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Modern Graphics Processing Units (GPU), offer a tremendous computing power, that is frequently an order of magnitude larger than even the most modern multi-core CPUs, making them an attractive platform for high performance computing due to their relative cheapness compared with conventional PC clusters. General purpose computing on GPUs (GPGPU) is becoming popular in High Performance Computing (HPC) because of its high peak performance. In this paper, a typical two-dimensional truss structure optimization problem is solved using Binary Genetic Algorithm (BGA) on both CPU and GPU. The kernel inside the GPU code computes the nodal displacements and elemental stresses by Finite Element Analysis (FEA) to evaluate the objective function and the constraints while making use of the Single Instruction Multiple Data (SIMD) structure of GPU to attain parallelization. The results are assessed for different values of parameters, such as complexity of the problem, number of elements, population size, maximum allowable generations and number of threads etc. to demonstrate how the value of speedup varies with these parameters and to provide a basic guideline for choosing the parameters for a different problem. The results clearly establish that calculations are performed considerably faster through the GPU than through the CPU in general.


GPU, Genetic Algorithm, GPGPU, Topology Optimization.
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