Implementing Decision Trees and Forests on a GPU

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Implementing Decision Trees and Forests on a GPU
We describe a method for implementing the evaluation and training of decision trees and forests entirely on a GPU, and show how this method can be used in the context of object recognition. Our strategy for evaluation involves mapping the data structure describing a decision forest to a 2D texture array. We navigate through the forest for each point of the input data in parallel using an efficient, nonbranching pixel shader. For training, we compute the responses of the training data to a set of candidate features, and scatter the responses into a suitable histogram using a vertex shader. The histograms thus computed can be used in conjunction with a broad range of tree learning algorithms. We demonstrate results for object recognition which are identical to those obtained on a CPU, obtained in about 1% of the time. To our knowledge, this is the first time a method has been proposed which is capable of evaluating or training decision trees on a GPU. Our method leverages the full parall...
Toby Sharp
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2008
Where ECCV
Authors Toby Sharp
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