SC22 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Research Posters Archive

A Bayesian Optimization-Assisted, High-Performance Simulator for Modeling RF Accelerator Cavities

Authors: Aman Rani (Texas Tech University) and Yang Liu, Tianhuan Luo, Hengrui Luo, and Xiaoye Li (Lawrence Berkeley National Laboratory (LBNL))

Abstract: Radio-frequency cavities are key components for high-energy particle accelerators, quantum computing, etc. Designing cavities comes along with many computational challenges such as multi-objective optimization, high performance computing (HPC) requirement for handling large-sized cavities etc. To be more precise, its multi-objective optimization requires an efficient 3D full-wave electromagnetic simulator. For which, we rely on the integral equation (IE) method and it requires fast solver with HPC and ML algorithms to search for resonance modes.

We propose an HPC-based fast direct matrix solver for IE, combined with hybrid optimization algorithms to attain an efficient simulator for accelerator cavity modeling. First, we solve the linear eigen problem for each trial frequency by a distributed-memory parallel, fast direct solver. Second, we propose the combination of the global optimizer Gaussian Process with the local optimizer Downhill-simplex methods to generate the trial frequency samples which successfully optimize the corresponding 1D objective function with multiple sharp minimums.

Best Poster Finalist (BP): no

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