A hybrid, massively parallel implementation of a genetic algorithm for optimization of the impact performance of a metal/polymer composite plate

Kiran Narayanan, Angel Mora Cordova, Nicholas Allsopp, Tamer S. El Sayed

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A hybrid parallelization method composed of a coarse-grained genetic algorithm (GA) and fine-grained objective function evaluations is implemented on a heterogeneous computational resource consisting of 16 IBM Blue Gene/P racks, a single x86 cluster node and a high-performance file system. The GA iterator is coupled with a finite-element (FE) analysis code developed in house to facilitate computational steering in order to calculate the optimal impact velocities of a projectile colliding with a polyurea/structural steel composite plate. The FE code is capable of capturing adiabatic shear bands and strain localization, which are typically observed in high-velocity impact applications, and it includes several constitutive models of plasticity, viscoelasticity and viscoplasticity for metals and soft materials, which allow simulation of ductile fracture by void growth. A strong scaling study of the FE code was conducted to determine the optimum number of processes run in parallel. The relative efficiency of the hybrid, multi-level parallelization method is studied in order to determine the parameters for the parallelization. Optimal impact velocities of the projectile calculated using the proposed approach, are reported. © The Author(s) 2012.
Original languageEnglish (US)
Pages (from-to)217-227
Number of pages11
JournalInternational Journal of High Performance Computing Applications
Volume27
Issue number2
DOIs
StatePublished - Jul 17 2012

ASJC Scopus subject areas

  • Hardware and Architecture
  • Theoretical Computer Science
  • Software

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