Conference paper

KADLUBIAK Kristián, JAROŠ Jiří and TREEBY Bradley E. GPU-accelerated Simulation of Elastic Wave Propagation. In: 2018 International Conference on High Performance Computing & Simulation (HPCS). Orleans: IEEE Computer Society, 2018, pp. 188-195. ISBN 978-1-5386-7879-4. Available from:
Publication language:english
Original title:GPU-accelerated Simulation of Elastic Wave Propagation
Title (cs):Akcelerace simulace šíření ultrazvuku v kostech pomoci GPU
Proceedings:2018 International Conference on High Performance Computing & Simulation (HPCS)
Conference:The 2018 International Conference on High Performance Computing and Simulation
Place:Orleans, FR
Publisher:IEEE Computer Society
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Ultrasound simulations, Elastic model, Pseudospectral methods, k-Wave toolbox, GPU
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.
   author = {Kristi{\'{a}}n Kadlubiak and Ji{\v{r}}{\'{i}}
	Jaro{\v{s}} and E. Bradley Treeby},
   title = {GPU-accelerated Simulation of Elastic Wave
   pages = {188--195},
   booktitle = {2018 International Conference on High Performance Computing
	\& Simulation (HPCS)},
   year = {2018},
   location = {Orleans, FR},
   publisher = {IEEE Computer Society},
   ISBN = {978-1-5386-7879-4},
   doi = {10.1109/HPCS.2018.00044},
   language = {english},
   url = {}

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