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High Performance Computing in Nuclear Engineering

  • Christophe Calvin
  • David Nowak
Reference work entry

Abstract

The aim of this chapter is to give some key points on the use of high performance computing (HPC) in the field of nuclear engineering. This chapter is divided into two main parts. This first one is an introduction to parallel computing. In this first part, we will describe not only the main computer and processor architectures which are used today but also some which are not so usual but which will allow the readers to better understand the key point of parallelism from the hardware point of view. Still in this first part, we will continue by describing the main parallelism models, in the same point of view as the description of the parallel architecture. In Sect. 4, we will give some basic ideas on how to design parallel programs. This section is the last of the first part. The second part is dedicated to the use of high performance computing in nuclear engineering. We will give first the main challenges which can be addressed using HPC. Some of them are illustrated in Sect. 6 on some of the main scientific domains in nuclear engineering (reactor physics, material sciences and thermal-hydraulic). For each of them we have tried to describe the main problems which can be addressed using HPC but some of them remain as scientific and industrial challenges.

Keywords

Direct Numerical Simulation Fuel Element Message Passing Interface High Performance Computing Memory Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Andrade H, Kurc T, Sussman A, Saltz J (2003) Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries. In: Proceedings parallel and distributed processing symposium, Nice, FranceGoogle Scholar
  2. Aybar HS, Ortego P (2005) A review of nuclear fule performance codes. Prog Nucl Energy 46: 127–141CrossRefGoogle Scholar
  3. Barre F, Bernard M (1990) The CATHARE code strategy and assessment. Nucl Eng Des J 124(3): 257–284CrossRefGoogle Scholar
  4. Baskes MI (1992) Modified embedded-atom potentials for cubic materials and impurities. Phys Rev B 46:2727CrossRefGoogle Scholar
  5. Baskes MI, Stan M (2003) An atomistic study of solid/liquid interfaces and phase equilibrium in binary systems. Metall Mater Trans 34A:435–439CrossRefGoogle Scholar
  6. Baskes MI, Muralidharan K, Stan M, Valone SM, Cherne FJ (2003) Using the modified embedded-atom method to calculate the properties of pu-ga alloys. JOM 55:41–50CrossRefGoogle Scholar
  7. Belle J (ed) (1961) Uranium oxide: properties and nuclear applications. Naval Reactors, Division of Reactor Development USAECGoogle Scholar
  8. Bieder U, Calvin C et al (2003) Detailed thermal hydraulic analysis of induced break severe accidents using the massively parallel CFD code Trio_U/PRICELES. In: 5th international conference on supercomputing for nuclear applications (SNA – 2003), 22–24 September 2003, Paris, FranceGoogle Scholar
  9. Billing GD, Mikkelsen KV (1996) Introduction to molecular dynamics and chemical kinetics. Wiley, New YorkGoogle Scholar
  10. Bird RB (2002) Transport phenomena. Wiley, New YorkGoogle Scholar
  11. Brown F, Goorley J, Sweezy J (2003) MCNP5 parallel processing workshop, workshop M&C. http://mcnp-green.lanl.gov/publication/mcnp_publications.html
  12. Bulatov V, Tang M, Zbib HM (2001) Crystal plasticity from dislocation dynamics. Mater Res Soc Bull 26:191–195CrossRefGoogle Scholar
  13. Burkhardt H et al (February 1992) Overview of the KSR1 computer system. Technical Report KSR-TR-9202001. Kendall Square Research, BostonGoogle Scholar
  14. Busker G et al (1999) Solution mechanisms for dopant oxides in yttira. J Am Ceram Soc 82:1553–1559CrossRefGoogle Scholar
  15. Butenhof DR 1997 Programming with POSIX(R) threads. Addison-Wesley Professional Computing SeriesGoogle Scholar
  16. Cahn JW (1961) On spinodal decomposition. Acta Metall 9:795–801CrossRefGoogle Scholar
  17. Calvin C (2003) A development framework for parallel CFD applications: Trio-U project. 5th international conference on supercomputing for nuclear applications (SNA – 2003), 22–24 September 2003, Paris, FranceGoogle Scholar
  18. Calvin C, Cueto O, Emonot P (2002) An object-oriented approach to the design of fluid mechanics software. M2AN 36(5):907–921Google Scholar
  19. Chandra R (2001) Parallel programming in OpenMP. Academic, San DiegoGoogle Scholar
  20. Chapman B et al (2001) Program development environment for OpenMP programs on ccNUMA architectures. In: Large-scale scientific computing. 3rd international symposium, Sozopol, Bulgarie, vol 2179, pp 210–217Google Scholar
  21. Chapman B, Jost G, van der Pas R (2007) Using OpenMP – portable shared memory parallel programming. MIT Press, CambridgeGoogle Scholar
  22. Coulomb F (1997) Parallelization of the DSN multigroup neutron transport equation on the CRAY-T3D using CRAFT. In Proceedings of the 8th SIAM conference on parallel processing for scientific computing, PPSC, Minneapolis, MinnesotaGoogle Scholar
  23. Cristea P, Stan M, Ramirez JC (2007) Point defects and oxygen diffusion in fluorite type oxides. J Optoelectr Adv Mater 9:1750–1756Google Scholar
  24. Dahmani M, Roy R (2005) Parallel solver based on the three-dimensional characteristics method: design and performance analysis. Nucl Sci Eng 150(2):155–169.Google Scholar
  25. Dahmani M, Roy R (2006) Scalability model- ing for deterministic particle transport solvers. Int J High Perform Comput Appl 20(4):541–556CrossRefGoogle Scholar
  26. Do JM et al (2009) Fuel loading pattern for heterogeneous EPR core configuration using a distributed evolutionary algorithm. In: Proceedings of M&C, Saratoga Springs, New YorkGoogle Scholar
  27. Daw MS, Baskes MI (1984) Embedded-atom method: Derivation and application to impurities and other defects in metals. Phys Rev B 29:6443CrossRefGoogle Scholar
  28. Deng L, Xie Z-S (1999) Parallelization of MCNP Monte Carlo neutron and photon transport code in parallel virtual machine and message passing interface. J Nucl Sci Technol 36(7):626–629Google Scholar
  29. Duncan R (1990) A survey of parallel computer architectures. IEEE Comput February:5–16Google Scholar
  30. Feautrier P (2006) Automatic parallelization in the polytope model. In: The data parallel programming model. LCNS. SpringerGoogle Scholar
  31. Fink JK (2000) Thermophysical properties of unranium dioxide. J Nucl Mater 279:1–18CrossRefGoogle Scholar
  32. Flynn M (1972) Some computer organizations and their effectiveness. IEEE Trans Comput C-21:948Google Scholar
  33. Foulkes WMC, Mitas L, Needs RJ, Rajagopal G (2005) Quantum monte carlo simulations of solids. Rev Mod Phys 73:33–83CrossRefGoogle Scholar
  34. Frank W, Elsasser C, Fahnle M (1995) AB initio force-constant method for phonon dispersions in alkali metals. Phys Rev Lett 74:1791–1794CrossRefGoogle Scholar
  35. FRAPCON simulation code. http://www.pnl.gov/frapcon3/
  36. Frenkel D, Smit B (2002) Understanding molecular dynamics. Academic Press, San DiegoGoogle Scholar
  37. Gannon DB, Rosendale JV (1984) On the impact of communication complexity on the design of parallel numerical algorithms. IEEE Trans Comput 33:1180–1194MATHCrossRefGoogle Scholar
  38. Ginzburg VL, Landau LD (1950) On the theory of superconductivity. Zh Eksp Teor Fiz 20:1064Google Scholar
  39. Glicksman ME (2000) Diffusion in solids. Wiley, New YorkGoogle Scholar
  40. Golfier H et al (2009) APOLLO3: a common project of CEA, AREVA and EDF for the development of a new deterministic multi-purpose code for core physics analysis. In: Proceedings of M&C, Saratoga Springs, New YorkGoogle Scholar
  41. Goorley T, Brown F, Cox LJ (2003) MCNP 5TM improvements for windows PCs. In: Nuclear mathematical and computational sciences, Gatlinburg, Tennessee, 6–11 April 2003 (CD-ROM). American Nuclear Society, LaGrange ParkGoogle Scholar
  42. Gschwind M et al (2006) Synergistic processing in cell’s multicore architecture. IEEE Micro 26(2):10–24CrossRefGoogle Scholar
  43. Guérin P, Baudron A-M, Lautard J-J (2005) A component mode synthesis method for 3D cell by cell SPn core calculation using the mixed dual finite element solver MINOS. M&C, Avignon, FranceGoogle Scholar
  44. Guérin P, Baudron A-M, Lautard J-J (2006) Component mode synthesis methods applied to 3D heterogeneous core calculations, using the mixed dual finite element solver MINOS. PHYSOR, Vancouver, CanadaGoogle Scholar
  45. Guérin P, Baudron A-M, Lautard J-J (2007) Domain decomposition methods for core calculations using the MINOS solver. M&C, Arignon, FranceGoogle Scholar
  46. Han Gyu J et al (2004) Methods and performance of a three-dimensional whole-core transport code DeCART. PHYSOR, Chicago, USAGoogle Scholar
  47. Hecker SS, Stan M (2008) Plutonium metallic fuels for fast reactors. J Nucl Mater 383:112–118CrossRefGoogle Scholar
  48. Hellekalek P (July 1998) Don’t trust parallel Monte Carlo! ACM SIGSIM Simul Digest Arch 28(1)Google Scholar
  49. Herrero JJ, Ahnert C, Aragonés JM (2007) Spatial domain decomposition for LWR cores at the pin scale. ANS Winter meetingGoogle Scholar
  50. Hillert M (1998) Phase equilibria, phase diagrams and phase transformations. Cambridge University Press, New YorkGoogle Scholar
  51. Hillis D (1982) New computer architectures and their relationship to physics or why CS is no good. Int J Theor Phys 21(3/4):255–262CrossRefGoogle Scholar
  52. Hirth JP, Rhee M, Zbib HM (1996) Modelling of deformation by a 3D simulation of multipole, curved dislocations. J Comput-Aided Mater Des 3:164–166CrossRefGoogle Scholar
  53. Hong S, Kim H (June 2009) An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness. ACM SIGARCH Comput Archit News Arch 37(3):152MathSciNetCrossRefGoogle Scholar
  54. H.P.F. Forum (1993) High Performance Fortran language specification (version 1.0). http://www.netlib.org/hpf/
  55. Hu SY, Baskes MI, Stan M, Tome C (2007) Phase-field modeling of microvoid evolution under elastic-plastic deformation. Appl Phys Lett 90:81921–81923CrossRefGoogle Scholar
  56. Hu S, Henager Jr CH, Heinisch HL, Stan M, Baskes MI, Valone SM (2009) Phase-field modeling of gas bubbles and thermal conductivity evolution in nuclear fuels. J Nucl Mater 392:292–300CrossRefGoogle Scholar
  57. Hugot FX, Lee YK, Malvagi F (2008) Recent R&D around the Monte Carlo code Tripoli4 for criticality calculations. In: Proceedings of PHYSOR conference, Interlaken, SwitzerlandGoogle Scholar
  58. Hwang K (1993) Advanced computer architecture – parallelism, scalability, programmability. McGraw-Hill/MIT Press, New York/CambridgeGoogle Scholar
  59. Incopera FP, DeWitt DP (1996) Fundamentals of heat and mass transfer. Wiley, New YorkGoogle Scholar
  60. Janek J, Timm H (1998) Thermal diffusion and Soret effect in (U,Me)O2 + δ: the heat of transport of oxygen. J Nucl Mater 255:116–127CrossRefGoogle Scholar
  61. Karma A (2001) Phase-field formulation for quantitative modeling of alloy solidification. Phys Rev Lett 87:115701CrossRefGoogle Scholar
  62. Kaufman L, Bernstein H (1970) Computer calculations of phase diagrams. Academic, New YorkGoogle Scholar
  63. Kim KC, Olander DR (1981) J Nucl Mater 102:192CrossRefGoogle Scholar
  64. King CT, Chu WH, Ni LM (1988) Pipelined data parallel algorithms – concept and modeling. In: Proceedings of the international conference on supercomputing, Saint-Malo, France, pp 385–395Google Scholar
  65. Kohn W, Sham LJ (1965) Phys Rev 140:A1133MathSciNetCrossRefGoogle Scholar
  66. Korte C, Janek J, Timm H (1997) Transport processes in temperature gradients: thermal diffusion and Soret effect in crystalline solids. Solid State Ionics 101/103:465–470Google Scholar
  67. Kunc K, Martin RM (1982) Ab initio force constants of GaAs: a new approach to calculation of phonons and dielectric properties. Phys Rev Lett 48:406–409CrossRefGoogle Scholar
  68. Laucoin É, Calvin C (2004) A parallel front-tracking method for two-phase flows simulations. Parallel Cfd, Gran Canaria Las Palmas City, SpainGoogle Scholar
  69. Leighton FT (1992) Introduction to parallel algorithms and architectures: arrays – trees – hypercubes. Morgan Kaufman Publishers, San MateoMATHGoogle Scholar
  70. Lepinoux J, Kubin LP (1987) The dynamic organization of dislocation structures: A simulation. Scr Metall 21:833–838CrossRefGoogle Scholar
  71. Lewis TG, Payne WH (July 1973) Generalized feedback shift register pseudorandom number algorithm. ACM 20(3):456–468MATHCrossRefGoogle Scholar
  72. Lindemer TB, Besmann TM (1985) Chemical thermodynamic representation of ¡PuO2-x¿ and ¡U1-zPuzOw¿. J Nucl Mater 130:473CrossRefGoogle Scholar
  73. Lyons MF et al (1965) Trans Am Nucl Soc 8:42Google Scholar
  74. March NH (1992) Electron density theory of atoms and molecules. Academic, New YorkGoogle Scholar
  75. Martin RM (2004) Electronic structure. Cambridge University Press, New YorkMATHGoogle Scholar
  76. Metcalf M, Reid J (1999) FORTRAN 90/95 explained, 2nd edn. Oxford University Press, OxfordMATHGoogle Scholar
  77. Metropolis N, Ulam S (1949) The Monte Carlo method. J Am Statist Assoc 44:335–341MathSciNetMATHGoogle Scholar
  78. Olander DR (1976) Fundamental aspects of nuclear reactor fuel elements. TID-26711-P1. Technical Information Service, U.S. Department of Commerce, Springfield, VirginiaGoogle Scholar
  79. Palmiotti G et al (2007) UNÌC: Ultimate Neutronic Investigation Code. M&C, Monterey, USAGoogle Scholar
  80. Phillips RB (2001) Crystals, defects, and microstructures. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  81. Procassini R, O’Brien M, Taylor J (2005) Load balancing of parallel Monte Carlo transport calculations. M&C, Avignon, FranceGoogle Scholar
  82. Rabenseifner R et al (2006) Hybrid MPI and OpenMP parallel programming. In: Recent advances in parallel virtual machine and message passing interface. LNCS. Springer, BerlinGoogle Scholar
  83. Ragusa J (April 2003a) Implementation of multithreaded computing in the neutronics FEM solver Minos. In: Proceedings of the ANS mathematics and computations international conference, Gatlinburg, TennesseeGoogle Scholar
  84. Ragusa J (September 2003b) Application of multithread computing and domain decomposition to the 3-D neutronics FEM code CRONOS. In: International conference on supercomputing in nuclear applications, Paris, FranceGoogle Scholar
  85. Ramirez JC, Stan M, Cristea P (2006) Simulations of heat and oxygen diffusion in UO2 nuclear fuel. rods. J Nucl Mater 359:174–184CrossRefGoogle Scholar
  86. Reddy JN, Gartling DK (2001) The finite element method in heat transfer and fluid dynamics. CRC Press, LLC, Boca RatonGoogle Scholar
  87. Roadrunner supercomputer. http://www.lanl.gov/roadrunner/
  88. Roosta SH (2000) Parallel processing and parallel algorithms: theory and computation. Springer, LCNSMATHCrossRefGoogle Scholar
  89. Roy R, Stankovski Z (1997) Parallelization of neutron transport solvers. In: Recent advances in parallel virtual machine and message passing interface. LCNS, vol 1332, pp 494–501Google Scholar
  90. Saad Y, Schultz MH (1989) Data communication in parallel architectures. Parallel Comput 11: 13–150MathSciNetCrossRefGoogle Scholar
  91. Samaras M, Victoria M, Hoffelner W (2009) Nuclear energy materials prediction: application of the multiscale modelling paradigm. Nucl Eng Technol 41:1–10CrossRefGoogle Scholar
  92. Sandler SI (1999) Chemical engineering and thermodynamics. Wiley, New YorkGoogle Scholar
  93. Simulation and modeling for advanced nuclear energy systems workshop. https://www.cels. anl.gov/events/workshops/anes/Google Scholar
  94. Singh JP et al (1993) An empirical comparison of the Kendall Square Research KSR-1 and Stanford DASH multiprocessors. In: Proceedings of the 1993 ACM/IEEE conference on supercomputing, Portland, OregonGoogle Scholar
  95. Singleton J (2001) Band theory and electronic properties of solids. Oxford University Press, OxfordGoogle Scholar
  96. Sjoden GE (November 1997) PENTRAN: a parallel 3-D S(N) transport code with complete phase space decomposition, adaptive differencing, and iterative solution methods. PhD thesis. The Pennsylvania State University, Source DAI-B 58/05, p. 2652. 301 ppGoogle Scholar
  97. Smith BF, Bjorstad P, Gropp W (1996) Domain decomposition – parallel multilevel methods for elliptic partial differential equations. Cambridge University Press, New YorkMATHGoogle Scholar
  98. Sounders N, Miodownik AP (1998) CALPHAD. Elsevier Science Limited, New YorkGoogle Scholar
  99. Stacey WM (2001) Nuclear reactor physics. Wiley, New YorkGoogle Scholar
  100. Stan M (2005) Materials models and simulations in support of nuclear fuels development. Los Alamos National Laboratory Report. LA-UR-05–5652Google Scholar
  101. Stan M (2009) Multi-Scale models and simulations of nuclear fuels. J Nucl Eng Technol 41:39–52CrossRefGoogle Scholar
  102. Stan M, Cristea P (2005) Thermochemistry of defects and oxygen diffusion in PuO2-x. J Nucl Mater 344:213–218CrossRefGoogle Scholar
  103. Stan M, Reardon B (2003) CALPHAD J 27:319CrossRefGoogle Scholar
  104. Stan M, Armstrong TJ, Butt DP, Wallace Sr TC, Park YS, Haertling CL, Hartmann T, Hanrahan Jr RJ (2002) Stability of the perovskite compounds in the Ce-Ga-O and Pu-Ga-O systems. J Am Ceram Soc 85:2811–2816CrossRefGoogle Scholar
  105. Stan M, Ramirez JC, Cristea P et al (2007) Models and simulations of nuclear fuel materials properties. J Alloys Comp 444–445:415–423CrossRefGoogle Scholar
  106. Stan M et al (2009) Discovery and design of nuclear fuels. J Nucl Mater, in pressGoogle Scholar
  107. Stankovski Z, Puill A, Dullier L (1997) Advanced plutonium assembly parallel calculations using the APOLLO2 code. M&C, Saratoga, USAGoogle Scholar
  108. Sutton AP (1993) Electronic structure of materials. Oxford University Press, OxfordGoogle Scholar
  109. Tanenbaum A (1992) Modern operating systems. Prentice-Hall, Englewood CliffsMATHGoogle Scholar
  110. The MPI Forum (1993) MPI: a message passing interface. Technical Report. University of Tennessee, KnoxvilleGoogle Scholar
  111. Trama JC (2008) Overview of TRIPOLI-4.5. In: Proceedings of ICRS conference, Pine MountainGoogle Scholar
  112. Trama JC, Hugot FX (2007) TRIPOLI-4: parallelism capability. ANS Winter meetingGoogle Scholar
  113. Trew A, Wilson G (1991) Past, present, parallel: a survey of available parallel computing systems. Springer-Verlag, New YorkMATHGoogle Scholar
  114. Tucker LW, Robertson GG (August 1988) Architecture and applications of the connection machine. Computer 21(8):26–38CrossRefGoogle Scholar
  115. United States Nuclear Regulatory Commission, Emergency Preparedness. http://www.nrc.gov/about-nrc/emerg-preparedness/images/fuel-pellet-assembly.jpg
  116. Van der Giessen E, Needleman A (1995) Discrete dislocation plasticity: a simple planar model. Model. Simul. Mater Sci Eng 3:689–735CrossRefGoogle Scholar
  117. Voter AF (2005) Introduction to the kinetic Monte Carlo method. In: Sickafus KE, Kotomin EA (eds) Radiation effects in solids. Springer, NATO Publishing Unit, Dordrecht, The NetherlandsGoogle Scholar
  118. Voter AF, Montalenti F, Germann TC (2002) Extending the time scale in atomisitc simulation of materials. Ann Rev Mater Res 32:321CrossRefGoogle Scholar
  119. Wang HY, LeSar R (1995) O(N) algorithm for dislocation dynamics. Philos Magazine A 71: 149–164CrossRefGoogle Scholar
  120. Wu G, Roy R (2001) Parallelization of characteristics solvers for 3D neutron transport. In: Recent advances in parallel virtual machine and message passing interface. 1, Springer, LCNS, pp 344–351Google Scholar
  121. Zacharie I et al (1998) Springer, J Nucl Mater 255:92–104CrossRefGoogle Scholar
  122. Zeyao M, Lianxiang F (2004) Parallel flux sweep algorithm for neutron transport on unstructured grid. J Supercomput 30(1):5–17MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Christophe Calvin
    • 1
  • David Nowak
    • 2
  1. 1.CEA/DEN/DANS/DM2S CEA SaclayGif-sur-YvetteFrance
  2. 2.Mathematics and Computer Science Division ArgonneNational LaboratoryArgonneUSA

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