to the traditional virtualization solution based on virtual machines. This study analyzes the suitable scenario for the use of containers and proposes an architecture for the deployment of elastic virtual clusters based on this technology. Finally, to demonstrate the functionality and advantages of the tools developed during this thesis, this document includes several use cases covering different scenarios and fields of knowledge, such as structural analysis of buildings, astrophysics or biodiversity. Las aplicaciones científicas generalmente precisan grandes requisitos de cómputo, memoria y gestión de datos para su ejecución. Este tipo de aplicaciones tradicionalmente ha empleado recursos de altas prestaciones, como supercomputadores de memoria compartida, clústers de PCs de memoria distribuida, o recursos provenientes de infraestructuras Grid, sobre los que se adaptaba la aplicación para que se ejecutara satisfactoriamente. El auge que han tenido las técnicas de virtualización en los últimos años, propiciando la aparición de la computación en la nube (Cloud Computing), ha provocado un importante cambio en la forma de ejecutar este tipo de aplicaciones. Sin embargo, la gestión de la ejecución de aplicaciones científicas sobre plataformas de computación elásticas de altas prestaciones no es una tarea trivial. En esta tesis doctoral se ha desarrollado Elastic Cloud Computing Cluster (EC3), una herramienta de código abierto capaz de llevar a cabo la ejecución de aplicaciones científicas de altas prestaciones creando para ello clústers virtuales, híbridos y elásticos, autogestionados y eficientes en cuanto a costes, sobre plataformas Cloud de tipo Infraestructura como Servicio (IaaS). Estos clústers autogestionados tienen la capacidad de adaptar su tamaño, es decir, el número de nodos, a la carga de trabajo, creando así la ilusión de un clúster real sin requerir una inversión por encima del uso actual. Además, son completamente configurables y pueden ser migrados de un proveedor a otro de manera automática y transparente a los usuarios y trabajos en ejecución en el cluster. EC3 también permite desplegar clústers híbridos sobre recursos Cloud públicos y privados, donde los recursos privados son complementados con recursos Cloud públicos para acelerar el proceso de ejecución. Otras configuraciones híbridas, como el empleo de diferentes tipos de instancias y el uso de instancias puntuales combinado con instancias bajo demanda son también soportadas por EC3. Además, el uso de instancias puntuales junto con técnicas de checkpointing permite a EC3 reducir significantemente el coste total de las ejecuciones a la vez que proporciona tolerancia a fallos. EC3 está concebido para facilitar el uso de clústers virtuales a los usuarios, que, aunque no tengan un conocimiento extenso sobre este tipo de tecnologías, pueden beneficiarse fácilmente de ellas. Por ello, la herramienta ofrece dos interfaces diferentes a sus usuarios, una interfaz web donde se expone EC3 como servicio para usuarios no experimentados y una potente interfaz de línea de comandos. Además, esta tesis doctoral se adentra en el campo de la virtualización ligera, mediante el uso de contenedores como alternativa a la solución tradicional de virtualización basada en máquinas virtuales. Este estudio analiza el escenario propicio para el uso de contenedores y propone una arquitectura para el despliegue de clusters virtuales elásticos basados en esta tecnología. Finalmente, para demostrar la funcionalidad y ventajas de las herramientas desarrolladas durante esta tesis, esta memoria recoge varios casos de uso que abarcan diferentes escenarios y campos de conocimiento, como estudios estructurales de edificios, astrofísica o biodiversidad. Les aplicacions científiques generalment precisen grans requisits de còmput, de memòria i de gestió de dades per a la seua execució. Este tipus d'aplicacions tradicionalment hi ha empleat recursos d'altes prestacions, com supercomputadors de memòria compartida, clústers de PCs de memòria distribuïda, o recursos provinents d'infraestructures Grid, sobre els quals s'adaptava l'aplicació perquè s'executara satisfactòriament. L'auge que han tingut les tècniques de virtualitzaciò en els últims anys, propiciant l'aparició de la computació en el núvol (Cloud Computing), ha provocat un important canvi en la forma d'executar este tipus d'aplicacions. No obstant això, la gestió de l'execució d'aplicacions científiques sobre plataformes de computació elàstiques d'altes prestacions no és una tasca trivial. En esta tesi doctoral s'ha desenvolupat Elastic Cloud Computing Cluster (EC3), una ferramenta de codi lliure capaç de dur a terme l'execució d'aplicacions científiques d'altes prestacions creant per a això clústers virtuals, híbrids i elàstics, autogestionats i eficients quant a costos, sobre plataformes Cloud de tipus Infraestructura com a Servici (IaaS). Estos clústers autogestionats tenen la capacitat d'adaptar la seua grandària, es dir, el nombre de nodes, a la càrrega de treball, creant així la il·lusió d'un cluster real sense requerir una inversió per damunt de l'ús actual. A més, són completament configurables i poden ser migrats d'un proveïdor a un altre de forma automàtica i transparent als usuaris i treballs en execució en el cluster. EC3 també permet desplegar clústers híbrids sobre recursos Cloud públics i privats, on els recursos privats són complementats amb recursos Cloud públics per a accelerar el procés d'execució. Altres configuracions híbrides, com l'us de diferents tipus d'instàncies i l'ús d'instàncies puntuals combinat amb instàncies baix demanda són també suportades per EC3. A més, l'ús d'instàncies puntuals junt amb tècniques de checkpointing permet a EC3 reduir significantment el cost total de les execucions al mateix temps que proporciona tolerància a fallades. EC3e stà concebut per a facilitar l'ús de clústers virtuals als usuaris, que, encara que no tinguen un coneixement extensiu sobre este tipus de tecnologies, poden beneficiar-se fàcilment d'elles. Per això, la ferramenta oferix dos interfícies diferents dels seus usuaris, una interfície web on s'exposa EC3 com a servici per a usuaris no experimentats i una potent interfície de línia d'ordres. A més, esta tesi doctoral s'endinsa en el camp de la virtualitzaciò lleugera, per mitjà de l'ús de contenidors com a alternativa a la solució tradicional de virtualitzaciò basada en màquines virtuals. Este estudi analitza l'escenari propici per a l'ús de contenidors i proposa una arquitectura per al desplegament de clusters virtuals elàstics basats en esta tecnologia. Finalment, per a demostrar la funcionalitat i avantatges de les ferramentes desenrotllades durant esta tesi, esta memòria arreplega diversos casos d'ús que comprenen diferents escenaris i camps de coneixement, com a estudis estructurals d'edificis, astrofísica o biodiversitat. Calatrava Arroyo, A. (2016). High Performance Scientific Computing over Hybrid Cloud Platforms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/75265 TESIS

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Agarwal, Dinesh. "Scientific High Performance Computing (HPC) Applications On The Azure Cloud Platform." Digital Archive @ GSU, 2013. http://digitalarchive.gsu.edu/cs_diss/75.

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Cloud computing is emerging as a promising platform for compute and data intensive scientific applications. Thanks to the on-demand elastic provisioning capabilities, cloud computing has instigated curiosity among researchers from a wide range of disciplines. However, even though many vendors have rolled out their commercial cloud infrastructures, the service offerings are usually only best-effort based without any performance guarantees. Utilization of these resources will be questionable if it can not meet the performance expectations of deployed applications. Additionally, the lack of the familiar development tools hamper the productivity of eScience developers to write robust scientific high performance computing (HPC) applications. There are no standard frameworks that are currently supported by any large set of vendors offering cloud computing services. Consequently, the application portability among different cloud platforms for scientific applications is hard. Among all clouds, the emerging Azure cloud from Microsoft in particular remains a challenge for HPC program development both due to lack of its support for traditional parallel programming support such as Message Passing Interface (MPI) and map-reduce and due to its evolving application programming interfaces (APIs). We have designed newer frameworks and runtime environments to help HPC application developers by providing them with easy to use tools similar to those known from traditional parallel and distributed computing environment set- ting, such as MPI, for scientific application development on the Azure cloud platform. It is challenging to create an efficient framework for any cloud platform, including the Windows Azure platform, as they are mostly offered to users as a black-box with a set of application programming interfaces (APIs) to access various service components. The primary contributions of this Ph.D. thesis are (i) creating a generic framework for bag-of-tasks HPC applications to serve as the basic building block for application development on the Azure cloud platform, (ii) creating a set of APIs for HPC application development over the Azure cloud platform, which is similar to message passing interface (MPI) from traditional parallel and distributed setting, and (iii) implementing Crayons using the proposed APIs as the first end-to-end parallel scientific application to parallelize the fundamental GIS operations.

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Gulabani, Teena Pratap. "Development of high performance scientific components for interoperability of computing packages." [Ames, Iowa : Iowa State University], 2008.

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Kaplan, Ali. "Collaborative framework for high-performance p2p-based data transfer in scientific computing." [Bloomington, Ind.] : Indiana University, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3380091.

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Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2009. Title from PDF t.p. (viewed on Jul 19, 2010). Source: Dissertation Abstracts International, Volume: 70-12, Section: B, page: 7668. Adviser: Geoffrey C. Fox.

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Steven, Monteiro Steena Dominica. "Statistical Techniques to Model and Optimize Performance of Scientific, Numerically Intensive Workloads." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5228.

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Projecting performance of applications and hardware is important to several market segments—hardware designers, software developers, supercomputing centers, and end users. Hardware designers estimate performance of current applications on future systems when designing new hardware. Software developers make performance estimates to evaluate performance of their code on different architectures and input datasets. Supercomputing centers try to optimize the process of matching computing resources to computing needs. End users requesting time on supercomputers must provide estimates of their application’s run time, and incorrect estimates can lead to wasted supercomputing resources and time. However, application performance is challenging to predict because it is affected by several factors in application code, specifications of system hardware, choice of compilers, compiler flags, and libraries. This dissertation uses statistical techniques to model and optimize performance of scientific applications across different computer processors. The first study in this research offers statistical models that predict performance of an application across different input datasets prior to application execution. These models guide end users to select parameters that produce optimal application performance during execution. The second study offers a suite of statistical models that predict performance of a new application on a new processor. Both studies present statistical techniques that can be generalized to analyze, optimize, and predict performance of diverse computation- and data-intensive applications on different hardware.

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Lin, Tien-Ju. "Web-based front-end design and scientific computing for material stress simulation software." Thesis, Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53101.

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A precise simulation requires a large amount of input data such as geometrical descriptions of the crystal structure, the external forces and loads, and quantitative properties of the material. Although some powerful applications already exist for research purposes, they are not widely used in education due to complex structure and unintuitive operation. To cater to the generic user base, a front-end application for material simulation software is introduced. With a graphic interface, it provides a more efficient way to conduct the simulation and to educate students who want to enlarge knowledge in relevant fields. We first discuss how we explore the solution for the front-end application and how to develop it on top of the material simulation software developed by mechanical engineering lab from Georgia Tech Lorraine. The user interface design, the functionality and the whole user experience are primary factors determining the product success or failure. This material simulation software helps researchers resolve the motion and the interactions of a large ensemble of dislocations for single or multi-layered 3D materials. However, the algorithm it utilizes is not well optimized and parallelized, so its performance of speedup cannot scale when using more CPUs in the cluster. This problem leads to the second topic on scientific computing, so in this thesis we offer different approaches that attempt to improve the parallelization and optimize the scalability.

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Krishnan, Manoj Kumar. "ProLAS a novel dynamic load balancing library for advanced scientific computing /." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-11102003-184622.

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Malenta, Mateusz. "Exploring the dynamic radio sky with many-core high-performance computing." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/exploring-the-dynamic-radio-sky-with-manycore-highperformance-computing(fe86c963-e253-48c0-a907-f8b59c44cf53).html.

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As new radio telescopes and processing facilities are being built, the amount of data that has to be processed is growing continuously. This poses significant challenges, especially if the real-time processing is required, which is important for surveys looking for poorly understood objects, such as Fast Radio Bursts, where quick detection and localisation can enable rapid follow-up observations at different frequencies. With the data rates increasing all the time, new processing techniques using the newest hardware, such as GPUs, have to be developed. A new pipeline, called PAFINDER, has been developed to process data taken with a phased array feed, which can generate up to 36 beams on the sky, with data rates of 25 GBps per beam. With the majority of work done on GPUs, the pipeline reaches real-time performance when generating filterbank files used for offline processing. The full real-time processing, including single-pulse searches has also been implemented and has been shown to perform well under favourable conditions. The pipeline was successfully used to record and process data containing observations of RRAT J1819-1458 and positions on the sky where 3 FRBs have been observed previously, including the repeating FRB121102. Detailed examination of J1819-1458 single-pulse detections revealed a complex emission environment with pulses coming from three different rotation phase bands and a number of multi-component emissions. No new FRBs and no repeated bursts from FRB121102 have been detected. The GMRT High Resolution Southern Sky survey observes the sky at high galactic latitudes, searching for new pulsars and FRBs. 127 hours of data have been searched for the presence of any new bursts, with the help of new pipeline developed for this survey. No new FRBs have been found, which can be the result of bad RFI pollution, which was not fully removed despite new techniques being developed and combined with the existing solutions to mitigate these negative effects. Using the best estimates on the total amount of data that has been processed correctly, obtained using new single-pulse simulation software, no detections were found to be consistent with the expected rates for standard candle FRBs with a flat or positive spectrum.

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Книги з теми "High performance scientific computing"

Di Napoli, Edoardo, Marc-André Hermanns, Hristo Iliev, Andreas Lintermann, and Alexander Peyser, eds. High-Performance Scientific Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53862-4.

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Berry, Michael W., Kyle A. Gallivan, Efstratios Gallopoulos, et al., eds. High-Performance Scientific Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2437-5.

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Breuer, Michael, Franz Durst, and Christoph Zenger, eds. High Performance Scientific And Engineering Computing. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-55919-8.

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Bungartz, Hans-Joachim, Franz Durst, and Christoph Zenger, eds. High Performance Scientific and Engineering Computing. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60155-2.

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Yang, Laurence Tianruo, and Yi Pan, eds. High Performance Scientific and Engineering Computing. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-5402-5.

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Chopp, David L. Introduction to High Performance Scientific Computing. Society for Industrial and Applied Mathematics, 2019. http://dx.doi.org/10.1137/1.9781611975642.

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A, Gallivan Kyle, Gallopoulos Efstratios, Grama Ananth, et al., eds. High-Performance Scientific Computing: Algorithms and Applications. Springer London, 2012.

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An Introduction to high-performance scientific computing. MIT Press, 1996.

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Gentzsch, Wolfgang. High speed and large scale scientific computing. IOS Press, 2009.

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Gentzsch, Wolfgang. High speed and large scale scientific computing. IOS Press, 2009.

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Частини книг з теми "High performance scientific computing"

Jalby, William, David C. Wong, David J. Kuck, Jean-Thomas Acquaviva, and Jean-Christophe Beyler. "Measuring Computer Performance." In High-Performance Scientific Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2437-5_3.

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Amman, H. M. "High Performance Computing in Economics." In Scientific Computing on Supercomputers. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0819-5_12.

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McBryan, Oliver A. "Limiting factors in high performance computing." In Parallel Scientific Computing. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/bfb0030165.

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Gallivan, Kyle A., Efstratios Gallopoulos, Ananth Grama, et al. "Parallel Numerical Computing from Illiac IV to Exascale—The Contributions of Ahmed H. Sameh." In High-Performance Scientific Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2437-5_1.

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Xia, Jianlin. "Robust and Efficient Multifrontal Solver for Large Discretized PDEs." In High-Performance Scientific Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2437-5_10.

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Baggag, Abdelkader. "A Preconditioned Scheme for Nonsymmetric Saddle-Point Problems." In High-Performance Scientific Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2437-5_11.

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Kilic, Sami A. "Effect of Ordering for Iterative Solvers in Structural Mechanics Problems." In High-Performance Scientific Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2437-5_12.

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Baker, Allison H., Robert D. Falgout, Tzanio V. Kolev, and Ulrike Meier Yang. "Scaling Hypre’s Multigrid Solvers to 100,000 Cores." In High-Performance Scientific Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2437-5_13.

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Gallivan, Kyle A., Chunhong Qi, and P. A. Absil. "A Riemannian Dennis-Moré Condition." In High-Performance Scientific Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2437-5_14.

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Wang, Mu, and Xiaoge Wang. "A Jump-Start of Non-negative Least Squares Solvers." In High-Performance Scientific Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2437-5_15.

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Тези доповідей конференцій з теми "High performance scientific computing"

Kenyon, Connor, and Collin Capano. "Apple Silicon Performance in Scientific Computing." In 2022 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2022. http://dx.doi.org/10.1109/hpec55821.2022.9926315.

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Hazelhurst, Scott. "Scientific computing using virtual high-performance computing." In the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists. ACM Press, 2008. http://dx.doi.org/10.1145/1456659.1456671.

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Noack, Matthias. "OpenCL in Scientific High Performance Computing." In IWOCL 2017: 5th International Workshop on OpenCL. ACM, 2017. http://dx.doi.org/10.1145/3078155.3078170.

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Butler, David M. "Scientific Computing Doesn't Need noSQL." In 2012 SC Companion: High Performance Computing, Networking, Storage and Analysis (SCC). IEEE, 2012. http://dx.doi.org/10.1109/sc.companion.2012.158.

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Higgins, Joshua, Violeta Holmes, and Colin Venters. "Securing user defined containers for scientific computing." In 2016 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2016. http://dx.doi.org/10.1109/hpcsim.2016.7568369.

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Volkema, Glenn, and Gaurav Khanna. "Scientific computing using consumer video-gaming embedded devices." In 2017 IEEE High-Performance Extreme Computing Conference (HPEC). IEEE, 2017. http://dx.doi.org/10.1109/hpec.2017.8091055.

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Kenyon, Connor, Glenn Volkema, and Gaurav Khanna. "Overcoming Limitations of GPGPU-Computing in Scientific Applications." In 2019 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2019. http://dx.doi.org/10.1109/hpec.2019.8916330.

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Colonnelli, I., and M. Aldinucci. "HPC07 - Hybrid Workflows For Large - Scale Scientific Applications." In Sixth EAGE High Performance Computing Workshop. European Association of Geoscientists & Engineers, 2022. http://dx.doi.org/10.3997/2214-4609.2022615029.

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Shirun Ho, S. Itoh, S. Ihara, and R. D. Schlichting. "Agent Middleware for Heterogeneous Scientific Simulations." In SC98 - High Performance Networking and Computing Conference. IEEE, 1998. http://dx.doi.org/10.1109/sc.1998.10014.

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Bassetti, F., D. Brown, K. Davis, W. Henshaw, and Dan Quinlan. "OVERTURE: An Object-Oriented Framework for High Performance Scientific Computing." In SC98 - High Performance Networking and Computing Conference. IEEE, 1998. http://dx.doi.org/10.1109/sc.1998.10013.

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Звіти організацій з теми "High performance scientific computing"

Jin, Yier. Resilient and Robust High Performance Computing Platforms for Scientific Computing Integrity. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1393914.

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Gulabani, Teena Pratap. Development of high performance scientific components for interoperability of computing packages. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/964389.

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Antypas, Katie, Jeffrey Broughton, Shane Canon, et al. NERSC 2011: High Performance Computing Facility Operational Assessment for the National Energy Research Scientific Computing Center. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1183198.

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Bergman, Keren, Tom Conte, Al Gara, et al. Future High Performance Computing Capabilities: Summary Report of the Advanced Scientific Computing Advisory Committee (ASCAC) Subcommittee. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1570693.

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Khaleel, Mohammad A. Scientific Grand Challenges: Forefront Questions in Nuclear Science and the Role of High Performance Computing. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/968204.

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Gerber, Richard, William Allcock, Chris Beggio, et al. DOE High Performance Computing Operational Review (HPCOR): Enabling Data-Driven Scientific Discovery at HPC Facilities. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1163236.

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Kendall, Richard P., Douglass E. Post, Jeffrey C. Carver, Dale B. Henderson, and David A. Fisher. A Proposed Taxonomy for Software Development Risks for High-Performance Computing (HPC) Scientific/Engineering Applications. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada468594.

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Hittinger, J. LLNL Response to the DOE ASCR RFI, "Stewardship of Software for Scientific and High-Performance Computing". Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1835687.

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Finkel, Hal, Ben Brown, Robinson Pino, Saswata Hier-Majumder, and Bill Spotz. Responses to the Request for Information on Stewardship of Software for Scientific and High-Performance Computing. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1843576.

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Rangaswami, Raju. Department of Energy Project ER25739 Final Report QoS-Enabled, High-performance Storage Systems for Data-Intensive Scientific Computing. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1046919.

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