GPU computing for accelerating the numerical Path Integration approach

Research output: Contribution to journal › Article › peer-review

Standard

GPU computing for accelerating the numerical Path Integration approach. / Alevras, Panagiotis; Yurchenko, D.

In: Computers and Structures, Vol. 171, 15.07.2016, p. 46-53.

Research output: Contribution to journal › Article › peer-review

Harvard

Alevras, P & Yurchenko, D 2016, 'GPU computing for accelerating the numerical Path Integration approach', Computers and Structures, vol. 171, pp. 46-53. https://doi.org/10.1016/j.compstruc.2016.05.002

APA

Alevras, P., & Yurchenko, D. (2016). GPU computing for accelerating the numerical Path Integration approach. Computers and Structures, 171, 46-53. https://doi.org/10.1016/j.compstruc.2016.05.002

Vancouver

Alevras P, Yurchenko D. GPU computing for accelerating the numerical Path Integration approach. Computers and Structures. 2016 Jul 15;171:46-53. https://doi.org/10.1016/j.compstruc.2016.05.002

Author

Alevras, Panagiotis ; Yurchenko, D. / GPU computing for accelerating the numerical Path Integration approach. In: Computers and Structures. 2016 ; Vol. 171. pp. 46-53.

Bibtex

@article{3cda3db767f24801aa50cc806eed9dd6,
title = "GPU computing for accelerating the numerical Path Integration approach",
abstract = "The paper discusses a novel approach of accelerating the numerical Path Integration method, used for generating a stationary joint response probability density function of a dynamic system subjected to a random excitation, by the GPU computing. The paper proposes the parallelization of nested loops technique and demonstrates the advantages of GPU computing. Two, three and four dimensional in space problems are investigated as a part of the pilot project and the achieved maximum accelerations are reported. Three degree-of-freedom system (6D) is approached by the Path Integration technique for the first time. The application of the proposed GPU methodology for problems of stochastic dynamics and reliability are discussed.",
keywords = "GPU computing, Path Integration method, Probability density function, Random vibrations, Reliability, Ship roll, Stochastic modeling",
author = "Panagiotis Alevras and D. Yurchenko",
year = "2016",
month = jul,
day = "15",
doi = "10.1016/j.compstruc.2016.05.002",
language = "English",
volume = "171",
pages = "46--53",
journal = "Computers & Structures",
issn = "0045-7949",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - GPU computing for accelerating the numerical Path Integration approach

AU - Alevras, Panagiotis

AU - Yurchenko, D.

PY - 2016/7/15

Y1 - 2016/7/15

N2 - The paper discusses a novel approach of accelerating the numerical Path Integration method, used for generating a stationary joint response probability density function of a dynamic system subjected to a random excitation, by the GPU computing. The paper proposes the parallelization of nested loops technique and demonstrates the advantages of GPU computing. Two, three and four dimensional in space problems are investigated as a part of the pilot project and the achieved maximum accelerations are reported. Three degree-of-freedom system (6D) is approached by the Path Integration technique for the first time. The application of the proposed GPU methodology for problems of stochastic dynamics and reliability are discussed.

AB - The paper discusses a novel approach of accelerating the numerical Path Integration method, used for generating a stationary joint response probability density function of a dynamic system subjected to a random excitation, by the GPU computing. The paper proposes the parallelization of nested loops technique and demonstrates the advantages of GPU computing. Two, three and four dimensional in space problems are investigated as a part of the pilot project and the achieved maximum accelerations are reported. Three degree-of-freedom system (6D) is approached by the Path Integration technique for the first time. The application of the proposed GPU methodology for problems of stochastic dynamics and reliability are discussed.

KW - GPU computing

KW - Path Integration method

KW - Probability density function

KW - Random vibrations

KW - Reliability

KW - Ship roll

KW - Stochastic modeling

UR - http://www.scopus.com/inward/record.url?scp=84976557558&partnerID=8YFLogxK

U2 - 10.1016/j.compstruc.2016.05.002

DO - 10.1016/j.compstruc.2016.05.002

M3 - Article

AN - SCOPUS:84976557558

VL - 171

SP - 46

EP - 53

JO - Computers & Structures

JF - Computers & Structures

SN - 0045-7949

ER -