Accelerating light scattering simulations of nanostructures by reconfigurable computing

In order to characterize nanostructures and nanosurfaces in production processes, measuring methods based on light scattering gain increasing importance. Thus the simulation capability of laser light scattering on surfaces with a size of several hundred or thousand wavelenghts in diameter and light scattering models on the nanometer scale are required to validate these new measurement techniques. This leads to a huge amount of computational complexity exceeding the resources of conventional desktop computers. In order to overcome this computational bottleneck two different approaches for massively parallel computing, namely graphic processing unit (GPU) computing and reconfigurable computing are compared in this paper. Both approaches are discussed with respect to the discrete dipole approximation (DDA) approach. Finally, a computer architecture incorporating both in a standard desktop system is presented.