A high-speed implementation of manifold coordinate representations of hyperspectral imagery: A GPU-based approach to rapid nonlinear modeling

Working with the Naval Research Laboratory, Celestech has implemented advanced non-linear hyperspectral image (HSI) processing algorithms optimized for Graphics Processing Units (GPU). These algorithms have demonstrated performance improvements of nearly 2 orders of magnitude over optimal CPU-based implementations. The paper briefly covers the architecture of the NIVIDIA GPU to provide a basis for discussing GPU optimization challenges and strategies. The paper then covers optimization approaches employed to extract performance from the GPU implementation of Dr. Bachmann's algorithms including memory utilization and process thread optimization considerations. The paper goes on to discuss strategies for deploying GPU-enabled servers into enterprise service oriented architectures. Also discussed are Celestech's on-going work in the area of middleware frameworks to provide an optimized multi-GPU utilization and scheduling approach that supports both multiple GPUs in a single computer as well as across multiple computers. This paper is a complementary work to the paper submitted by Dr. Charles Bachmann entitled "A Scalable Approach to Modeling Nonlinear Structure in Hyperspectral Imagery and Other High-Dimensional Data Using Manifold Coordinate Representations". Dr. Bachmann's paper covers the algorithmic and theoretical basis for the HSI processing approach.