A coating is an interface between a product and the environment primarily to protect the product. Nanomaterials-based coatings are becoming more demanding to build the best interfaces for a wide range of products including materials and nanomaterials used in aircrafts. Nano reinforcements in nanocomposites are scattered into the matrix during processing. The properties of nanocomposites are significantly affected by the locality of these reinforcements. The poor electric conductivity of epoxy-based nanocomposite coatings may limit their use in aircrafts and other applications. Therefore, it is important to understand the heterogeneous electric behavior of mixtures with carbon nanotubes of these epoxy nanocoating materials. Conventional laboratory techniques that are used to analyze nano-coating are expensive, ineffectual, and often very dangerous. Currently available other methodologies, like computer simulation of finite different method (FDM) with periodic boundary conditions, to assess the electric behavior of composite materials are extremely time consuming. In this work, we introduce a time-efficient simulation technique using NVIDIA compute unified device architecture (CUDA) to analyze the nano-coating properties very quickly. According to this approach, multithreaded parallel programs are developed to execute the computation extensive program segments concurrently in parallel. The sequential portion of the program is executed on a multicore central processing unit (CPU), and the parallel portion of the program is executed on a many-core graphics processing unit (GPU). Experimental results from CUDA-assisted parallel programming of an FDM-based Laplace's equation demonstrate up to 257x speedup and 97% energy savings over a parallel MATLAB implementation while solving a 4Kx4K problem with reasonable accuracy.