GPU-Parallelized Simulation of Optical Forces on Nanoparticles in a Fluid Medium

Experimental research in physics can be a costly and time-consuming venture, requiring simulation-based approaches to effectively narrow down the scope of experiments to only the most promising cases. Our multidisciplinary research in this paper demonstrates how the simulation of light-driven nanoparticles can substantially benefit from GPU-based parallelism. We develop a novel ray-tracing strategy and we implement it in C++/CUDA and extend it with a parallel differential equation solver. Our implementation relies on a custom memory layout optimization to tackle the computational challenges in the field and provide accurate solutions in near real time. We evaluate our approach on a variety of popular GPU architectures, including advanced data-center GPUs like the Nvidia V100, as well as consumer-grade hardware like the Nvidia RTX 2080 Ti and Nvidia GTX 1080. Our GPU-based approach achieves a speedup of up to $20\times$ compared to a parallel CPU-based prototype implementation.