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GTC On-Demand

AI Application Deployment and Inference
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CatBoost: Fast Open-Source Gradient Boosting Library For GPU
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.
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.  Back
 
Keywords:
AI Application Deployment and Inference, Tools and Libraries, HPC and AI, GTC Silicon Valley 2018 - ID S8393
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