Detail publikace

GPU-accelerated Simulation of Elastic Wave Propagation

KADLUBIAK, K. JAROŠ, J. TREEBY, B.

Originální název

GPU-accelerated Simulation of Elastic Wave Propagation

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Modeling of ultrasound waves propagation in hard biological materials such as bones and skull has a rapidly growing area of applications, e.g. brain cancer treatment planing, deep brain neurostimulation and neuromodulation, and opening blood brain barriers. Recently, we have developed a novel numerical model of elastic wave propagation based on the Kelvin-Voigt model accounting for linear elastic wave proration in heterogeneous absorption media. Although, the model offers unprecedented fidelity, its computational requirements have been prohibitive for realistic simulations. This paper presents an optimized version of the simulation model accelerated by the Nvidia CUDA language and deployed on the best GPUs including the Nvidia P100 accelerators present in the Piz Daint supercomputer. The native CUDA code reaches a speed-up of 5.4 when compared to the Matlab prototype accelerated by the Parallel Computing Toolbox running on the same GPU. Such reduction in computation time enables computation of large-scale treatment plans in terms of hours.

Klíčová slova

Ultrasound simulations, Elastic model, Pseudospectral methods, k-Wave toolbox, GPU

Autoři

KADLUBIAK, K.; JAROŠ, J.; TREEBY, B.

Vydáno

29. 10. 2018

Nakladatel

IEEE Computer Society

Místo

Orleans

ISBN

978-1-5386-7878-7

Kniha

Proceedings - 2018 International Conference on High Performance Computing and Simulation, HPCS 2018

Strany od

188

Strany do

195

Strany počet

8

URL

https://ieeexplore.ieee.org/document/8514349

BibTex

@inproceedings{BUT155002,
  author="Kristián {Kadlubiak} and Jiří {Jaroš} and Bradley {Treeby}",
  title="GPU-accelerated Simulation of Elastic Wave Propagation",
  booktitle="Proceedings - 2018 International Conference on High Performance Computing and Simulation, HPCS 2018",
  year="2018",
  pages="188--195",
  publisher="IEEE Computer Society",
  address="Orleans",
  doi="10.1109/HPCS.2018.00044",
  isbn="978-1-5386-7878-7",
  url="https://ieeexplore.ieee.org/document/8514349"
}