TY - GEN

T1 - Accelerating real-time ship motion simulations using general purpose GPU computations

AU - Petriakov, Ivan

AU - Gankevich, Ivan

AU - Korkhov, Vladimir

AU - Degtyarev, Alexander

N1 - Funding Information: The research was carried out within framework of grant of Saint Petersburg State University no. 26520170. Publisher Copyright: © 2018 Ivan Petriakov, Ivan Gankevich, Vladimir Korkhov, Degtyarev Alexander. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 2018

Y1 - 2018

N2 - Software suites for ship simulations are typically used for statistical studies of ship dynamics, but also as a simulator for training ship crew in dangerous situations. One problem that arises during training is speeding-up a part of the session which does not involve actions from the crew. The aim of the study reported here is to accelerate solution of ship motion equations using general purpose computations on GPU. These equations describe dynamics of ship manoeuvring in wavy sea surface, and are central to the simulator programme. The equations are solved numerically via Runge-Kutta-Fehlberg method. Due to high number of floating point operations, computation on GPU achieves considerable speed-up over CPU. High performance solution allows to shorten training sessions and make them more efficient, but also beneficial for statistical studies as it reduces simulation time.

AB - Software suites for ship simulations are typically used for statistical studies of ship dynamics, but also as a simulator for training ship crew in dangerous situations. One problem that arises during training is speeding-up a part of the session which does not involve actions from the crew. The aim of the study reported here is to accelerate solution of ship motion equations using general purpose computations on GPU. These equations describe dynamics of ship manoeuvring in wavy sea surface, and are central to the simulator programme. The equations are solved numerically via Runge-Kutta-Fehlberg method. Due to high number of floating point operations, computation on GPU achieves considerable speed-up over CPU. High performance solution allows to shorten training sessions and make them more efficient, but also beneficial for statistical studies as it reduces simulation time.

KW - GPGPU

KW - Maritime simulator

KW - Ocean waves

KW - OpenCL

KW - Ship Dynamics

KW - Virtual testbed

UR - http://www.scopus.com/inward/record.url?scp=85060111588&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85060111588

T3 - CEUR Workshop Proceedings

SP - 410

EP - 414

BT - Selected Papers of the 8th International Conference ""Distributed Computing and Grid-Technologies in Science and Education"", GRID 2018

T2 - 8th International Conference "Distributed Computing and Grid-Technologies in Science and Education", GRID 2018

Y2 - 10 September 2018 through 14 September 2018

ER -