TY - GEN
T1 - On the GPU Parallel Computing for Sommerfeld Integral Tails
AU - Yuan, Xin
AU - Yan, Chao Ze
AU - Wu, Bi Yi
AU - Yang, Ming Lin
AU - Sheng, Xin Qing
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The Sommerfeld integrals (SIs) in layered media are notoriously difficult to calculate due to their oscillatory nature, and their evaluation can require a significant amount of computational resources. This letter investigates the acceleration of numerical integrations of SIs by using the(GPU) processor. With the advent of high-performance computing architectures, such as graphics processing units (GPUs), it has become possible to accelerate SI evaluation, leveraging the parallelism inherent in these architectures. By distributing the workload across a large number of processing units, GPUs can significantly reduce the computational time required to evaluate these integrals. Implemented using the CUDA platform, up to 24 times speedup is achieved compared to a state-of-art multicore CPU with OpenMP parallelism. We render the proposed method can be applied to a wide range of problems in electromagnetics, acoustics, and optics, among other fields.
AB - The Sommerfeld integrals (SIs) in layered media are notoriously difficult to calculate due to their oscillatory nature, and their evaluation can require a significant amount of computational resources. This letter investigates the acceleration of numerical integrations of SIs by using the(GPU) processor. With the advent of high-performance computing architectures, such as graphics processing units (GPUs), it has become possible to accelerate SI evaluation, leveraging the parallelism inherent in these architectures. By distributing the workload across a large number of processing units, GPUs can significantly reduce the computational time required to evaluate these integrals. Implemented using the CUDA platform, up to 24 times speedup is achieved compared to a state-of-art multicore CPU with OpenMP parallelism. We render the proposed method can be applied to a wide range of problems in electromagnetics, acoustics, and optics, among other fields.
KW - Direct numerical integration
KW - GPU parallel
KW - Sommerfeld integral tail
KW - layered media
UR - http://www.scopus.com/inward/record.url?scp=85173044147&partnerID=8YFLogxK
U2 - 10.1109/iWEM58222.2023.10234915
DO - 10.1109/iWEM58222.2023.10234915
M3 - Conference contribution
AN - SCOPUS:85173044147
T3 - 2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023
SP - 370
EP - 372
BT - 2023 IEEE International Workshop on Electromagnetics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023
Y2 - 15 July 2023 through 18 July 2023
ER -