XGBoost: Scalable GPU Accelerated Learning
We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast, scalable training on multi-GPU systems with all of the features of the XGBoost library. We employ data compression techniques to minimise the usage of scarce GPU memory while still allowing highly efficient implementation. Using our algorithm we show that it is possible to process 115 million training instances in under three minutes on a publicly available cloud computing instance. The algorithm is implemented using end-to-end GPU parallelism, with prediction, gradient calculation, feature quantisation, decision tree construction and evaluation phases all computed on device.
Code (1)
Tasks
Cloud ComputingData CompressionGPUSimilar Papers 제목 키워드 기반
Survival regression with accelerated failure time model in XGBoost
Survival regression is used to estimate the relation between time-to-event and feature variables, and is important in application domains such as medicine, marketing, risk management and sales management. Nonlinear tree …
ManagementMarketingregressionXGBoost: A Scalable Tree Boosting System
Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-…
BIG-bench Machine LearningClusteringData CompressionDimensionality Reduction+3A Comparative Analysis of XGBoost
XGBoost is a scalable ensemble technique based on gradient boosting that has demonstrated to be a reliable and efficient machine learning challenge solver. This work proposes a practical analysis of how this novel techni…
BIG-bench Machine LearningFairXGBoost: Fairness-aware Classification in XGBoost
Highly regulated domains such as finance have long favoured the use of machine learning algorithms that are scalable, transparent, robust and yield better performance. One of the most prominent examples of such an algori…
ClassificationFairnessGeneral ClassificationImbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost
The paper presents Imbalance-XGBoost, a Python package that combines the powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced classification tasks. Though a small-scale program in te…
Binary ClassificationClassificationGeneral Classificationimbalanced classification