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Journal articles on the topic 'High performance scientific computing'

Author: Grafiati

Published: 10 December 2022

Last updated: 19 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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10

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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11

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 we

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12

Coveney, Peter V. "Scientific Grid computing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1833 (2005): 1707–13. http://dx.doi.org/10.1098/rsta.2005.1632.

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We introduce a definition of Grid computing which is adhered to throughout this Theme Issue. We compare the evolution of the World Wide Web with current aspirations for Grid computing and indicate areas that need further research and development before a generally usable Grid infrastructure becomes available. We discuss work that has been done in order to make scientific Grid computing a viable proposition, including the building of Grids, middleware developments, computational steering and visualization. We review science that has been enabled by contemporary computational Grids, and associat

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13

Cohen, Jeremy, Ioannis Filippis, Mark Woodbridge, et al. "RAPPORT: running scientific high-performance computing applications on the cloud." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1983 (2013): 20120073. http://dx.doi.org/10.1098/rsta.2012.0073.

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Cloud computing infrastructure is now widely used in many domains, but one area where there has been more limited adoption is research computing, in particular for running scientific high-performance computing (HPC) software. The Robust Application Porting for HPC in the Cloud (RAPPORT) project took advantage of existing links between computing researchers and application scientists in the fields of bioinformatics, high-energy physics (HEP) and digital humanities, to investigate running a set of scientific HPC applications from these domains on cloud infrastructure. In this paper, we focus on

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14

Ware, Colin, David Rogers, Mark Petersen, James Ahrens, and Erol Aygar. "Optimizing for Visual Cognition in High Performance Scientific Computing." Electronic Imaging 2016, no. 16 (2016): 1–9. http://dx.doi.org/10.2352/issn.2470-1173.2016.16.hvei-130.

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15

Barba, Lorena A., Andreas Klockner, Prabhu Ramachandran, and Rollin Thomas. "Scientific Computing With Python on High-Performance Heterogeneous Systems." Computing in Science & Engineering 23, no. 4 (2021): 5–7. http://dx.doi.org/10.1109/mcse.2021.3088549.

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16

Epperly, Thomas GW, Gary Kumfert, Tamara Dahlgren, et al. "High-performance language interoperability for scientific computing through Babel." International Journal of High Performance Computing Applications 26, no. 3 (2011): 260–74. http://dx.doi.org/10.1177/1094342011414036.

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17

Schuster, Micah D. "The Heat Equation: High-Performance Scientific Computing Case Study." Computing in Science & Engineering 20, no. 5 (2018): 114–27. http://dx.doi.org/10.1109/mcse.2018.05329820.

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18

Paprzycki, M. "An Introduction To High-performance Scientific Computing [Book Review]." IEEE Concurrency 5, no. 3 (1997): 73–74. http://dx.doi.org/10.1109/mcc.1997.605921.

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19

Di Gregorio, S., R. Rongo, W. Spataro, G. Spezzano, and D. Talia. "High performance scientific computing by a parallel cellular environment." Future Generation Computer Systems 12, no. 5 (1997): 357–69. http://dx.doi.org/10.1016/s0167-739x(96)00023-4.

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20

Nielsen, Ida M. B., and Curtis L. Janssen. "Multicore Challenges and Benefits for High Performance Scientific Computing." Scientific Programming 16, no. 4 (2008): 277–85. http://dx.doi.org/10.1155/2008/450818.

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Until recently, performance gains in processors were achieved largely by improvements in clock speeds and instruction level parallelism. Thus, applications could obtain performance increases with relatively minor changes by upgrading to the latest generation of computing hardware. Currently, however, processor performance improvements are realized by using multicore technology and hardware support for multiple threads within each core, and taking full advantage of this technology to improve the performance of applications requires exposure of extreme levels of software parallelism. We will her

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21

Cameron, K. W., Rong Ge, and Xizhou Feng. "High-performance, power-aware distributed computing for scientific applications." Computer 38, no. 11 (2005): 40–47. http://dx.doi.org/10.1109/mc.2005.380.

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22

Haney, S., and J. Crotlinger. "How templates enable high-performance scientific computing in C++." Computing in Science & Engineering 1, no. 4 (1999): 66–72. http://dx.doi.org/10.1109/5992.774843.

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23

Armstrong, Rob, Gary Kumfert, Lois Curfman McInnes, et al. "The CCA component model for high-performance scientific computing." Concurrency and Computation: Practice and Experience 18, no. 2 (2005): 215–29. http://dx.doi.org/10.1002/cpe.911.

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24

Kritz, Mauricio Vieira. "Meeting report-colloquia on high performance scientific computing 1996." Complexity 2, no. 3 (1997): 3–4. http://dx.doi.org/10.1002/(sici)1099-0526(199701/02)2:3<3::aid-cplx1>3.0.co;2-c.

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25

Grannan, A., K. Sood, B. Norris, and A. Dubey. "Understanding the landscape of scientific software used on high-performance computing platforms." International Journal of High Performance Computing Applications 34, no. 4 (2020): 465–77. http://dx.doi.org/10.1177/1094342019899451.

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Scientific discovery increasingly relies on computation through simulations, analytics, and machine and deep learning. Of these, simulations on high-performance computing (HPC) platforms have been the cornerstone of scientific computing for more than two decades. However, the development of simulation software has, in general, occurred through accretion, with a few exceptions. With an increase in scientific understanding, models have become more complex, rendering an accretion mode untenable to the point where software productivity and sustainability have become active concerns in scientific c

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26

Greer, Bruce, John Harrison, Greg Henry, Wei Li, and Peter Tang. "Scientific Computing on the Itanium® Processor." Scientific Programming 10, no. 4 (2002): 329–37. http://dx.doi.org/10.1155/2002/193478.

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The 64-bit Intel® Itanium® architecture is designed for high-performance scientific and enterprise computing, and the Itanium processor is its first silicon implementation. Features such as extensive arithmetic support, predication, speculation, and explicit parallelism can be used to provide a sound infrastructure for supercomputing. A large number of high-performance computer companies are offering Itanium® -based systems, some capable of peak performance exceeding 50 GFLOPS. In this paper we give an overview of the most relevant architectural features and provide illustrations of how these

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27

Kang, Pilsung. "Programming for High-Performance Computing on Edge Accelerators." Mathematics 11, no. 4 (2023): 1055. http://dx.doi.org/10.3390/math11041055.

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The field of edge computing has grown considerably over the past few years, with applications in artificial intelligence and big data processing, particularly due to its powerful accelerators offering a large amount of hardware parallelism. As the computing power of the latest edge systems increases, applications of edge computing are being expanded to areas that have traditionally required substantially high-performant computing resources such as scientific computing. In this paper, we review the latest literature and present the current status of research for implementing high-performance co

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28

Simon, Horst D. "The Recent Revolution in High Performance Computing." MRS Bulletin 22, no. 10 (1997): 5–6. http://dx.doi.org/10.1557/s0883769400034096.

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Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of busines

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29

Matkerim, Bazargul, Darhan Akhmed-Zaki, and Manuel Barata. "Development high performance scientific computing application using model-driven architecture." Applied Mathematical Sciences 7 (2013): 4961–74. http://dx.doi.org/10.12988/ams.2013.37426.

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30

Adakin, A., S. Belov, D. Chubarov, et al. "Building a High Performance Computing Infrastructure for Novosibirsk Scientific Center." Journal of Physics: Conference Series 331, no. 5 (2011): 052020. http://dx.doi.org/10.1088/1742-6596/331/5/052020.

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31

Ferreira da Silva, Rafael, Rosa M. Badia, Deborah Bard, Ian T. Foster, Shantenu Jha, and Frédéric Suter. "Frontiers in Scientific Workflows: Pervasive Integration With High-Performance Computing." Computer 57, no. 8 (2024): 36–44. http://dx.doi.org/10.1109/mc.2024.3401542.

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32

Kumar, Phani, V. V. Nukala, Srdan Simunović, and Richard T. Mills. "Statistical physics of fracture: scientific discovery through high-performance computing." Journal of Physics: Conference Series 46 (September 1, 2006): 278–91. http://dx.doi.org/10.1088/1742-6596/46/1/039.

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33

Belletti, Francesco, Maria Cotallo, A. Cruz, et al. "Janus: An FPGA-Based System for High-Performance Scientific Computing." Computing in Science & Engineering 11, no. 1 (2009): 48–58. http://dx.doi.org/10.1109/mcse.2009.11.

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34

Chen, Zizhong, and Jack Dongarra. "Highly Scalable Self-Healing Algorithms for High Performance Scientific Computing." IEEE Transactions on Computers 58, no. 11 (2009): 1512–24. http://dx.doi.org/10.1109/tc.2009.42.

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35

Chang, Chia, Christopher Körber, and André Loud. "EspressoDB: A scientific database for managing high-performance computing workflows." Journal of Open Source Software 5, no. 46 (2020): 2007. http://dx.doi.org/10.21105/joss.02007.

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36

Jorissen, K., F. D. Vila, and J. J. Rehr. "A high performance scientific cloud computing environment for materials simulations." Computer Physics Communications 183, no. 9 (2012): 1911–19. http://dx.doi.org/10.1016/j.cpc.2012.04.010.

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37

Lee, Kin Long Kelvin, and Nalini Kumar. "Artificial Intelligence for Scientific Discovery at High-Performance Computing Scales." Computer 56, no. 4 (2023): 116–22. http://dx.doi.org/10.1109/mc.2023.3241692.

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38

Shoshany, Barak. "A C++17 thread pool for high-performance scientific computing." SoftwareX 26 (May 2024): 101687. http://dx.doi.org/10.1016/j.softx.2024.101687.

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39

Researcher. "PARALLELISM AND MULTITHREADING IN HIGH-PERFORMANCE COMPUTING." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 360–75. https://doi.org/10.5281/zenodo.13987265.

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This comprehensive article explores the critical role of parallelism and multithreading in high-performance computing (HPC), addressing the growing demand for computational power in scientific and technological advancements. It covers key concepts such as multithreading, thread pools, OpenMP, task parallelism, memory consistency models, and task scheduling in multicore processors. The article presents detailed explanations, code examples, and performance metrics from various studies, highlighting the significant efficiency, scalability, and resource utilization improvements achieved through th

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40

Getov, Vladimir, Paul Gray, Sava Mintchev, and Vaidy Sunderam. "Multi-Language Programming Environments for High Performance Java Computing." Scientific Programming 7, no. 2 (1999): 139–46. http://dx.doi.org/10.1155/1999/975837.

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Recent developments in processor capabilities, software tools, programming languages and programming paradigms have brought about new approaches to high performance computing. A steadfast component of this dynamic evolution has been the scientific community’s reliance on established scientific packages. As a consequence, programmers of high‐performance applications are reluctant to embrace evolving languages such as Java. This paper describes the Java‐to‐C Interface (JCI) tool which provides application programmers wishing to use Java with immediate accessibility to existing scientific package

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41

Misra, Goldi, Sandeep Agrawal, Nisha Kurkure, Shweta Das, Kapil Mathur, and Sucheta Pawar. "ONAMA: A Quantum Leap in High Performance Computing." Advanced Materials Research 328-330 (September 2011): 2337–42. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.2337.

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The growth of serial and High Performance Computing (HPC) applications presents the challenge of porting of scientific and engineering applications. A number of key issues and trends in High Performance Computing will impact the delivery of breakthrough science and engineering in the future. ONAMA was developed to cope with increasing demands for HPC. ONAMA, which means a new beginning, is a desktop based Graphical User Interface which is developed using C and GTK. It aims to satisfy the research needs of academic institutions. ONAMA is a comprehensive package, comprising of applications cover

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42

Hogervorst, Tom, Răzvan Nane, Giacomo Marchiori, Tong Dong Qiu, Markus Blatt, and Alf Birger Rustad. "Hardware Acceleration of High-Performance Computational Flow Dynamics Using High-Bandwidth Memory-Enabled Field-Programmable Gate Arrays." ACM Transactions on Reconfigurable Technology and Systems 15, no. 2 (2022): 1–35. http://dx.doi.org/10.1145/3476229.

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Scientific computing is at the core of many High-Performance Computing applications, including computational flow dynamics. Because of the utmost importance to simulate increasingly larger computational models, hardware acceleration is receiving increased attention due to its potential to maximize the performance of scientific computing. Field-Programmable Gate Arrays could accelerate scientific computing because of the possibility to fully customize the memory hierarchy important in irregular applications such as iterative linear solvers. In this article, we study the potential of using Field

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43

Anzt, Hartwig, Goran Flegar, Thomas Grützmacher, and Enrique S. Quintana-Ortí. "Toward a modular precision ecosystem for high-performance computing." International Journal of High Performance Computing Applications 33, no. 6 (2019): 1069–78. http://dx.doi.org/10.1177/1094342019846547.

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With the memory bandwidth of current computer architectures being significantly slower than the (floating point) arithmetic performance, many scientific computations only leverage a fraction of the computational power in today’s high-performance architectures. At the same time, memory operations are the primary energy consumer of modern architectures, heavily impacting the resource cost of large-scale applications and the battery life of mobile devices. This article tackles this mismatch between floating point arithmetic throughput and memory bandwidth by advocating a disruptive paradigm chang

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44

Vijayaraj, M., R. Malar Vizhi, P. Chandrakala, Laith H. Alzubaidi, Khasanov Muzaffar, and R. Senthilkumar. "Parallel and Distributed Computing for High-Performance Applications." E3S Web of Conferences 399 (2023): 04039. http://dx.doi.org/10.1051/e3sconf/202339904039.

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The study of parallel and distributed computing has become an important area in computer science because it makes it possible to create high-performance software that can effectively handle challenging computational tasks. In terms of their use in the world of high-performance applications, parallel and distributed computing techniques are given a thorough introduction in this study. The partitioning of computational processes into smaller subtasks that may be completed concurrently on numerous processors or computers is the core idea underpinning parallel and distributed computing. This strat

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45

Siek, J. G., and A. Lumsdaine. "The Matrix Template Library: generic components for high-performance scientific computing." Computing in Science & Engineering 1, no. 6 (1999): 70–71. http://dx.doi.org/10.1109/5992.805137.

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46

Siegel, Stephen F., and Timothy K. Zirkel. "FEVS: A Functional Equivalence Verification Suite for High-Performance Scientific Computing." Mathematics in Computer Science 5, no. 4 (2011): 427–35. http://dx.doi.org/10.1007/s11786-011-0101-6.

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47

Bourne, Emily, Yaman Güçlü, Said Hadjout, and Ahmed Ratnani. "Pyccel: a Python-to-X transpiler for scientific high-performance computing." Journal of Open Source Software 8, no. 83 (2023): 4991. http://dx.doi.org/10.21105/joss.04991.

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48

Möller, Matthias, and Cornelis Vuik. "On the impact of quantum computing technology on future developments in high-performance scientific computing." Ethics and Information Technology 19, no. 4 (2017): 253–69. http://dx.doi.org/10.1007/s10676-017-9438-0.

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49

Sorokin, Aleksei, Sergey Malkovsky, Georgiy Tsoy, Alexander Zatsarinnyy, and Konstantin Volovich. "Comparative Performance Evaluation of Modern Heterogeneous High-Performance Computing Systems CPUs." Electronics 9, no. 6 (2020): 1035. http://dx.doi.org/10.3390/electronics9061035.

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The study presents a comparison of computing systems based on IBM POWER8, IBM POWER9, and Intel Xeon Platinum 8160 processors running parallel applications. Memory subsystem bandwidth was studied, parallel programming technologies were compared, and the operating modes and capabilities of simultaneous multithreading technology were analyzed. Performance analysis for the studied computing systems running parallel applications based on the OpenMP and MPI technologies was carried out by using the NAS Parallel Benchmarks. An assessment of the results obtained during experimental calculations led t

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50

Pradeep, Murugan, Subramanian Suraj, Pandarinathan V., and D. Rajinigirinath Dr. "Using Docker for Containerization in High Performance Computing Applications." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2005–9. https://doi.org/10.31142/ijtsrd11591.

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Virtualization technology plays a vital role in cloud computing. In particular, benefits of virtualization are widely employed in high performance computing HPC applications. Containers have a long and storied history in computing. Unlike hypervisor virtualization, where one or more independent machines run virtually on physical hardware via an intermediation layer, containers instead run user space on top of an operating systems kernel. As a result, container virtualization is often called operating system level virtualization. Container technology allows multiple isolated user space instance

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