Finite difference iterative solvers for electroencephalography: serial and parallel performance analysis.
Currently the resolution of the head models used in electroencephalography (EEG) studies is limited by the speed of the forward solver. Here, we present a parallel finite difference technique that can reduce the solution time of the governing Poisson equation for a head model. Multiple processors ar...
| Published in: | Medical & Biological Engineering & Computing Vol. 46; no. 9; pp. 901 - 911 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Sep2008
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105552625&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105552625 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2008 vid: 46 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105552625 NLM18478286 2010025073 10.1007/s11517-008-0344-9 NLM18478286 105552625 ppf: 901 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Finite difference iterative solvers for electroencephalography: serial and parallel performance analysis. aug: au: Barnes DN George JS Ng KT Barnes, Derek N George, John S Ng, Kwong T affil: Klipsch School of Electrical and Computer Engineering, New Mexico State University, MSC 3-O, Las Cruces, NM 88003, USA sug: subj: Electroencephalography Methods Signal Processing, Computer Assisted Head Anatomy and Histology Magnetic Resonance Imaging Models, Anatomic ab: Currently the resolution of the head models used in electroencephalography (EEG) studies is limited by the speed of the forward solver. Here, we present a parallel finite difference technique that can reduce the solution time of the governing Poisson equation for a head model. Multiple processors are used to work on the problem simultaneously in order to speed up the solution and provide the memory for solving large problems. The original computational domain is divided into multiple rectangular partitions. Each partition is then assigned to a processor, which is responsible for all the computations and inter-processor communication associated with the nodes in that particular partition. Since the forward solution time is mainly spent on solving the associated matrix equation, it is desirable to find the optimum matrix solver. A detailed comparison of various iterative solvers was performed for both isotropic and anisotropic realistic head models constructed from MRI images. The conjugate gradient (CG) method preconditioned with an advanced geometric multigrid technique was found to provide the best overall performance. For an anisotropic model with 256 x 128 x 256 cells, this technique provides a speedup of 508 on 32 processors over the serial CG solution, with a speedup of 20.1 and 25.3 through multigrid preconditioning and parallelization, respectively. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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