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Peta-scale phase-field simulation for dendritic solidification on the TSUBAME 2.0 supercomputer

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Published:12 November 2011Publication History

ABSTRACT

The mechanical properties of metal materials largely depend on their intrinsic internal microstructures. To develop engineering materials with the expected properties, predicting patterns in solidified metals would be indispensable. The phase-field simulation is the most powerful method known to simulate the micro-scale dendritic growth during solidification in a binary alloy. To evaluate the realistic description of solidification, however, phase-field simulation requires computing a large number of complex nonlinear terms over a fine-grained grid. Due to such heavy computational demand, previous work on simulating three-dimensional solidification with phase-field methods was successful only in describing simple shapes. Our new simulation techniques achieved scales unprecedentedly large, sufficient for handling complex dendritic structures required in material science. Our simulations on the GPU-rich TSUBAME 2.0 supercomputer at the Tokyo Institute of Technology have demonstrated good weak scaling and achieved 1.017 PFlops in single precision for our largest configuration, using 4,000 GPUs along with 16,000 CPU cores.

References

  1. T. Aoki, S. Ogawa, and A. Yamanaka. Multiple-GPU scalability of phase-field simulation for dendritic solidification. Progress in Nuclear Science and Technology, in press.Google ScholarGoogle Scholar
  2. W. J. Boettinger, J. A. Warren, C. Beckermann, and A. Karma. Phase-field simulation of solidification. Annual Review of Materials Research, 32(1):163--194, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  3. T. Brandvik and G. Pullan. Acceleration of a 3D Euler Solver using commodity graphics hardware. In 46th AIAA Aerospace Sciences Meeting. American Institute of Aeronautics and Astronautics, January 2008.Google ScholarGoogle ScholarCross RefCross Ref
  4. S. Browne, J. Dongarra, N. Garner, G. Ho, and P. Mucci. A portable programming interface for performance evaluation on modern processors. Int. J. High Perform. Comput. Appl., 14(3):189--204, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. C. Burstedde, O. Ghattas, M. Gurnis, T. Isaac, G. Stadler, T. Warburton, and L. Wilcox. Extreme-scale AMR. In Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC '10, pages 1--12, Washington, DC, USA, 2010. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. L.-Q. Chen. Phase-field models for microstructure evolution. Annual Review of Materials Research, 32(1):113--140, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  7. Y. Cui, K. B. Olsen, T. H. Jordan, K. Lee, J. Zhou, P. Small, D. Roten, G. Ely, D. K. Panda, A. Chourasia, J. Levesque, S. M. Day, and P. Maechling. Scalable earthquake simulation on petascale supercomputers. In Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC '10, pages 1--20, Washington, DC, USA, 2010. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. Eiken. Dendritic growth texture evolution in Mg-based alloys investigated by phase-field simulation. International Journal of Cast Metals Research, 22:86--89(4), August 2009.Google ScholarGoogle ScholarCross RefCross Ref
  9. T. Endo, A. Nukada, S. Matsuoka, and N. Maruyama. Linpack evaluation on a supercomputer with heterogeneous accelerators. In Proceedings of the 24th IEEE International Parallel and Distributed Processing Symposium (IPDPS'10), Atlanta, GA, USA, Apr 2010. IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  10. W. L. George and J. A. Warren. A parallel 3d dendritic growth simulator using the phase-field method. Journal of Computational Physics, 177(2):264--283, 2002.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. T. Hamada and K. Nitadori. 190 TFlops astrophysical N-body simulation on a cluster of gpus. In Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC '10, pages 1--9, New Orleans, LA, USA, 2010. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. S. G. Kim, W. T. Kim, and T. Suzuki. Phase-field model for binary alloys. Phys. Rev. E, 60(6):7186--7197, Dec 1999.Google ScholarGoogle ScholarCross RefCross Ref
  13. T. Kim and M. C. Lin. Visual simulation of ice crystal growth. In Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation, SCA '03, pages 86--97, Aire-la-Ville, Switzerland, Switzerland, 2003. Eurographics Association. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. R. Kobayashi. Modeling and numerical simulations of dendritic crystal growth. Physica D: Nonlinear Phenomena, 63(3-4):410--423, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. C. Linford, J. Michalakes, M. Vachharajani, and A. Sandu. Multi-core acceleration of chemical kinetics for simulation and prediction. In SC '09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, pages 1--11, New York, NY, USA, 2009. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. Michalakes and M. Vachharajani. GPU acceleration of numerical weather prediction. In IPDPS, pages 1--7. IEEE, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  17. B. Nestler. A 3D parallel simulator for crystal growth and solidification in complex alloy systems. Journal of Crystal Growth, 275(1-2):e273 -- e278, 2005. Proceedings of the 14th International Conference on Crystal Growth and the 12th International Conference on Vapor Growth and Epitaxy.Google ScholarGoogle ScholarCross RefCross Ref
  18. T. Shimokawabe, T. Aoki, J. Ishida, K. Kawano, and C. Muroi. 145 TFlops performance on 3990 GPUs of TSUBAME 2.0 supercomputer for an operational weather prediction. Procedia Computer Science, 4:1535--1544, 2011. Proceedings of the International Conference on Computational Science, ICCS 2011.Google ScholarGoogle ScholarCross RefCross Ref
  19. T. Shimokawabe, T. Aoki, C. Muroi, J. Ishida, K. Kawano, T. Endo, A. Nukada, N. Maruyama, and S. Matsuoka. An 80-fold speedup, 15.0 TFlops full GPU acceleration of non-hydrostatic weather model ASUCA production code. In Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC '10, pages 1--11, New Orleans, LA, USA, 2010. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. C. Thibault and I. Senocak. CUDA implementation of a Navier-Stokes solver on multi-GPU desktop platforms for incompressible flows. In Proceedings of the 47th AIAA Aerospace Sciences Meeting, number AIAA 2009--758, jan 2009.Google ScholarGoogle ScholarCross RefCross Ref
  21. J. Tiaden, B. Nestler, H. J. Diepers, and I. Steinbach. The multiphase-field model with an integrated concept for modelling solute diffusion. Physica D: Nonlinear Phenomena, 115(1-2):73--86, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. A. Yamanaka, T. Aoki, S. Ogawa, and T. Takaki. GPU-accelerated phase-field simulation of dendritic solidification in a binary alloy. Journal of Crystal Growth, 318(1):40--45, 2011. The 16th International Conference on Crystal Growth (ICCG16)/The 14th International Conference on Vapor Growth and Epitaxy (ICVGE14). Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
        November 2011
        866 pages
        ISBN:9781450307710
        DOI:10.1145/2063384

        Copyright © 2011 ACM

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        Publication History

        • Published: 12 November 2011

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        SC '11 Paper Acceptance Rate74of352submissions,21%Overall Acceptance Rate1,516of6,373submissions,24%

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