Treffer: Accelerating numerical modeling of wave propagation through 2-D anisotropic materials using OpenCL.

Title:
Accelerating numerical modeling of wave propagation through 2-D anisotropic materials using OpenCL.
Authors:
Molero M; Centro de Acústica Aplicada y Evaluación No Destructiva, CAEND (CSIC-UPM), Arganda del Rey, 28500 Madrid, Spain. miguel.molero@csic.es, Iturrarán-Viveros U
Source:
Ultrasonics [Ultrasonics] 2013 Mar; Vol. 53 (3), pp. 815-22. Date of Electronic Publication: 2012 Dec 13.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Science Country of Publication: Netherlands NLM ID: 0050452 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1874-9968 (Electronic) Linking ISSN: 0041624X NLM ISO Abbreviation: Ultrasonics Subsets: PubMed not MEDLINE
Imprint Name(s):
Publication: 1995- : Amsterdam : Elsevier Science
Original Publication: London. Butterworth Scientific Ltd.,
Entry Date(s):
Date Created: 20130108 Date Completed: 20130405 Latest Revision: 20130215
Update Code:
20250114
DOI:
10.1016/j.ultras.2012.11.014
PMID:
23290584
Database:
MEDLINE

Weitere Informationen

We present an implementation of the numerical modeling of elastic waves propagation, in 2D anisotropic materials, using the new parallel computing devices (PCDs). Our study is aimed both to model laboratory experiments and explore the capabilities of the emerging PCDs by discussing performance issues. In the experiments a sample plate of an anisotropic material placed inside a water tank is rotated and, for every angle of rotation it is subjected to an ultrasonic wave (produced by a large source transducer) that propagates in the water and through the material producing some reflection and transmission signals that are recording by a "point-like" receiver. This experiment is numerically modeled by running a finite difference code covering a set of angles θ∈[-50°, 50°], and recorded the signals for the transmission and reflection results. Transversely anisotropic and weakly orthorhombic materials are considered. We accelerated the computation using an open-source toolkit called PyOpenCL, which lets one to easily access the OpenCL parallel computation API's from the high-level programming environment of Python. A speedup factor over 19 using the GPU is obtained when compared with the execution of the same program in parallel using a CPU multi-core (in this case we use the 4-cores that has the CPU). The performance for different graphic cards and operating systems is included together with the full 2-D finite difference code with PyOpenCL.
(Copyright © 2012 Elsevier B.V. All rights reserved.)