DOI QR코드

DOI QR Code

AMG-CG method for numerical analysis of high-rise structures on heterogeneous platforms with GPUs

  • Li, Zuohua (School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Shan, Qingfei (School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Ning, Jiafei (School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Li, Yu (School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Guo, Kaisheng (School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Teng, Jun (School of Civil and Environmental Engineering, Harbin Institute of Technology)
  • 투고 : 2020.12.14
  • 심사 : 2022.02.08
  • 발행 : 2022.02.25

초록

The degrees of freedom (DOFs) of high-rise structures increase rapidly due to the need for refined analysis, which poses a challenge toward a computationally efficient method for numerical analysis of high-rise structures using the finite element method (FEM). This paper presented an efficient iterative method, an algebraic multigrid (AMG) with a Jacobi overrelaxation smoother preconditioned conjugate gradient method (AMG-CG) used for solving large-scale structural system equations running on heterogeneous platforms with parallel accelerator graphics processing units (GPUs) enabled. Furthermore, an AMG-CG FEM application framework was established for the numerical analysis of high-rise structures. In the proposed method, the coarsening method, the optimal relaxation coefficient of the JOR smoother, the smoothing times, and the solution method for the coarsest grid of an AMG preconditioner were investigated via several numerical benchmarks of high-rise structures. The accuracy and the efficiency of the proposed FEM application framework were compared using the mature software Abaqus, and there were speedups of up to 18.4x when using an NVIDIA K40C GPU hosted in a workstation. The results demonstrated that the proposed method could improve the computational efficiency of solving structural system equations, and the AMG-CG FEM application framework was inherently suitable for numerical analysis of high-rise structures.

키워드

과제정보

The research described in this paper was financially supported by the National Natural Science Foundation of China [grant number 51921006, 51978224]; China Major Development Project for Scientific Research Instrument [grant number 51827811]; the Shenzhen Technology Innovation Program [grant number JCYJ20180508152238111, JCYJ20200109112803851].

참고문헌

  1. Ahmad, H.R., Namdari, N., Cao, M. and Bayat, M. (2019), "Seismic investigation of pushover methods for concrete piers of curved bridges in plan", Comput. Concrete, 23(1), 1-10. https://doi.org/10.12989/cac.2019.23.1.001.
  2. Ahmadi, H.R., Mahdavi, N. and Bayat, M. (2020), "Applying adaptive pushover analysis to estimate incremental dynamic analysis curve", J. Earthq. Tsunami, 14(4), 2050016. https://doi.org/10.1142/S1793431120500165.
  3. Balamonica, K., Gopalakrishnan, N. and Ramamohan Rao, A. (2020), "Seismic analysis of structures subjected to spatially varying earthquake using POD vectors: Experimental and analytical studies", J. Earthq. Tsunami, 14(4), 2050017. https://doi.org/10.1142/S1793431120500177.
  4. Benzi, M. (2002), "Preconditioning techniques for large linear systems: A survey", J. Comput. Phys., 182, 418-477. https://doi.org/10.1006/jcph.2002.7176.
  5. Berger-Vergiat, L., Waisman, H., Hiriyur, B., Tuminaro, R. and Keyes, D. (2012), "Inexact Schwarz-algebraic multigrid preconditioners for crack problems modeled by extended finite element methods", Int. J. Numer. Meth. Eng., 90(3), 311-328. https://doi.org/10.1002/nme.3318.
  6. Bouras, A. and Fraysse, V. (2005), "Inexact matrix-vector products in Krylov methods for solving linear systems: A relaxation strategy", SIAM J. Matrix Anal. Appl., 26(3), 660-678. https://doi.org/10.1137/S0895479801384743.
  7. Boyle, J., Mihajlovic, M. and Scott, J. (2010), "HSL_MI20: An efficient AMG preconditioner for finite element problems in 3D", Int. J. Numer. Method. Eng., 82(1), 64-98. https://doi.org/10.1002/nme.2758.
  8. Brandt, A. (1977), "Multi-level adaptive solutions to boundary-value problems", Math. Comput., 31(138), 333-390. https://doi.org/10.1090/S0025-5718-1977-0431719-X.
  9. Brandt, A. (1986), "Algebraic multigrid theory: The symmetric case", Appl. Math. Comput., 19(1), 23-56. https://doi.org/10.1016/0096-3003(86)90095-0.
  10. Brandt, A., McCormick, S. and Ruge, J. (1982), "Algebraic multigrid (AMG) for automatic algorithm design and problem solution", Colorado State University, Fort Collins, FC, USA.
  11. Brandt, A., McCormick, S. and Ruge, J. (1984), "Algebraic multigrid (AMG) for sparse matrix equations", Cambridge University Press, Cambridge, UK.
  12. Brezina, M., Tong, C. and Becker, R. (2006), "Parallel algebraic multigrids for structural mechanics", SIAM J. Sci. Comput., 27(5), 1534-1554. https://doi.org/10.1137/040608271.
  13. Clees, T. and Ganzer, L. (2010), "An efficient algebraic multi-grid solver strategy for adaptive implicit methods in oil-reservoir simulation", SPE J., 15(3), 670-681. https://doi.org/10.2118/105789-PA.
  14. Clough, R.W. (1960). "The finite element method in plane stress analysis", Proceedings of 2nd ASCE Conference on Electronic Computation, Pittsburgh Pa, September.
  15. Cremon, M.A., Castelletto, N. and White, J.A. (2020), "Multistage preconditioners for thermal-compositional-reactive flow in porous media", J. Comput. Phys., 418, 109607. https://doi.org/10.1016/j.jcp.2020.109607.
  16. Erlangga, Y. and Nabben, R. (2009), "Algebraic multilevel krylov methods", SIAM J. Sci. Comput., 31(5), 3417-3437. https://doi.org/10.1137/080731657.
  17. Falgout, R.D. (2006), "An introduction to algebraic multigrid", Comput. Sci. Eng., 8(6), 24-33. https://doi.org/10.1109/mcse.2006.105.
  18. Falgout, R.D. (2010), "Multigrid methods", Numer. Linear Algebra Appl., 17(2), 175-178. https://doi.org/10.1002/nla.712.
  19. Franceschini, A., Paludetto Magri, V.A., Mazzucco, G., Spiezia, N. and Janna, C. (2019), "A robust adaptive algebraic multigrid linear solver for structural mechanics", Comput. Method. Appl. Mech. Eng., 352, 389-416. https://doi.org/10.1016/j.cma.2019.04.034.
  20. Fujita, K., Horikoshi, M., Ichimura, T., Meadows, L., Nakajima, K., Hori, M. and Maddegedara, L. (2020), "Development of element-by-element kernel algorithms in unstructured finiteelement solvers for many-core wide-SIMD CPUs: Application to earthquake simulation", J. Comput. Sci., 45, 101174. https://doi.org/10.1016/j.jocs.2020.101174.
  21. Gaspar, F.J. and Rodrigo, C. (2017), "On the fixed-stress split scheme as smoother in multigrid methods for coupling flow and geomechanics", Comput. Method. Appl. Mech. Eng., 326, 526-540. https://doi.org/10.1016/j.cma.2017.08.025.
  22. Hackbusch, W. (1985), Multi-grid Methods and Applications, Spring-Verlag Berlin Heidelberg, New York, NY, USA.
  23. Hadjidimos, A. (1978), "Accelerated overrelaxation method", Math. Comput., 32(141), 149-157. https://doi.org/10.1090/S0025-5718-1978-0483340-6.
  24. Helou, M. and Kara, S. (2018), "Design, analysis and manufacturing of lattice structures: an overview", Int. J. Comput. Integrated Manuf., 31(3), 243-261. https://doi.org/10.1080/0951192X.2017.1407456.
  25. Iakymchuk, R., Barreda, M., Wiesenberger, M., Aliaga, J.I. and Quintana-Orti, E.S. (2020), "Reproducibility strategies for parallel preconditioned conjugate gradient", J. Comput. Appl. Math., 371, 112697. https://doi.org/10.1016/j.cam.2019.112697.
  26. Islam, A.B.M.S. (2020), "Computer aided failure prediction of reinforced concrete beam", Comput. Concrete, 25(1), 67-73. https://doi.org/10.12989/cac.2020.25.1.067.
  27. Iwamura, C., Costa, F.S., Sbarski, I., Easton, A. and Li, N. (2003), "An efficient algebraic multigrid preconditioned conjugate gradient solver", Comput. Method. Appl. Mech. Eng., 192(20), 2299-2318. https://doi.org/https://doi.org/10.1016/S0045-7825(02)00378-X.
  28. Jiao, Y.Y., Zhao, Q., Wang, L., Huang, G.H. and Tan, F. (2019), "A hybrid MPI/OpenMP parallel computing model for spherical discontinuous deformation analysis", Comput. Geotech., 106, 217-227. https://doi.org/10.1016/j.compgeo.2018.11.004.
  29. Kim, J.M., Son, K., Yoo, Y., Lee, D. and Kim, D.Y. (2018), "Identifying risk indicators of building damage due to typhoons: Focusing on cases of South Korea", Sustain., 10(11), 3947. https://doi.org/10.3390/su10113947.
  30. Koric, S. and Gupta, A. (2016), "Sparse matrix factorization in the implicit finite element method on petascale architecture", Comput. Meth. Appl. Mech. Eng., 302, 281-292. https://doi.org/10.1016/j.cma.2016.01.011.
  31. Langston, M.H., Harris, M.T., Letourneau, P.D., Lethin, R. and Ezick, J. (2019). "Combinatorial Multigrid: Advanced preconditioners for Ill-Conditioned linear systems", 2019 IEEE High Performance Extreme Computing Conference, Waltham, September.
  32. Li, H., Li, Z. and Teng, J. (2016), "A dynamic analysis algorithm for RC frames using parallel GPU strategies", Comput. Concrete, 18(5), 1019-1039. https://doi.org/10.12989/cac.2016.18.5.1019.
  33. Li, J.W., Bose, M., Wyss, M., Wald, D.J., Hutchison, A.A., Clinton, J.F., Wu, Z.L., Jiang, C.S. and Zhou, S.Y. (2020), "Estimating rupture dimensions of three major earthquakes in Sichuan, China, for early warning and rapid loss estimates", Bull. Seismol. Soc. Am., 110(2), 920-936. https://doi.org/10.1785/0120190117.
  34. Li, M.X. and Wang, G.X. (2018), "Loss assessment of group residential buildings and information management system for wind catastrophe", J. Wind Eng. Ind. Aerodyn., 174, 141-151. https://doi.org/10.1016/j.jweia.2017.12.028.
  35. Liu, H., Wang, K. and Chen, Z. (2016), "A family of constrained pressure residual preconditioners for parallel reservoir simulations", 23(1), 120-146. https://doi.org/10.1002/nla.2017.
  36. Lu, X.Z., Han, B., Hori, M., Xiong, C. and Xu, Z. (2014), "A coarse-grained parallel approach for seismic damage simulations of urban areas based on refined models and GPU/CPU cooperative computing", Adv. Eng. Softw., 70, 90-103. https://doi.org/10.1016/j.advengsoft.2014.01.010.
  37. Mccormick, S. and Ruge, J. (1989), "Algebraic multigrid methods applied to problems in computational structural mechanics", State-of-the-Art Surveys on Computational Mechanics, New York, January.
  38. Mirfakhraei, S.F., Ahmadi, H.R. and Chan, R. (2020), "Numerical and experimental research on actuator forces in toggled active vibration control system (Part I: Numerical)", Smart Struct. Syst., 25(2), 229-240. https://doi.org/10.12989/sss.2020.25.2.229.
  39. Mishev, I.D., Shaw, J.S. and Lu, P. (2011). "Numerical experiments with AMG solver in reservoir simulation", Proceedings of SPE Reservoir Simulation Symposium, Woodlands, February.
  40. Mittal, S. and Vetter, J.S. (2015), "A survey of CPU-GPU heterogeneous computing techniques", ACM Comput. Surv., 47(4), 1-35. https://doi.org/10.1145/2788396.
  41. Moallemi, S. and Pietruszczak, S. (2018), "Numerical analysis of propagation of macrocracks in 3D concrete structures affected by ASR", Comput. Concrete, 22(1), 1-10. https://doi.org/10.12989/cac.2018.22.1.001.
  42. Naumov, M., Arsaev, M., Castonguay, P., Cohen, J., Demouth, J., Eaton, J., Layton, S., Markovskiy, N., Reguly, I., Sakharnykh, N., Sellappan, V. and Strzodka, R. (2015), "AmgX: A library for GPU accelerated algebraic multigrid and preconditioned iterative methods", SIAM J. Sci. Comput., 37(5), S602-S626. https://doi.org/10.1137/140980260.
  43. Notay, Y. (2010), "An aggregation-based algebraic multigrid method", Elect. Transac. Numer. Anal., 37(6), 123-146.
  44. Ruge, J. (1986), "AMG for problems of elasticity", Appl. Math. Comput., 19(1), 293-309. https://doi.org/10.1016/0096-3003(86)90109-8.
  45. Ruge, J.W. and Stuben, K. (1987), Algebraic Multigrid, SIAM, Philadelphia, PA, USA.
  46. Shamsi, J. (2017), "Advancements in GPGPU computing", 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies, Karachi, April.
  47. Sojka, R., Horak, D., Hapla, V. and Cermak, M. (2018), "The impact of enabling multiple subdomains per MPI process in the TFETI domain decomposition method", Appl. Math. Comput., 319, 586-597. https://doi.org/10.1016/j.amc.2017.07.031.
  48. Sotoudehnia, E., Shahabian, F. and Sani, A.A. (2019), "An iterative method for damage identification of skeletal structures utilizing biconjugate gradient method and reduction of search space", Smart Struct. Syst., 23(1), 45-60. https://doi.org/10.12989/sss.2019.23.1.045.
  49. Stuben, K. (1999), "Algebraic multigrid (AMG): An introduction with applications, german national research center for information technology", Augustin, Germany.
  50. Stuben, K. (2001a), "An introduction to algebraic multigrid, german national research centre for information technology", Augustin, Germany.
  51. Stuben, K. (2001b), "A review of algebraic multigrid", J. Comput. Appl. Math., 128(1), 281-309. https://doi.org/10.1016/B978-0-444-50617-7.50015-X.
  52. Sun, X. and Jing, X. (2016), "Analysis and design of a nonlinear stiffness and damping system with a scissor-like structure", Mech. Syst. Signal Proc., 66, 723-742. https://doi.org/10.1016/j.ymssp.2015.05.026.
  53. Tatebe, O. (1993), "The multigrid preconditioned conjugate gradient method", The Sixth Copper Mountain Conference on Multigrid Methods, Colorado, April.
  54. Tian, R., Zhou, M., Wang, J., Li, Y., An, H., Xu, X., Wen, L., Wang, L., Xu, Q. and Leng, J. (2019), "A challenging dam structural analysis: Large-scale implicit thermo-mechanical coupled contact simulation on Tianhe-II", Comput. Mech., 63(1), 99-119. https://doi.org/10.1007/s00466-018-1586-5.
  55. Wang, L.J., Deng, Q.C. and Xie, Y.X. (2017), "A new conjugate gradient algorithm for solving dynamic load identification", Struct. Eng. Mech., 64(2), 271-278. https://doi.org/10.12989/sem.2017.64.2.271.
  56. Wang, Y. and Killough, J.E. (2015), "Solver preconditioning using the combinatorial multilevel method on reservoir simulation", Comput. Geosci., 19(4), 695-708. https://doi.org/10.1007/s10596-015-9485-8.
  57. Wathen, A.J. (2015), "Preconditioning", Acta Numer., 24, 329-376. https://doi.org/10.1017/S0962492915000021.
  58. Xiao, S.J., Xu, L.H. and Li, Z.X. (2019), "Seismic performance and damage analysis of RC frame-core tube building with self-centering braces", Soil Dyn. Earthq. Eng., 120, 146-157. https://doi.org/10.1016/j.soildyn.2019.01.029.
  59. Xu, J. and Zikatanov, L. (2017), "Algebraic multigrid methods", Acta Numer., 26, 591-721. https://doi.org/10.1017/s0962492917000083.
  60. Xu, L.H., Xiao, S.J. and Lu, X. (2018), "Seismic response analysis of RC frame core-tube building with self-centering braces", Struct. Monit. Maint., 5(2), 189-204. https://doi.org/10.12989/smm.2018.5.2.189.
  61. Xu, X.W. (2019), "Parallel algebraic multigrid methods: State-of-the art and challenges for extreme-scale applications", J. Numer. Meth. Comput. Appl., 40(4), 243-260. https://doi.org/10.1007/3-540-31619-1_6.
  62. Xu, X.W., Mo, Z.Y. and An, H.B. (2016), "An adaptive AMG preconditioning strategy for solving large-scale sparse linear systems", Scientia Sinica Informationis, 46(10), 1411-1420. https://doi.org/10.1360/N112016-00074.
  63. Yamaguchi, T., Kawase, Y., Nagase, A. and Ishimura, S. (2019), "Performance evaluation of 3-D hybrid parallel finite element method by MPI/OpenMP", J. Japan Soc. Appl. Elect. Mech., 27(1), 85-90. https://doi.org/10.14243/jsaem.27.85.
  64. Zhao, B., Liu, Y., Goh, S. and Lee, F. (2016), "Parallel finite element analysis of seismic soil structure interaction using a PC cluster", Comput. Geotech., 80, 167-177. https://doi.org/10.1016/j.compgeo.2016.07.006.