We show how to optimize a Support Vector Machine and a predictor for Collaborative Filtering with Stochastic Gradient Descent on the GPU, achieving 1.66 to 6-times accelerations compared to a CPU-based implementation. The reference implementations are the Support Vector Machine by Bottou and the BRISMF predictor from the Netflix Prices winning team. Our main idea is to create a hash function of the input data and use it to execute threads in parallel that write on different elements of the parameter vector. We also compare the iterative optimization with a batch gradient descent and an alternating least squares optimization. The predictor is tested against over a hundred million data sets which demonstrates the increasing memory management capabilities of modern GPUs. We make use of matrix as well as float compression to alleviate the memory bottleneck. © 2012 Springer-Verlag.
CITATION STYLE
Zastrau, D., & Edelkamp, S. (2012). Stochastic gradient descent with GPGPU. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7526 LNAI, pp. 193–204). https://doi.org/10.1007/978-3-642-33347-7_17
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