Abstract

This report presents challenges, opportunities, and directions for computational science and engineering (CSE) research and education for the next decade. Over the past two decades the field of CSE has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers with algorithmic inventions and software systems that transcend disciplines and scales. CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society, and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution and increased attention to data-driven discovery, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. With these many current and expanding opportunities for the CSE field, there is a growing demand for CSE graduates and a need to expand CSE educational offerings. This need includes CSE programs at both the undergraduate and graduate levels, as well as continuing education and professional development programs, exploiting the synergy between computational science and data science. Yet, as institutions consider new and evolving educational programs, it is essential to consider the broader research challenges and opportunities that provide the context for CSE education and workforce development.

Keywords

  1. computational science and engineering
  2. education
  3. high-performance computing
  4. large data analytics
  5. predictive science

MSC codes

  1. 97-02
  2. 0A-72
  3. 01-08
  4. 68U20
  5. 68W99
  6. 97A99
  7. 65Y99
  8. 65Y05
  9. 68N99
  10. 62-07

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References

1.
M. Adams, D. Higdon et al., Report of the National Academies Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification, National Academies Press, 2012, http://www.nap.edu/catalog/13395/.
2.
S. Baker et al., Data-Enabled Science in the Mathematical and Physical Sciences, National Science Foundation, 2010, http://www.nsf.gov/mps/dms/documents/Data-EnabledScience.pdf.
3.
M. R. Benioff and E. D. Lazowski, PITAC Co-Chairs, Computational Science: Ensuring America's Competitiveness: President's Information Technology Advisory Committee, 2005, https://www.nitrd.gov/pitac/reports/20050609_computational/computational.pdf.
4.
S. Billinge, Viewpoint: The nanostructure problem, Physics, 3 (2010), art. 25.
5.
D. L. Brown, J. Bell, D. Estep, W. Gropp, B. Hendrickson, S. Keller-McNulty, D. Keyes, J. T. Oden, L. Petzold, and M. Wright, Applied Mathematics at the U.S. Department of Energy: Past, Present, and a View to the Future, Office of Science, U.S. Department of Energy, 2008, http://science.energy.gov/~/media/ascr/pdf/program-documents/docs/Brown_report_may_08.pdf.
6.
D. Brown, P. Messina et al., Scientific Grand Challenges: Crosscutting Technologies for Computing at the Exascale, Office of Science, U.S. Department of Energy, 2010, http://science.energy.gov/~/media/ascr/pdf/program-documents/docs/crosscutting_grand_challenges.pdf.
7.
G. Ceder and K. Persson, The stuff of dreams, Scientific Amer., 309 (2013), pp. 36--40.
8.
Council on Competitiveness, Advance: Benchmarking Industrial Use of High Performance Computing for Innovation, 2008, http://www.compete.org/storage/images/uploads/File/PDF%20Files/HPC_ADVANCE_FINAL0508(1).pdf.
9.
DOE Computational Science Graduate Fellowship Program, https://www.krellinst.org/csgf/.
10.
J. Dongarra, P. Beckman, T. Moore, P. Aerts, G. Aloisio, J.-C. Andre, D. Barkai, J.-Y. Berthou, T. Boku, B. Braunschweig, F. Cappello, B. Chapman, X. Chi, A. Choudhary, S. Dosanjh, T. Dunning, S. Fiore, A. Geist, W. D. Gropp, R. Harrison, M. Hereld, M. Heroux, A. Hoisie, K. Hotta, Y. Ishikawa, Z. Jin, F. Johnson, S. Kale, R. Kenway, D. Keyes, W. Kramer, J. Labarta, A. Lichnewsky, T. Lippert, R. Lucas, B. Maccabe, S. Matsuoka, P. Messina, P. Michielse, B. Mohr, M. Mueller, W. Nagel, H. Nakashima, M. E. Papka, D. Reed, M. Sato, E. Seidel, J. Shalf, D. Skinner, M. Snir, T. Sterling, R. Stevens, F. Streitz, R. Sugar, S. Sumimoto, W. Tang, J. Taylor, R. Thakur, A. Trefethen, M. Valero, A. van der Steen, J. Vetter, P. Williams, R. Wisniewski, and K. Yelick, The international exascale software roadmap, Internat. J. High Performance Comput. Appl., 25 (2011), pp. 3--60, https://doi.org/10.1177/1094342010391989.
11.
J. Dongarra, L. Petzold, and V. Voevodin, Survey of CSE Graduate Programs, 2012, https://www.siam.org/students/resources/pdf/Summary-of-CSE-Survey.pdf.
12.
J. Dongarra and F. Sullivan, The top ten algorithms of the 20th century, Comput. Sci. Engrg., 2 (2000), pp. 22--23, https://doi.org/10.1109/MCISE.2000.814652.
13.
S. Glotzer, S. Kim, P. T. Cummings, A. Deshmukh, M. Head-Gordon, G. Karniadakis, L. Petzold, C. Sagui, and M. Shinozuka, International Assessment of Research and Development in Simulation-Based Engineering and Science, World Technologies Evaluation Center Panel Report, 2009, http://www.wtec.org/sbes/SBES-GlobalFinalReport.pdf.
14.
P. Glynn, From Human Genome to Materials Genome, 2014, http://science.energy.gov/news/featured-articles/2014/127050.
15.
W. Gropp, R. Harrison et al., Future Directions for NSF Advanced Computing Infrastructure to Support U.S. Science and Engineering in 2017--2020, National Academies Press, 2016, http://www.nap.edu/catalog/21886/.
16.
M. Heroux, G. Allen, et al., Computational Science and Engineering Software Sustainability and Productivity Challenges (CSESSP) Workshop Report, Networking and Information Technology Research and Development (NITRD) Program, Arlington, VA, 2016, https://www.nitrd.gov/PUBS/CSESSPWorkshopReport.pdf.
17.
S. Hettrick, Research Software Sustainability, Report on a Knowledge Exchange Workshop, 2016, http://repository.jisc.ac.uk/6332/1/Research_Software_Sustainability_Report_on_KE_Workshop_Feb_2016_FINAL.pdf.
18.
A. J. Hey, S. Tansley, K. M. Tolle et al., The Fourth Paradigm: Data-Intensive Scientific Discovery, Vol. 1, Microsoft Research Redmond, WA, 2009.
19.
High End Computing Interagency Working Group (HEC-IWG), Education and workforce development in the high end computing community, National Coordination Office for Networking and Information Technology Research and Development, 2013. https://www.nitrd.gov/nitrdgroups/images/4/4f/HEC_Education_Initiative_Position_%28March_2013%29.pdf.
20.
N. J. Higham, ed., The Princeton Companion to Applied Mathematics, Princeton University Press, Princeton, NJ, 2015.
21.
IEEE Standard Glossary of Software Engineering Terminology, IEEE std 610.12-1990, IEEE, 1990.
22.
F. Jahanian, Next Generation Computing and Big Data Analytics: Testimony at Congressional Hearing on Big Data Analytics, 2013, http://www.nsf.gov/attachments/128791/public/JahanianBigDataTestimony4-24-13.pdf.
23.
H. Johansen, L. C. McInnes et al., Software Productivity for Extreme-Scale Science, Report on DOE Workshop, 2014, http://www.orau.gov/swproductivity2014/SoftwareProductivityWorkshopReport2014.pdf.
24.
C. Johnson, R. Moorhead, T. Munzner, H. Pfister, P. Rheingans, and T. S. Yoo, NIH/NSF Visualization Research Challenges Report, IEEE Computing Society, Los Alamitos, CA, 2006, http://tab.computer.org/vgtc/vrc/NIH-NSF-VRC-Report-Final.pdf.
25.
C. Johnson, R. Ross, S. Ahern, J. Ahrens, W. Bethel, K. Ma, M. Papka, J. Rosendale, H. Shen, and J. Thomas, Visualization and Knowledge Discovery: Report from the DOE/ASCR Workshop on Visual Analysis and Data Exploration at Extreme Scale, 2007, http://science.energy.gov/~/media/ascr/pdf/program-documents/docs/Doe_visualization_report_2007.pdf.
26.
D. Keyes, V. Taylor, et al., National Science Foundation Advisory Committee on CyberInfrastructure, Task Force on Software for Science and Engineering, final report, 2011, http://www.nsf.gov/cise/aci/taskforces/TaskForceReport_Software.pdf.
27.
D. E. Keyes, P. Colella, T. H. Dunning, Jr., and W. D. Gropp, eds., A Science-Based Case for Large-Scale Simulation, Office of Science, U.S. Department of Energy, 2003 http://www.pnl.gov/scales/docs/volume1_300dpi.pdf.
28.
D. E. Keyes, L. C. McInnes, C. Woodward, W. Gropp, E. Myra, M. Pernice, J. Bell, J. Brown, A. Clo, J. Connors, E. Constantinescu, D. Estep, K. Evans, C. Farhat, A. Hakim, G. Hammond, G. Hansen, J. Hill, T. Isaac, X. Jiao, K. Jordan, D. Kaushik, E. Kaxiras, A. Koniges, K. Lee, A. Lott, Q. Lu, J. Magerlein, R. Maxwell, M. McCourt, M. Mehl, R. Pawlowski, A. P. Randles, D. Reynolds, B. Rivière, U. Rüde, T. Scheibe, J. Shadid, B. Sheehan, M. Shephard, A. Siegel, B. Smith, X. Tang, C. Wilson, and B. Wohlmuth, Multiphysics simulations: Challenges and opportunities, Internat. J. High Performance Comput. Appl., 27 (2013), pp. 4--83, https://doi.org/10.1177/1094342012468181.
29.
G. King, Restructuring the social sciences: Reflections from Harvard's Institute for Quantitative Social Science, PS: Political Science & Politics, 47 (2014), pp. 165--172.
30.
H. P. Langtangen, U. Rüde, and A. Tveito, Scientific Computing, Springer, Berlin, Heidelberg, 2015, pp. 1302--1310.
31.
R. Lucas et al., The Top Ten Exascale Research Challenges, Advanced Scientific Computing Advisory Committee (ASCAC) Subcommittee for the Top Ten Exascale Research Challenges, Office of Science, U.S. Department of Energy, 2014, http://science.energy.gov/~/media/ascr/ascac/pdf/meetings/20140210/Top10reportFEB14.pdf.
32.
Materials Project, The Materials Project, 2014, https://www.materialsproject.org.
33.
P. Messina et al., Argonne Training Program on Extreme-Scale Computing (ATPESC), 2017, http://extremecomputingtraining.anl.gov.
34.
A. Mezzacappa et al., Breakthroughs: Report of the Panel on Recent Significant Advances in Computational Science, 2008, http://science.energy.gov/~/media/ascr/pdf/program-documents/docs/Breakthroughs_2008.pdf.
36.
J. T. Oden, T. Belytschko, T. J. R. Hughes, C. Johnson, D. Keyes, A. Laub, L. Petzold, D. Srolovitz, and S. Yip, Revolutionizing Engineering Science through Simulation: A Report of the National Science Foundation Blue Ribbon Panel on Simulation-Based Engineering Science, 2006, http://www.nsf.gov/pubs/reports/sbes_final_report.pdf.
37.
J. T. Oden, O. Ghattas, J. L. King et al., Final Report of the Advisory Committee for Cyberinfrastructure Task Force on Grand Challenges, National Science Foundation, 2011, http://www.nsf.gov/cise/aci/taskforces/TaskForceReport_GrandChallenges.pdf.
38.
L. Petzold et al., Graduate education in computational science and engineering, SIAM Rev., 43 (2001), pp. 163--177, https://doi.org/10.1137/S0036144500379745.
39.
P. Ricoux and M. Ramalho, European Exascale Software Initiative, 2017, http://www.eesi-project.eu.
40.
R. Rosner et al., The Opportunities and Challenges of Exascale Computing, Advanced Scientific Computing Advisory Committee (ASCAC) Subcommittee on Exascale Computing, Office of Science, U.S. Department of Energy, 2010, http://science.energy.gov/~/media/ascr/ascac/pdf/reports/Exascale_subcommittee_report.pdf.
41.
S. Sachs, K. Yelick et al., ASCR Programming Challenges for Exascale Computing, 2011, http://science.energy.gov/~/media/ascr/pdf/program-documents/docs/ProgrammingChallengesWorkshopReport.pdf.
42.
SIAM Activity Group on Computational Science and Engineering (SIAG/CSE), SIAG/CSE wiki, 2017, http://wiki.siam.org/siag-cse/.
43.
SIAM Activity Group on Computational Science and Engineering (SIAG/CSE), SIAM CSE conference series, 2017, http://www.siam.org/meetings/archives.php\#CS.
44.
V. Stodden, D. H. Bailey, J. Borwein, R. J. LeVeque, W. Rider, and W. Stein, Setting the Default to Reproducible: Reproducibility in Computational and Experimental Mathematics, ICERM Workshop Report, 2013, https://icerm.brown.edu/topical_workshops/tw12-5-rcem/icerm_report.pdf.
45.
P. Turner et al., Undergraduate computational science and engineering education, SIAM Rev., 53 (2011), pp. 561--574, https://doi.org/10.1137/07070406X.
46.
U.S. Department of Energy Advanced Scientific Computing Advisory Committee, ASCAC Workforce Subcommittee Letter, 2014, http://science.energy.gov/~/media/ascr/ascac/pdf/charges/ASCAC_Workforce_Letter_Report.pdf.
47.
U.S. Department of Energy Exascale Computing Project (ECP), 2017, https://exascaleproject.org.
48.
Wissenschaftsrat, Strategische Weiterentwicklung des Hoch- und Höchstleistungsrechnens in Deutschland --- Positionspapier, 2012, http://www.wissenschaftsrat.de/download/archiv/1838-12.pdf.
49.
Wissenschaftsrat, Bedeutung und Weiterentwicklung von Simulation in der Wissenschaft, Positionspapier, 2014, http://www.wissenschaftsrat.de/download/archiv/4032-14.pdf.
50.
P. C. Wong, H.-W. Shen, C. R. Johnson, C. Chen, and R. B. Ross, The top 10 challenges in extreme-scale visual analytics, IEEE Computer Graphics Appl., 32 (2012), pp. 63--67. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907777/.
51.
Working towards Sustainable Software for Science: Practice and Experiences, http://wssspe.researchcomputing.org.uk.

Information & Authors

Information

Published In

cover image SIAM Review
SIAM Review
Pages: 707 - 754
ISSN (online): 1095-7200

History

Submitted: 3 October 2016
Accepted: 28 November 2017
Published online: 8 August 2018

Keywords

  1. computational science and engineering
  2. education
  3. high-performance computing
  4. large data analytics
  5. predictive science

MSC codes

  1. 97-02
  2. 0A-72
  3. 01-08
  4. 68U20
  5. 68W99
  6. 97A99
  7. 65Y99
  8. 65Y05
  9. 68N99
  10. 62-07

Authors

Affiliations

Funding Information

National University of Singapore (NUS) https://doi.org/10.13039/501100001352
U.S. Department of Energy (DOE) https://doi.org/10.13039/100000015 : DE-AC02-06CH11357

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