JOURNAL BROWSE
Search
Advanced SearchSearch Tips
New GPU computing algorithm for wind load uncertainty analysis on high-rise systems
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Wind and Structures
  • Volume 21, Issue 5,  2015, pp.461-487
  • Publisher : Techno-Press
  • DOI : 10.12989/was.2015.21.5.461
 Title & Authors
New GPU computing algorithm for wind load uncertainty analysis on high-rise systems
Wei, Cui; Luca, Caracoglia;
 Abstract
In recent years, the Graphics Processing Unit (GPU) has become a competitive computing technology in comparison with the standard Central Processing Unit (CPU) technology due to reduced unit cost, energy and computing time. This paper describes the derivation and implementation of GPU-based algorithms for the analysis of wind loading uncertainty on high-rise systems, in line with the research field of probability-based wind engineering. The study begins by presenting an application of the GPU technology to basic linear algebra problems to demonstrate advantages and limitations. Subsequently, Monte-Carlo integration and synthetic generation of wind turbulence are examined. Finally, the GPU architecture is used for the dynamic analysis of three high-rise structural systems under uncertain wind loads. In the first example the fragility analysis of a single degree-of-freedom structure is illustrated. Since fragility analysis employs sampling-based Monte Carlo simulation, it is feasible to distribute the evaluation of different random parameters among different GPU threads and to compute the results in parallel. In the second case the fragility analysis is carried out on a continuum structure, i.e., a tall building, in which double integration is required to evaluate the generalized turbulent wind load and the dynamic response in the frequency domain. The third example examines the computation of the generalized coupled wind load and response on a tall building in both along-wind and cross-wind directions. It is concluded that the GPU can perform computational tasks on average 10 times faster than the CPU.
 Keywords
wind engineering;parallel computing;GPU computing;uncertainty quantification;performance-based wind engineering;tall buildings;
 Language
English
 Cited by
1.
An approach to enhance the performance of large-scale structural analysis on CPU-MIC heterogeneous clusters, Concurrency and Computation: Practice and Experience, 2017, 29, 8, e4033  crossref(new windwow)
2.
A dynamic analysis algorithm for RC frames using parallel GPU strategies, Computers and Concrete, 2016, 18, 5, 1019  crossref(new windwow)
 References
1.
Aly, A.M. (2013), "Pressure integration technique for predicting wind-induced response in high-rise buildings", Alexandria Eng. J., 52(4), 717-731. crossref(new window)

2.
Asanovic, K., Bodik, R., Catanzaro, B.C., Gebis, J.J., Husbands, P., Keutzer, K., Patterson, D.A., Plishker, W.L., Shalf, J., Williams, S.W. et al. (2006), The landscape of parallel computing research: A view from Berkeley, Technical Report UCB/EECS-2006-183, EECS Department, University of California, Berkeley, California, USA.

3.
Barbato, M., Petrini, F., Unnikrishnan, V.U. and Ciampoli, M. (2013), "Performance-Based Hurricane Engineering (PBHE) framework", Struct. Saf., 45, 24-35. crossref(new window)

4.
Bauer, B.A., Davis, J.E., Taufer, M. and Patel, S. (2011), "Molecular dynamics simulations of aqueous ions at the liquid-vapor interface accelerated using graphics processors", J. Comput. Chem., 32(3), 375-385. crossref(new window)

5.
Bernardini, E., Spence, S.M. and Gioffre, M. (2012), "Effects of the aerodynamic uncertainties in HFFB loading schemes on the response of tall buildings with coupled dynamic modes", Eng. Struct., 42, 329-341. crossref(new window)

6.
Cecka, C., Lew, A.J. and Darve, E. (2011), "Assembly of finite element methods on graphics processors", Int. J. Numer. Meth. Eng., 85(5), 640-669. crossref(new window)

7.
Colella, P. (2004), "Defining software requirements for scientific computing", Slide of 2004 presentation included in David Patterson's 2005 talk.

8.
Corrigan, A., Camelli, F.F., Lohner, R. and Wallin, J. (2011), "Running unstructured grid-based CFD solvers on modern graphics hardware", Int. J. Numer. Meth. Fl., 66(2), 221-229. crossref(new window)

9.
Cui, W. and Caracoglia, L. (2015), "Simulation and analysis of intervention costs due to wind-induced damage on tall buildings", Eng. Struct., 87, 183-197. crossref(new window)

10.
Culler, D.E., Singh, J.P. and Gupta, A. (1999), Parallel computer architecture: a hardware/software approach, Gulf Professional Publishing, Houston, Texas, USA.

11.
Dziekonski, A., Sypek, P., Lamecki, A. and Mrozowski, M. (2013), "Generation of large finite-element matrices on multiple graphics processors", Int. J. Numer. Meth. Eng., 94(2), 204-220. crossref(new window)

12.
Faires, D. and Burden, R. (2012), Numerical Methods, Cengage Learning, Boston, MA, USA.

13.
Feng, R., Yan, G. and Ge, J. (2012), "Effects of high modes on the wind-induced response of super high-rise buildings", Earthq. Eng. Eng. Vib., 11(3), 427-434. crossref(new window)

14.
Foundation, F.S. (2014), GNU Scientific Library, URL: http://www.gnu.org/software/gsl/

15.
Gaurav, and Wojtkiewicz, S.F. (2011), "Use of GPU computing for uncertainty quantification in computational mechanics: A case study", Scientific Programming, 19(4), 199-212 crossref(new window)

16.
Georgescu, S., Chow, P. and Okuda, H. (2013), "GPU acceleration for FEM-based structural analysis", Arch. Comput. Method. E., 20(2), 111-121. crossref(new window)

17.
Grigoriu, M. (2002), Stochastic calculus: applications in science and engineering, Birkhauser, Boston, MA, USA.

18.
Huang, S., Li, Q. and Wu, J. (2010), "A general inflow turbulence generator for large eddy simulation", J. Wind Eng. Ind. Aerod., 98(10-11), 600-617. crossref(new window)

19.
Iannuzzi, A. and Spinelli, P. (1987), "Artificial wind generation and structural response", J. Struct. Eng. - ASCE, 113(12), 2382-2398. crossref(new window)

20.
Intel (2014a), Intel Xeon Processor E5-2670 Specifications, URL: http://ark.intel.com/products/64595

21.
Intel (2014b), Intel Xeon Processor E7-8893 v2 Specifications, URL: http://ark.intel.com/products/75260

22.
Kareem, A. (1981), "Wind-excited response of buildings in higher modes", J. Struct. Div. - ASCE, 107(4), 701-706.

23.
Kepner, J. (2009), Parallel MATLAB for multicore and multinode computers, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, Pennsylvania, USA.

24.
Klockner, A., Warburton, T., Bridge, J. and Hesthaven, J.S. (2009), "Nodal discontinuous Galerkin methods on graphics processors", J. Comput. Phys., 228(21), 7863-7882. crossref(new window)

25.
Krawezik, G.P. and Poole, G. (2010), "Accelerating the ANSYS direct sparse solver with GPUs", 2010 Symposium on Application Accelerators in High Performance Computing.

26.
Mathworks (2013), MATLAB Documents, The MathWorks Inc., Natick, Massachusetts, USA.

27.
Melbourne, W.H. (1980), "Comparison of measurements on the CAARC standard tall building model in simulated model wind flows", J. Wind Eng. Ind. Aerod., 6(1), 73-88. crossref(new window)

28.
Melbourne, W. and Cheung, J. (1988), "Designing for serviceable accelerations in tall buildings", Proceedings of the 4th International Conference on Tall Buildings, Hong Kong and Shanghai.

29.
Moore, G. (1998), "Cramming More Components Onto Integrated Circuits", Proc. IEEE, 86(1), 82-85. crossref(new window)

30.
Nvidia (2013a), TESLA K20 GPU active accelerator, URL: http://www.nvidia.com/content/PDF/kepler/Tesla-K20-Active-BD-06499-001-v04.pdf, Santa Clara, California: The Nvidia Inc.

31.
Nvidia (2013b), TESLA K40 GPU active accelerator, URL:http://www.nvidia.com/content/PDF/kepler/Tesla-K40-PCIe-Passive-Board-Spec-BD-06902-001_v05.pdf , Santa Clara, California: The Nvidia Inc.

32.
Nvidia (2014), CUDA C programming guide, The Nvidia Inc., Santa Clara, California, USA.

33.
Piccardo, G. and Solari, G. (2000), "3D wind-excited response of slender structures: Closed-form solution", J. Struct. Eng. - ASCE, 126(8), 936-943. crossref(new window)

34.
Robert, C.P. and Casella, G. (2005), Monte Carlo statistical methods, Springer, Heidelberg, Germany.

35.
Seo, D.W. and Caracoglia, L. (2012), "Statistical buffeting response of flexible bridges influenced by errors in aeroelastic loading estimation", J. Wind Eng. Ind. Aerod., 104, 129-140.

36.
Seo, D.W. and Caracoglia, L. (2013), "Estimating life-cycle monetary losses due to wind hazards: Fragility analysis of long-span bridges", Eng. Struct., 56, 1593-1606. crossref(new window)

37.
Simiu, E. and Scanlan, R.H. (1996), Wind effects on structures: fundamentals and applications to design, John Wiley & Sons, New Jersey, USA.

38.
Sipser, M. (2012), Introduction to the theory of computation, Cengage Learning, Boston, MA, USA.

39.
Smith, M.A. and Caracoglia, L. (2011), "A Monte Carlo based method for the dynamic "fragility analysis" of tall buildings under turbulent wind loading", Eng. Struct., 33(2), 410-420. crossref(new window)

40.
Smith, M.A. (2009), A Monte Carlo based method for the dynamic performance analysis of tall buildings under turbulent wind loading, M.S. Thesis, Northeastern University, Boston, Massachusetts, USA.

41.
Solari, G. (1988), "Equivalent wind-spectrum technique: theory and applications", J. Struct. Eng. - ASCE, 114(6), 1303-1323. crossref(new window)

42.
Spence, S.M. and Gioffre, M. (2012), "Large scale reliability-based design optimization of wind excited tall buildings", Probabilist. Eng. Mech., 28, 206-215. crossref(new window)

43.
Stantchev, G., Juba, D., Dorland, W. and Varshney, A. (2009), "Using graphics processors for high-performance computation and visualization of plasma turbulence", Comput. Sci. Eng., 11(2), 52-59. crossref(new window)