Multilevel parallel optimization using massively parallel structural dynamics

A large-scale structural optimization of an electronics package has been completed using a massively parallel structural dynamics code. The optimization goals were to maximize safety margins for stress and acceleration resulting from transient impulse loads, while remaining within strict mass limits. The optimization process utilized nongradient, gradient, and approximate optimization methods in succession to modify shell thickness and foam density values within the electronics package. This combination of optimization methods was successful in improving the performance from an infeasible design that violated response allowables by a factor of two to a completely feasible design with positive design margins, while remaining within the mass limits. In addition, a tradeoff curve of mass versus safety margin was developed to facilitate the design decision process. These studies employed the ASCI Red supercomputer and used multiple levels of parallelism on up to 2560 processors. In total, a series of calculations were performed on ASCI Red in five days, where an equivalent calculation on a single desktop computer would have taken greater than 12 years to complete. This paper conveys the approaches, results, and lessons learnt from this large-scale production design application.

[1]  Charbel Farhat,et al.  An Unconventional Domain Decomposition Method for an Efficient Parallel Solution of Large-Scale Finite Element Systems , 1992, SIAM J. Sci. Comput..

[2]  Ren-Jye Yang,et al.  Robustness optimization for vehicular crash simulations , 2000, Comput. Sci. Eng..

[3]  C. Kelley Iterative Methods for Linear and Nonlinear Equations , 1987 .

[4]  Michael T. Heath,et al.  Scientific Computing: An Introductory Survey , 1996 .

[5]  Harold Thimbleby,et al.  Computerised Parkinson's Law , 1993 .

[6]  Garth M. Reese,et al.  Salinas - An implicit finite element structural dynamics code developed for massively parallel platforms , 2000 .

[7]  Paul T. Boggs,et al.  Large Scale Non-Linear Programming for PDE Constrained Optimization , 2002 .

[8]  T. W. Layne,et al.  A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models , 1998 .

[9]  Danny C. Sorensen,et al.  P_ARPACK: An Efficient Portable Large Scale Eigenvalue Package for Distributed Memory Parallel Architectures , 1996, PARA.

[10]  Raphael T. Haftka,et al.  Structural Optimization: What Has Moore's Law Done For Us? , 2002 .

[11]  Michael A. Saunders,et al.  User''s guide for NPSOL (Ver-sion 4.0): A FORTRAN package for nonlinear programming , 1984 .

[12]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[13]  J. Sobieszczanski-Sobieski,et al.  Optimization of car body under constraints of noise, vibration, and harshness (NVH), and crash , 2001 .

[14]  Bjarne Stroustrup,et al.  The C++ programming language (2nd ed.) , 1991 .

[15]  George Biros,et al.  Parallel Lagrange-Newton-Krylov-Schur Methods for PDE-Constrained Optimization. Part II: The Lagrange-Newton Solver and Its Application to Optimal Control of Steady Viscous Flows , 2005, SIAM J. Sci. Comput..

[16]  D. Day A basic parallel sparse eigensolver for structural dynamics , 1998 .

[17]  Michael S. Eldred,et al.  IMPLEMENTATION OF A TRUST REGION MODEL MANAGEMENT STRATEGY IN THE DAKOTA OPTIMIZATION TOOLKIT , 2000 .

[18]  Michael S. Eldred,et al.  Multilevel parallelism for optimization on MP computers - Theory and experiment , 2000 .

[19]  Jack Dongarra,et al.  MPI: The Complete Reference , 1996 .

[20]  R. Brightwell,et al.  A System Software Architecture for High End Computing , 1997, ACM/IEEE SC 1997 Conference (SC'97).

[21]  Graham Glass UNIX for Programmers and Users: A Complete Guide , 1993 .

[22]  Michael S. Eldred,et al.  DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Sensitivity Analysis, and Uncertainty Quantification , 1996 .

[23]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[24]  J. Friedman Multivariate adaptive regression splines , 1990 .

[25]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .