Out-of-Core GPU Gradient Boosting

Rong Ou

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arXiv · 2005.09148

Out-of-Core GPU Gradient Boosting

Rong Ou

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Abstract

GPU-based algorithms have greatly accelerated many machine learning methods; however, GPU memory is typically smaller than main memory, limiting the size of training data. In this paper, we describe an out-of-core GPU gradient boosting algorithm implemented in the XGBoost library. We show that much larger datasets can fit on a given GPU, without degrading model accuracy or training time. To the best of our knowledge, this is the first out-of-core GPU implementation of gradient boosting. Similar approaches can be applied to other machine learning algorithms

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Rong Ou. 2020-05-19. Out-of-Core GPU Gradient Boosting. https://arxiv.org/abs/2005.09148

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