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11 PFLOP/s simulations of cloud cavitation collapse

Published:17 November 2013Publication History

ABSTRACT

We present unprecedented, high throughput simulations of cloud cavitation collapse on 1.6 million cores of Sequoia reaching 55% of its nominal peak performance, corresponding to 11 PFLOP/s. The destructive power of cavitation reduces the lifetime of energy critical systems such as internal combustion engines and hydraulic turbines, yet it has been harnessed for water purification and kidney lithotripsy. The present two-phase flow simulations enable the quantitative prediction of cavitation using 13 trillion grid points to resolve the collapse of 15'000 bubbles. We advance by one order of magnitude the current state-of-the-art in terms of time to solution, and by two orders the geometrical complexity of the flow. The software successfully addresses the challenges that hinder the effective solution of complex flows on contemporary supercomputers, such as limited memory bandwidth, I/O bandwidth and storage capacity. The present work redefines the frontier of high performance computing for fluid dynamics simulations.

References

  1. R. Abgrall and S. Karni. Computations of compressible multifluids. Journal of Computational Physics, 169(2):594--623, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. N. Adams and S. Schmidt. Shocks in cavitating flows. In C. F. Delale, editor, Bubble Dynamics and Shock Waves, volume 8 of Shock Wave Science and Technology Reference Library, pages 235--256. Springer Berlin Heidelberg, 2013.Google ScholarGoogle Scholar
  3. A. S. Almgren, J. B. Bell, M. J. Lijewski, Z. Lukić, and E. V. Andel. Nyx: A massively parallel amr code for computational cosmology. The Astrophysical Journal, 765(1):39, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  4. AMD Inc. Software Optimization Guide for the AMD 15h Family, 2011.Google ScholarGoogle Scholar
  5. T. B. Benjamin and A. T. Ellis. The collapse of cavitation bubbles and the pressures thereby produced against solid boundaries. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 260(1110):221--240, 1966.Google ScholarGoogle ScholarCross RefCross Ref
  6. M. Berger and P. Colella. Local adaptive mesh refinement for shock hydrodynamics. Journal of Computational Physics, 82(1):64--84, 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. J. Berger and J. Oliger. Adaptive mesh refinement for hyperbolic partial differential equations. J. Comput. Phys., 53(3):484--512, 1984.Google ScholarGoogle ScholarCross RefCross Ref
  8. M. Berzins, J. Luitjens, Q. Meng, T. Harman, C. A. Wight, and J. R. Peterson. Uintah: a scalable framework for hazard analysis. In Proceedings of the 2010 TeraGrid Conference, TG '10, pages 3:1--3:8. ACM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. R. Blake, M. C. Hooton, P. B. Robinson, and R. P. Tong. Collapsing cavities, toroidal bubbles and jet impact. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 355(1724):537--550, 1997.Google ScholarGoogle ScholarCross RefCross Ref
  10. C. E. Brennen. Cavitation and bubble dynamics. Oxford University Press, USA, 1995.Google ScholarGoogle Scholar
  11. C. E. Brennen. An introduction to cavitation fundamentals. Technical report, In: WIMRC Forum 2011 -- Cavitation: Turbo-machinery & Medical Applications, 2011.Google ScholarGoogle Scholar
  12. A. Cohen, I. Daubechies, and P. Vial. Wavelets on the interval and fast wavelet transforms. Applied and Computational Harmonic Analysis, 1(1):54--81, 1993.Google ScholarGoogle ScholarCross RefCross Ref
  13. A. Cucheval and R. Chow. A study on the emulsification of oil by power ultrasound. Ultrasonics Sonochemistry, 15(5):916--920, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  14. J. Dear and J. Field. A study of the collapse of arrays of cavities. J. Fluid Mech, 190(409):172, 1988.Google ScholarGoogle Scholar
  15. N. G. Dickson, K. Karimi, and F. Hamze. Importance of explicit vectorization for cpu and gpu software performance. Journal of Computational Physics, 230(13):5383--5398, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. M. O. Domingues, S. M. Gomes, O. Roussel, and K. Schneider. Space-time adaptive multiresolution methods for hyperbolic conservation laws: Applications to compressible euler equations, Sep 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. D. Donoho. Interpolating wavelet transforms, 1992.Google ScholarGoogle Scholar
  18. S. Faulk, E. Loh, M. L. D. Vanter, S. Squires, and L. Votta. Scientific computing's productivity gridlock: How software engineering can help. Computing in Science Engineering, 11(6):30--39, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. R. T. Fisher, L. P. Kadanoff, D. Q. Lamb, A. Dubey, T. Plewa, A. Calder, F. Cattaneo, P. Constantin, I. T. Foster, M. E. Papka, S. I. Abarzhi, S. M. Asida, P. M. Rich, C. C. Glendenin, K. Antypas, D. J. Sheeler, L. B. Reid, B. Gallagher, and S. G. Needham. Terascale turbulence computation using the flash3 application framework on the ibm blue gene/lsystem. IBM Journal of Research and Development, 52(1--2):127--136, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Folk M., Cheng A. and Yates K. Hdf5: A file format and i/o library for high performance computing applications. In Proceedings of Supercomputing, 1999.Google ScholarGoogle Scholar
  21. J. P. Franc and M. Riondet. Incubation time and cavitation erosion rate of work-hardening materials. In The proceeding of the Sixth International Symposium on Cavitation, CAV2006, 2006.Google ScholarGoogle Scholar
  22. B. Fryxell, K. Olson, P. Ricker, F. X. Timmes, M. Zingale, D. Q. Lamb, P. MacNeice, R. Rosner, J. W. Truran, and H. Tufo. Flash: An adaptive mesh hydrodynamics code for modeling astrophysical thermonuclear flashes. The Astrophysical Journal Supplement Series, 131(1):273, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  23. J.-l. Gailly and M. Adler. Zlib compression library. 2004.Google ScholarGoogle Scholar
  24. E. Gamma, R. Helm, R. Johnson, and J. Vlissides. Design patterns: Abstraction and reuse of object-oriented design. In O. Nierstrasz, editor, ECOOP' 93 --- Object-Oriented Programming, volume 707 of Lecture Notes in Computer Science, pages 406--431. Springer Berlin/Heidelberg, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. F. R. Gilmore. The collapse and growth of a spherical bubble in a viscous compressible liquid. Technical Report 26--4, California Institute of Technology, 1952.Google ScholarGoogle Scholar
  26. J. A. Greenough, B. R. De Supinski, R. K. Yates, C. A. Rendleman, D. Skinner, V. Beckner, M. Lijewski, and J. Bell. Performance of a block structured, hierarchical adaptive mesh refinement code on the 64k node ibm bluegene/l computer. Computer, pages 1--12, 2005.Google ScholarGoogle Scholar
  27. W. Gropp, D. Kaushik, D. Keyes, and B. Smith. High-performance parallel implicit CFD. Parallel Computing, 27(4):337--362, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. F. Günther, M. Mehl, M. Pögl, and C. Zenger. A cache aware algorithm for pdes on hierarchical data structures based on space filling curves. SIAM Journal on Scientific Computing, 28(5):1634--1650, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. F. G. Hammitt. Damage to solids caused by cavitation. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 260(1110):245--255, 1966.Google ScholarGoogle ScholarCross RefCross Ref
  30. I. Hansson, V. Kedrinskii, and K. A. Morch. On the dynamics of cavity clusters. Journal of Physics D: Applied Physics, 15(9):1725, 1982.Google ScholarGoogle ScholarCross RefCross Ref
  31. R. Haring, M. Ohmacht, T. Fox, M. Gschwind, D. Satterfield, K. Sugavanam, P. Coteus, P. Heidelberger, M. Blumrich, R. Wisniewski, A. Gara, G.-T. Chiu, P. Boyle, N. Chist, and C. Kim. The ibm blue gene/q compute chip. Micro, IEEE, 32(2):48--60, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. N. A. Hawker and Y. Ventikos. Interaction of a strong shockwave with a gas bubble in a liquid medium: a numerical study. Journal of Fluid Mechanics, 701:59--97, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  33. B. Hejazialhosseini, D. Rossinelli, C. Conti, and P. Koumoutsakos. High throughput software for direct numerical simulations of compressible two-phase flows. Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, pages 16:1--16:12, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. B. Hejazialhosseini, D. Rossinelli, and P. Koumoutsakos. 3D shock-bubble interactions at mach 3. Physics of Fluids (Gallery of Fluid Motion), 2013.Google ScholarGoogle Scholar
  35. R. Hickling and M. S. Plesset. Collapse and rebound of a spherical bubble in water. Physics of Fluids, 7(1):7--14, 1964.Google ScholarGoogle ScholarCross RefCross Ref
  36. M. Holmström. Solving hyperbolic pdes using interpolating wavelets. SIAM Journal on Scientific Computing, 21(2):405--420, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. X. Y. Hu, B. C. Khoo, N. A. Adams, and F. L. Huang. A conservative interface method for compressible flows. Journal of Computational Physics, 219(2):553--578, Dec 10 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Y. Hu, A. Cox, and W. Zwaenepoel. Improving fine-grained irregular shared-memory benchmarks by data reordering. In Supercomputing, ACM/IEEE 2000 Conference, pages 33--33, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. IBM. A2 Processor User's Manual for Blue Gene/Q, 2012.Google ScholarGoogle Scholar
  40. T. Ikeda, S. Yoshizawa, M. Tosaki, J. S. Allen, S. Takagi, N. Ohta, T. Kitamura, and Y. Matsumoto. Cloud cavitation control for lithotripsy using high intensity focused ultrasound. Ultrasound in Medicine & Biology, 32(9):1383--1397, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  41. Intel Corporation. Intel® 64 and IA-32 Architectures Optimization Reference Manual. Intel Corporation, 2009.Google ScholarGoogle Scholar
  42. G. Jiang and C. Shu. Efficient implementation of weighted ENO schemes. Journal of Computational Physics, 126(1):202--228, Jun 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. E. Johnsen and T. Colonius. Implementation of WENO schemes in compressible multicomponent flow problems. Journal of Computational Physics, 219(2):715--732, Dec 10 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. E. Johnsen and T. Colonius. Numerical simulations of non-spherical bubble collapse. Journal of Fluid Mechanics, 629:231--262, 5 2009.Google ScholarGoogle ScholarCross RefCross Ref
  45. E. Johnsen and F. Ham. Preventing numerical errors generated by interface-capturing schemes in compressible multi-material flows. Journal of Computational Physics, 231(17):5705--5717, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. D. Kelly. A software chasm: Software engineering and scientific computing. Software, IEEE, 24(6):120--119, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. N. K. R. Kevlahan and O. V. Vasilyev. An adaptive wavelet collocation method for fluid-structure interaction at high reynolds numbers. SIAM Journal on Scientific Computing, 26(6):1894--1915, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. B.-J. Kim and W. Pearlman. An embedded wavelet video coder using three dimensional set partitioning in hierarchical trees (spiht). In Data Compression Conference, 1997. DCC '97. Proceedings, pages 251--260, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. R. T. Knapp. Recent investigations of the mechanics of cavitations and cavitation damage. Trans. ASME, 77, 1955.Google ScholarGoogle Scholar
  50. T. Kodama and K. Takayama. Dynamic behavior of bubbles during extracorporeal shock-wave lithotripsy. Ultrasound in Medicine & Biology, 24(5):723--738, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  51. E. Lauer, X. Y. Hu, S. Hickel, and N. A. Adams. Numerical investigation of collapsing cavity arrays. Physics of Fluids, 24(5):052104, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  52. J. Li, W.-k. Liao, A. Choudhary, R. Ross, R. Thakur, W. Gropp, R. Latham, A. Siegel, B. Gallagher, and M. Zingale. Parallel netcdf: A high-performance scientific i/o interface. In Proceedings of the 2003 ACM/IEEE conference on Supercomputing, SC '03. ACM, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. J. F. Lofstead, S. Klasky, K. Schwan, N. Podhorszki, and C. Jin. Flexible io and integration for scientific codes through the adaptable io system (adios). In Proceedings of the 6th international workshop on Challenges of large applications in distributed environments, CLADE '08, pages 15--24. ACM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. J. Mellor-Crummey, D. Whalley, and K. Kennedy. Improving memory hierarchy performance for irregular applications using data and computation reorderings. International Journal of Parallel Programming, 29(3):217--247, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Q. Meng and M. Berzins. Abstract: Uintah hybrid task-based parallelism algorithm. In High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:, pages 1431--1432, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. K. Mørch. Energy considerations on the collapse of cavity clusters. Applied Scientific Research, 38:313--321, 1982.Google ScholarGoogle ScholarCross RefCross Ref
  57. A. Myers. Stanford researchers break million-core supercomputer barrier, January 2013.Google ScholarGoogle Scholar
  58. P. Colella, D. T. Graves, T. J. Ligocki, D. F. Martin, D. Mondiano, D. B. Serafini, and B. Van Straalen. Chombo software package for amr applications design document. Technical report, Lawrence Berkeley National Laboratory, 2003.Google ScholarGoogle Scholar
  59. R. Prosser. Resolution independent lifted interpolating wavelets on an interval. 2009.Google ScholarGoogle Scholar
  60. D. Ranjan, J. H. J. Niederhaus, J. G. Oakley, M. H. Anderson, J. A. Greenough, and R. Bonazza. Experimental and numerical investigation of shock-induced distortion of a spherical gas inhomogeneity. Physica Scripta Volume T, 132(1):014020, Dec. 2008.Google ScholarGoogle ScholarCross RefCross Ref
  61. L. Rayleigh. Viii. on the pressure developed in a liquid during the collapse of a spherical cavity. Philosophical Magazine Series 6, 34(200):94--98, 1917.Google ScholarGoogle ScholarCross RefCross Ref
  62. S. J. Reckinger, D. Livescu, and O. V. Vasilyev. Adaptive wavelet collocation method simulations of Rayleigh-Taylor instability. Physica Scripta, T142, DEC 2010. 2nd International Conference and Advanced School on Turbulent Mixing and Beyond, Abdus Salam Int Ctr Theoret Phys, Trieste, ITALY, JUL 27-AUG 07, 2009.Google ScholarGoogle Scholar
  63. S. M. Reckinger, O. V. Vasilyev, and B. Fox-Kemper. Adaptive volume penalization for ocean modeling. Ocean Dynamics, 62(8):1201--1215, AUG 2012.Google ScholarGoogle ScholarCross RefCross Ref
  64. D. Rossinelli, B. Hejazialhosseini, D. Spampinato, and P. Koumoutsakos. Multicore/multi-gpu accelerated simulations of multiphase compressible flows using wavelet adapted grids. SIAM J. Scientific Computing, 33(2), 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. O. Roussel, K. Schneider, A. Tsigulin, and H. Bockhorn. A conservative fully adaptive multiresolution algorithm for parabolic pdes. Journal Of Computational Physics, 188(2):493--523, Jul 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. E. R. Schendel, S. V. Pendse, J. Jenkins, D. A. Boyuka, II, Z. Gong, S. Lakshminarasimhan, Q. Liu, H. Kolla, J. Chen, S. Klasky, R. Ross, and N. F. Samatova. Isobar hybrid compression-i/o interleaving for large-scale parallel i/o optimization. In Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing, HPDC '12, pages 61--72, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. D. P. Schmidt and M. L. Corradini. The internal flow of diesel fuel injector nozzles: A review. International Journal of Engine Research, 2(1):1--22, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  68. Schmidt et al. Assessment of the prediction capabalities of a homogeneous cavitation model fir the collapse characteristics of a vapour-bubble cloud. In WIMRC 3rd International Cavitation Forum, Coventry, U.K., 2011.Google ScholarGoogle Scholar
  69. K. Schneider and O. V. Vasilyev. Wavelet methods in computational fluid dynamics. Annual Review of Fluid Mechanics, 42(1):473--503, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  70. K. Schwaber and M. Beedle. Agile Software Development with Scrum. Prentice Hall PTR, 1st edition, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. J. Sermulins, W. Thies, R. Rabbah, and S. Amarasinghe. Cache aware optimization of stream programs. SIGPLAN Not., 40(7):115--126, June 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. J. M. Shapiro. Embedded image coding using zerotrees of wavelet coefficients. Signal Processing, IEEE Transactions on, 41(12):3445--3462, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  73. I. B. G. team. Design of the ibm blue gene/q compute chip. IBM Journal of Research and Development, 57(1/2):1:1--1:13, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Y. Tomita and A. Shima. Mechanisms of impulsive pressure generation and damage pit formation by bubble collapse. Journal of Fluid Mechanics, 169:535--564, Aug. 1986.Google ScholarGoogle ScholarCross RefCross Ref
  75. Y. Utturkar, J. Wu, G. Wang, and W. Shyy. Recent progress in modeling of cryogenic cavitation for liquid rocket propulsion. Progress in Aerospace Sciences, 41(7):558--608, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  76. O. Vasilyev and C. Bowman. Second-generation wavelet collocation method for the solution of partial differential equations. Journal of Computational Physics, 165(2):660--693, DEC 10 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  77. T. Wen, J. Su, P. Colella, K. Yelick, and N. Keen. An adaptive mesh refinement benchmark for modern parallel programming languages. In Proceedings of the 2007 ACM/IEEE conference on Supercomputing, SC '07, pages 1--12. ACM, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  78. B. Wendroff. Approximate Riemann solvers, Godunov schemes and contact discontinuities. In Toro, EF, editor, Godunov Methods: Theory and Applications, pages 1023--1056, 233 Spring St, New York, NY 10013 USA, 2001. London Math Soc, Kluwer Academic/Plenum Publ.Google ScholarGoogle ScholarCross RefCross Ref
  79. S. Williams, A. Waterman, and D. Patterson. Roofline: an insightful visual performance model for multicore architectures. Commun. ACM, 52:65--76, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  80. J. Williamson. Low-Storage Runge-Kutta Schemes. Journal of Computational Physics, 35(1):48--56, 1980.Google ScholarGoogle ScholarCross RefCross Ref
  81. G. Wilson. Where's the real bottleneck in scientific computing? American Scientist, 2006.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Conferences
    SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
    November 2013
    1123 pages
    ISBN:9781450323789
    DOI:10.1145/2503210
    • General Chair:
    • William Gropp,
    • Program Chair:
    • Satoshi Matsuoka

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    • Published: 17 November 2013

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