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Security in high-performance computing environments

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Published:23 August 2017Publication History
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Abstract

Exploring the many distinctive elements that make securing HPC systems much different than securing traditional systems.

References

  1. Adiga, N.R. et al. An overview of the Blue-Gene/L supercomputer. In Proceedings of the ACM/IEEE Conference on Supercomputing, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Austin, B. et al. 2014 NERSC Workload Analysis (Nov. 5., 2015); http://portal.nersc.gov/project/mpccc/baustin/NERSC_2014_Workload_Analysis_v1.1.pdf.Google ScholarGoogle Scholar
  3. Anderson, R.J. UEPS: A second-generation electronic wallet. In Proceedings of the 2nd European Symposium on Research in Computer Security (Nov. 1992), 411--418. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bailey, D.H. Resolving numerical anomalies in scientific computation, 2008.Google ScholarGoogle Scholar
  5. Bailey, D.H., Borwein, J.M. and Stodden, V. Facilitating reproducibility in scientific computing: Principles and practice. Reproducibility: Principles, Problems, Practices. H. Atmanspacher and S. Maasen, Eds. John Wiley and Sons, New York, NY, 2015.Google ScholarGoogle Scholar
  6. Bailey, D.H., Demmel, J., Kahan, W., Revy, G. and Sen, K. Techniques for the automatic debugging of scientific floating-point programs. In Proceedings of the 14th GAMM-IMACS International Symposium on Scientific Computing, Computer Arithmetic and Validated Numerics (Lyon, France, Sept. 2010).Google ScholarGoogle Scholar
  7. Bishop, M. Computer Security: Art and Science. Addison-Wesley Professional, Boston, MA, 2003.Google ScholarGoogle Scholar
  8. Cappello, F. Improving the trust in results of numerical simulations and scientific data analytics. 2015.Google ScholarGoogle Scholar
  9. CoreOS, Inc. rkt - App Container runtime. https://github.com/coreos/rkt.Google ScholarGoogle Scholar
  10. Cray, Inc. Cray Linux Environment Software Release Overview, s-2425--52xx edition (Apr 2014); http://docs.cray.com/books/S-2425-52xx.Google ScholarGoogle Scholar
  11. DARPA. Transparent Computing; http://www.darpa.mil/Our_Work/I2O/Programs/Transparent_Computing.aspx.Google ScholarGoogle Scholar
  12. Das, A., Agrawal, H., Zitnick, C.L., Parikh, D. and Batra, D. Human attention in visual question answering: Do humans and deep networks look at the same regions? In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  13. Dart, E., Rotman, L., Tierney, B., Hester, M. and Zurawski, J. The science DMZ: A network design pattern for data-intensive science. In Proceedings of the IEEE/ACM Annual SuperComputing Conference (Denver CO, 2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. DeMasi, O., Samak, T. and Bailey, D.H. Identifying HPC codes via performance logs and machine learning. In Proceedings of the Workshop on Changing Landscapes in HPC Security (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Dwork, C. Differential privacy. In Proceedings of the 33rd International Colloquium on Automata, Languages and Programming, Part II. Lecture Notes in Computer Science 4052, (July 2006), 1--12. Springer Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Gefter, A. Is artificial intelligence permanently inscrutable? Nautilus 40 (Sept. 1, 2016).Google ScholarGoogle Scholar
  17. Gentry, C. Computing arbitrary functions of encrypted data. Commun. ACM 53, 3 (Mar. 2010), 97--105. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Haber, S. and Stornetta, W.S. How to time-stamp a digital document. J. Cryptology 3, 2 (Jan. 1991), 99--111. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Jacobsen, D.M. and Canon, R.S. Contain this, unleashing docker for HPC. Proceedings of the Cray User Group, 2015.Google ScholarGoogle Scholar
  20. Jiang, L. and Su, Z. Osprey: A practical type system for validating dimensional unit correctness of c programs. In Proceedings of the 28th International Conference on Software Engineering, (2006), 262--271 ACM, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. KBase: The Department of Energy Systems Biology Knowledgebase; http://kbase.us.Google ScholarGoogle Scholar
  22. Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S. and Smith, A. What can we learn privately? SIAM J. Computing 40, 3 (2011), 793--826. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Kurtzer, G.M. et al. Singularity; http://singularity.lbl.gov.Google ScholarGoogle Scholar
  24. Marko, J. and Bergman, L. Internet attack is called broad and long lasting. New York Times (May 10, 2005).Google ScholarGoogle Scholar
  25. Merkel, D. Docker: Lightweight Linux containers for consistent development and deployment. Linux J. 239 (2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Nakamoto, S. Bitcoin: A Peer-to-Peer Electronic Cash System (May 24, 2009); http://www.bitcoin.org/bitcoin.pdf.Google ScholarGoogle Scholar
  27. Nataraj, A., Malony, A.D., Morris, A. and Shende, S. Early experiences with KTAU on the IBM BG/L. In European Conference on Parallel Processing, pp. 99--110. Springer, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Paxson, V. Bro: A system for detecting network intruders in real time. Computer Networks 31, 23 (1999), 2435--2463. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Peisert, S., et al. The Medical Science DMZ. J. American Medical Informatics Assoc. 23, 6 (Nov. 1, 2016).Google ScholarGoogle Scholar
  30. Peisert S. Fingerprinting Communication and Computation on HPC Machines. TR LBNL-3483E, Lawrence Berkeley National Laboratory, June 2010.Google ScholarGoogle Scholar
  31. Peisert, S., et al. ASCR Cybersecurity for Scientific Computing Integrity. TR LBNL-6953E, U.S. Department of Energy Office of Science, Feb. 2015.Google ScholarGoogle Scholar
  32. Peisert, S. et al. ASCR Cybersecurity for Scientific Computing Integrity|Research Pathways and Ideas Workshop. TR LBNL-191105, U.S. Department of Energy Office of Science, Sept. 2015.Google ScholarGoogle Scholar
  33. Pérez, F. and Granger, B.E. IPython: A System for interactive scientific computing. Computing in Science and Engineering 9, 3 (May 2007), 21--29. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Popa, R.A., Redfield, C., Zeldovich, N. and Balakrishnan, H. Cryptdb: Processing queries on an encrypted database. Commun. ACM 55, 9 (Sept. 2012), 103--111. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Popa, R.A., Stark, E., Helfer, J., Valdez, S., Zeldovich, N., Kaashoek, M.F. and Balakrishnan, H. Building Web applications on top of encrypted data using Mylar. In Proceedings of the 11th Symposium on Networked Systems Design and Implementation (2014), 157--172. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Rubio-Gonzàlez, C. Precimonious: Tuning assistant for floating-point precision. In Proceedings of the International Conf. on High Performance Computing, Networking, Storage and Analysis. ACM, 2013, 27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Reubel, O. WarpIV: In situ visualization and analysis of ion accelerator simulations. IEEE Computer Graphics and Applications 36, 3 (2016), 22--35.Google ScholarGoogle Scholar
  38. Ramakrishnan, L., Poon, S., Hendrix, V., Gunter, D., Pastorello, G.Z. and Agarwal, D. Experiences with user-centered design for the Tigres workflow API. In Proceedings of 2014 IEEE 10th International Conference on e-Science, vol 1. IEEE, 290--297. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Singer A. Tempting fate. ;login: 30, 1 (Feb. 2005), 27--30.Google ScholarGoogle Scholar
  40. Schneier, B. and Kelsey, J. Automatic event-stream notarization using digital signatures. In Proceedings of the 4th International Workshop on Security Protocols. Springer, 1996, 155--169. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Sommer, R. and Paxson, V. Outside the closed world: On using machine learning for network intrusion detection. In Proceedings of the 31st IEEE Symposium on Security and Privacy, Oakland, CA, May 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Stoll, C. Stalking the wily hacker. Commun. ACM 31, 5 (May 1988), 484--497. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Skinner, D., Wright, N., Fürlinger, K., Yelick, K.A. and Snavely, A. Integrated Performance Monitoring; http://ipm-hpc.sourceforge.net/.Google ScholarGoogle Scholar
  44. Wallace, D. Compute node Linux: New frontiers in compute node operating systems. Cray User Group, 2007.Google ScholarGoogle Scholar
  45. Whitlock, B., Favre, J.M. and Meredith, J.S. Parallel in situ coupling of simulation with a fully featured visualization system. In Proceedings of the 11th Eurographics Conference on Parallel Graphics and Visualization, 2011, 101--109. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Wisniewski, R.W., Inglett, T., Keppel, P., Murty, R. and Riesen, R. mOS: An architecture for extreme-scale operating systems. In Proceedings of the 4th International Workshop on Runtime and Operating Systems for Supercomputers. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Whalen, S., Peisert, S. and Bishop, M. Network-theoretic classification of parallel computation patterns. In Proceedings of the First International Workshop on Characterizing Applications for Heterogeneous Exascale Systems (Tucson, AZ, June 4, 2011).Google ScholarGoogle Scholar
  48. Whalen, S., Peisert, S. and Bishop, M. Multiclass Classification of Distributed Memory Parallel Computations. Pattern Recognition Letters 34, 3 (Feb. 2013), 322--329. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Yosinski, J., Clune, J., Fuchs, T. and Lipson, H. Understanding neural networks through deep visualization. In Proceedings of the Deep Learning Workshop, International Conference on Machine Learning, 2015.Google ScholarGoogle Scholar
  50. Yelick, K. A Superfacility for Data Intensive Science. Advanced Scientific Computing Research Advisory Committee, Washington, DC, Nov. 8, 2016; http://science.energy.gov/~/media/ascr/ascac/pdf/meetings/201609/Yelick_Superfacility-ASCAC_2016.pdf.Google ScholarGoogle Scholar

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    • Published in

      cover image Communications of the ACM
      Communications of the ACM  Volume 60, Issue 9
      September 2017
      94 pages
      ISSN:0001-0782
      EISSN:1557-7317
      DOI:10.1145/3134526
      Issue’s Table of Contents

      Copyright © 2017 Owner/Author

      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 23 August 2017

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