A Tutorial on Combining Nonlinear Optimization with CUDA

Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3515
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.
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Categories: Code speed optimization, Optimization, Parallelization, SMP, Registration
Keywords: CUDA, Nonlinear Optimization, VNL, Tutorial, Source Code
Toolkits: CMake
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