Campana Olivo, Romel

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    Parallel implementation of nonlinear dimensionality reduction methods using cuda in gpu
    (2011) Campana Olivo, Romel; Manian, Vidya; College of Engineering; Vélez Reyes, Miguel; Santiago Santiago, Nayda G.; Department of Electrical and Computer Engineering; Ortiz Albino, Reyes M.
    Manifold learning, is one of the methods for nonlinear dimensionality reduction, which affords a way to understand and visualize the structure of nonlinear hyperspectral datasets. These methods use graphs to represent the manifold topology, and use metrics like geodesic distance, allowing embedding higher dimension objects into lower dimensional space. However the complexities of some manifold learning algorithms is ??(????), therefore they are very slow (high computational algorithms). In this project, we present a CUDA-based parallel implementation of the three most popular manifold learning algorithms: Isomap, Locally linear embedding, and Laplacian eigenmaps, using the CUDA multi-thread model. Each of these algorithms has three main parts: find ?? nearest neighbors, build the matrix of distances or weights, and compute the low dimension of the hyperspectral image. The first part was implemented in CUDA by (Garcia, Debreuve, & Barlaud, 2008), the second part was implemented by us in pure C++ and CUDA to measure the speedup between these implementations, and the third was carried out using the libraries of CULA and MKL LAPACK. The manifold learning algorithms were implemented on a 64-bit workstation equipped with a quad-core Intel® Xeon with 12 GB RAM and two NVIDIA Tesla C1060 GPU cards. The CUDA implementation achieved 26?? speedup compared to a pure C++ implementation. It also showed good scalability when varying the size of the dataset and the number of K nearest neighbors.