Human brain functions are based on the electrochemical activity and
interaction of the neurons constituting the brain. Some brain diseases are
characterized by abnormalities of this activity. Detection of the location
and orientation of this electrical activity is called electro-magnetic source
imaging (EMSI) and is of signicant importance since it promises to serve
as a powerful tool for neuroscience. Boundary Element Method (BEM) is a
method applicable for EMSI on realistic head geometries that generates large
systems of linear equations with dense matrices. Generation and solution of
these matrix equations are time and memory consuming due to the size of
the matrices and high computational complexity of direct methods. This
study presents a relatively cheap and eective solution the this problem and
reduces the processing times to clinically acceptable values using parallel
cluster of personal computers on a local area network. For this purpose,
a cluster of 8 workstations is used. A parallel BEM solver is implemented
that distributes the model eciently to the processors. The parallel solver
for BEM is developed using the PETSc library. The performance of the
iv
solver is evaluated in terms of CPU and memory usage for dierent number
of processors. For a 15011 node mesh, a speed-up eciency of 97.5% is
observed when computing transfer matrices. Individual solutions can be
obtained in 520 ms on 8 processors with 94.2% parallellization eciency.
It was observed that workstation clusters is a cost eective tool for solving
complex BEM models in clinically acceptable time. Eect of parallelization
on inverse problem is also demonstrated by a genetic algorithm and very
similar speed-up is obtained.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12606548/index.pdf |
| Date | 01 September 2005 |
| Creators | Ataseven, Yoldas |
| Contributors | Gencer, Nevzat Guneri |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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