Traditionally, optical circuit design is tested and validated using software which implement numerical modeling techniques such as Beam Propagation, Finite Element Analysis and the Finite-Difference Time-Domain (FDTD) method. FDTD simulations require significant computational power. Existing installations may distribute the computational requirements across large clusters of high-powered servers. This approach entails significant expense in terms of hardware, staffing and software support which may be prohibitive for some research facilities and private-sector engineering firms. The application of modern programmable GPUs to problems in scientific visualization and computation has facilitated faster development cycles for a variety of industry segments including large dataset visualization, aerospace and optical circuit design. GPU-based supercomputers such as National Labs' Summit, co-designed by NVIDIA and IBM, provide dramatically increased compute capability while using less power than CPU-based solutions. The FDTD algorithm maps well to the massively-multithreaded data-parallel nature of GPUs. This thesis explores a GPU-based FDTD implementation and details performance gains, limitations of the GPU approach, optimization techniques and potential future enhancements.

Degree Date

Summer 8-3-2022

Document Type


Degree Name



Electrical and Computer Engineering


Marc Christensen

Second Advisor

Nathan Huntoon

Third Advisor

Ira Greenberg

Subject Area

Computer Science

Number of Pages




Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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