Scalability Aspects of Distributed Simulation -based Optimization in Manufacturing

Advances in processing power of modern computer hardware allow the analysis of increasingly c omplex systems by means of simulation. However, many multidisciplinary engineering problems such as design optimization problems in the aircraft or automotive industry have extremely high computational demands. To solve these problems in reasonable time, t he computation has to be distributed over many CPUs, e.g. in form of a compute cluster or a computational grid. To exploit the total processing power of hundreds of CPUs, intelligent, scalable algorithms are required. In this paper, the concept of scalabil ity is examined in the context of parallel simulation software systems and the distributed solution of simulation -based optimization problems in manufacturing. As case studies, the optimization of alloy casting processes amd the simulation of metal -sheet f orming are presented. I. Introduction PTIMIZATION tasks in multidisciplinary optimization include design problems in manufacturing in the aircraft or automotive industry, alloy casting processes and metal -sheet forming. Typically, solving these problems inv olves running many complex simulations which are implemented in commercial software packages. In general, the optimization algorithm has to treat the simulation system as a black box. To solve such a simulation -based optimization problem, many hundreds or thousands of simulations are necessary, each of which is computationally expensive. This results in extremely high computational demands which can only be met by distributing the computation over many CPUs. Basically, there are two different approaches ho w parallel or distributed computing can be used in this scenario: First, the time needed for a single simulation run can be reduced by parallelizing the simulation software itself, e.g. by partitioning a FEM mesh and assigning each partition to a different CPU. Second, the optimization algorithm can request the evaluation of multiple scenarios at the same time, so that many (sequential) simulations are executed simultaneously. It is also possible to combine the two approaches. The simulation and optimizatio n of complex products in the area of virtual prototyping aims at reducing the time to market for new innovative products and creates an ever -increasing demand for computational power which can only be met by utilizing larger numbers of CPUs. This requires scalable hardware architectures and algorithms. This paper focuses on algorithmic scalability; in particular, three scalable optimization algorithms are presented. To allow a meaningful scalability analysis, a suitable performance and efficiency metric for heuristic, parallel optimization is developed. This is then used to analyze the characteristics of the algorithms for solving a benchmark problem. Further results are shown for computations of industrial pr oblems from casting processes