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A Tutorial on Combining Nonlinear Optimization with CUDA

Hatt, Charles
University of Wisconsin - Madison
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3515
New: Prefer using the following doi: https://doi.org/10.54294/ndjv6l
Published in The Insight Journal - 2015 January-December.
Submitted by Charles Hatt on 2015-04-27 09:06:50.

Nonlinear optimization is a key component of many image registration algorithms. Improving registration speed is almost always desirable. One way to do this is to accelerate the optimization cost function using a parallel implementation. The purpose of this document is to provide a tutorial on how to combine the CUDA GPU computing framework with standard nonlinear optimization libraries (VNL) using CMake. The provided code can be used as a starting template for programmers looking for a relatively painless introduction to CUDA-accelerated medical image registration and other nonlinear optimization problems.