Broadband monitoring simulation with massively parallel processors

Modern efficient optimization techniques, namely needle optimization and gradual evolution, enable one to design optical coatings of any type. Even more, these techniques allow obtaining multiple solutions with close spectral characteristics. It is important, therefore, to develop software tools that can allow one to choose a practically optimal solution from a wide variety of possible theoretical designs. A practically optimal solution provides the highest production yield when optical coating is manufactured. Computational manufacturing is a low-cost tool for choosing a practically optimal solution. The theory of probability predicts that reliable production yield estimations require many hundreds or even thousands of computational manufacturing experiments. As a result reliable estimation of the production yield may require too much computational time. The most time-consuming operation is calculation of the discrepancy function used by a broadband monitoring algorithm. This function is formed by a sum of terms over wavelength grid. These terms can be computed simultaneously in different threads of computations which opens great opportunities for parallelization of computations. Multi-core and multi-processor systems can provide accelerations up to several times. Additional potential for further acceleration of computations is connected with using Graphics Processing Units (GPU). A modern GPU consists of hundreds of massively parallel processors and is capable to perform floating-point operations efficiently.