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The web is moving from an era of "search" to that of "discovery". Collaborative filtering (CF) recommender systems are now commonly used to predict user's preference towards an unknown item from past ratings. To be scalable or effective, they are typically deployed in distributed clusters and operate on extremely large apriori datasets. Improvement of the efficiency of these systems...
This article presents an optimized algorithm for Nonnegative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including dimensionality reduction, feature extraction, and...
To exploit the full potential of GPGPUs for general purpose computing, DOACR parallelism abundant in scientific and engineering applications must be harnessed. However, the presence of cross-iteration data dependences in DOACR loops poses an obstacle to execute their computations concurrently using a massive number of fine-grained threads. This work focuses on iterative PDE solvers rich in DOACR parallelism...
The Viterbi algorithm is the compute-intensive kernel in Hidden Markov Model (HMM) based sequence alignment applications. In this paper, we investigate extending several parallel methods, such as the wave-front and streaming methods for the Smith-Waterman algorithm, to achieve a significant speed-up on a GPU. The wave-front method can take advantage of the computing power of the GPU but it cannot...
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