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GTC 2018 Silicon Valley

S8393 - CatBoost: Fast Open-Source Gradient Boosting Library For GPU

Session Speakers
Session Description

Learn how to use GPUs to accelerate gradient boosting on decision trees. We'll discuss CUDA implementation of CatBoost — an open-source library that successfully handles categorical features and shows better quality compared to other open-source gradient boosted decision trees libraries. We'll provide a brief overview of problems which could be solved with CatBoost. Then, we'll discuss challenges and key optimizations in the most significant computation blocks. We'll describe how one can efficiently build histograms in shared memory to construct decision trees and how to avoid atomic operation during this step. We'll provide benchmarks that shows that our GPU implementation is five to 40 times faster compared to Intel server CPUs. We'll also provide performance comparison against GPU implementations of gradient boosting in other open-source libraries.


Additional Information
HPC and AI, Tools and Libraries, AI Application Deployment / Inference
Software
Intermediate technical
Talk
25 minutes
Session Schedule