GPUTreeShap: Fast Parallel Tree Interpretability
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SHAP (SHapley Additive exPlanation) values provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values. While SHAP values are intractable in general, a recursive polynomial time algorithm specialised for decision tree models is available, named TreeShap. Despite its polynomial time complexity, TreeShap can become a significant bottleneck in practical machine learning pipelines when applied to large decision tree ensembles. We present GPUTreeShap, a software package implementing a modified TreeShap algorithm in CUDA for Nvidia GPUs. Our approach first preprocesses the input model to isolate variable sized sub-problems from the original recursive algorithm, then solves a bin packing problem, and finally maps sub-problems to streaming multiprocessors for parallel execution with specialised hardware instructions. With a single GPU, we achieve speedups of up to 19x for SHAP values, and 340x for SHAP interaction values, over a state-of-the-art multi-core CPU implementation. We also experiment with an 8 GPU DGX-1 system, demonstrating throughput of 1.2M rows per second—equivalent CPU-based performance is estimated to require 6850 CPU cores.
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