TY - GEN
T1 - Implementation of Radio Wave Propagation using RT Cores and Consideration of Programming Models
AU - Hashinoki, Shinya
AU - Ohshima, Satoshi
AU - Katagiri, Takahiro
AU - Nagai, Toru
AU - Hoshino, Tetsuya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the NVIDIA Turing architecture generation, several NVIDIA graphics processing units (GPUs) have introduced ray tracing acceleration hardware (RT cores). Ray tracing processing can be regarded as a simulation of wave and particle propagation, collision, and reflection. Therefore, it is expected to be applied to computational science and high-performance computing. However, few studies have been conducted using RT cores. The purpose of this research is to demonstrate the use of RT cores in the scientific and technical computing fields. We implemented a radio wave propagation loss calculation with the programmable ray tracing application framework OptiX and evaluated its performance. Furthermore, we investigated the challenges of reducing the description of framework-specific settings and the needs of hardware allocation. In the simple two spheres experiment, the RT core implementation showed the highest performance. Moreover, the acceleration was super linear scaling, between (10000, 5000) and (20000, 10000). In the experiment with a sphere and planes, the performance achieved by the RT cores was up to approximately 390 times higher than the parallel execution of the BVH search algorithm. We also proved that a large number of RT cores yielded higher performance. In the open data problem space experiment, we evaluated various GPUs and revealed that a larger number of RT cores is effective. These results show that RT cores are sufficiently effective for radio propagation calculations with an adequate number of ray projections. Through this research, we contributed to the RT core use in computational science by proposing an implementation method for ray tracing applications and revealing the effects of RT cores in radio wave propagation loss calculations.
AB - With the NVIDIA Turing architecture generation, several NVIDIA graphics processing units (GPUs) have introduced ray tracing acceleration hardware (RT cores). Ray tracing processing can be regarded as a simulation of wave and particle propagation, collision, and reflection. Therefore, it is expected to be applied to computational science and high-performance computing. However, few studies have been conducted using RT cores. The purpose of this research is to demonstrate the use of RT cores in the scientific and technical computing fields. We implemented a radio wave propagation loss calculation with the programmable ray tracing application framework OptiX and evaluated its performance. Furthermore, we investigated the challenges of reducing the description of framework-specific settings and the needs of hardware allocation. In the simple two spheres experiment, the RT core implementation showed the highest performance. Moreover, the acceleration was super linear scaling, between (10000, 5000) and (20000, 10000). In the experiment with a sphere and planes, the performance achieved by the RT cores was up to approximately 390 times higher than the parallel execution of the BVH search algorithm. We also proved that a large number of RT cores yielded higher performance. In the open data problem space experiment, we evaluated various GPUs and revealed that a larger number of RT cores is effective. These results show that RT cores are sufficiently effective for radio propagation calculations with an adequate number of ray projections. Through this research, we contributed to the RT core use in computational science by proposing an implementation method for ray tracing applications and revealing the effects of RT cores in radio wave propagation loss calculations.
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U2 - 10.1109/IPDPSW59300.2023.00115
DO - 10.1109/IPDPSW59300.2023.00115
M3 - Conference contribution
AN - SCOPUS:85169299733
T3 - 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023
SP - 673
EP - 681
BT - 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023
Y2 - 15 May 2023 through 19 May 2023
ER -