Relevant bibliographies by topics / High performance scientific computing

Contents

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

Academic literature on the topic 'High performance scientific computing'

Author: Grafiati
Published: 10 December 2022
Last updated: 31 July 2025

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Journal articles on the topic "High performance scientific computing"

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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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Avi Trivedi. "High-Performance Parallel Computing for Scientific Simulations." Universal Research Reports 11, no. 4 (2024): 146–256. http://dx.doi.org/10.36676/urr.v11.i4.1353.

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One essential strategy for overcoming the difficulties of contemporary scientific simulations is High-Performance Parallel Computing, or HPPC. In order to handle massive information and solve complicated equations, these simulations—which mimic complex phenomena like weather patterns, fluid dynamics, and biological systems—require enormous processing resources. By dividing tasks into smaller components and processing them concurrently, HPPC enables scientists to answer issues more quickly and precisely. Research in several disciplines, including biology, environmental science, engineering, and
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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
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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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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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Dissertations / Theses on the topic "High performance scientific computing"

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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
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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 f
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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.<br>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 applic
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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 knowled
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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
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Books on the topic "High performance scientific computing"

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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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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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Milutinović, Veljko, Marijana Despotović-Zrakić, and Aleksandar Belić. Handbook of research on high performance and cloud computing in scientific research and education. Information Science Reference, 2014.

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Book chapters on the topic "High performance scientific computing"

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Brieda, Lubos, Joseph Wang, and Robert Martin. "High-Performance Computing." In Introduction to Modern Scientific Programming and Numerical Methods. CRC Press, 2024. http://dx.doi.org/10.1201/9781003132233-9.

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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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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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Conference papers on the topic "High performance scientific computing"

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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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Reports on the topic "High performance scientific computing"

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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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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
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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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