"XGBoost GPU Implementation and Optimization"
"XGBoost GPU Implementation and Optimization" is a comprehensive technical guide that explores the intersection of advanced machine learning and high-performance GPU computing. Beginning with the mathematical and algorithmic foundations of XGBoost, this book delves deep into topics such as gradient boosting theory, state-of-the-art regularization, sophisticated loss functions, sparsity management, and benchmark comparisons with leading libraries like CatBoost and LightGBM. Readers are provided with a robust understanding of the internal mechanics that distinguish XGBoost as a leading library in scalable, accurate machine learning solutions.
The book then transitions into the architecture, programming, and optimization of GPUs for XGBoost, covering the nuances of CUDA programming, GPU memory management, pipeline design, profiling techniques, and parallel computing paradigms. Through detailed algorithmic chapters, it guides practitioners in translating boosting methods to GPUs, optimizing data transfers, load balancing across multi-GPU systems, and accelerating inference. Core implementation details are thoroughly examined, including GPU-based histogram building, gradient aggregation, kernel fusion, and integration with XGBoost's advanced scheduling and distributed capabilities.
Designed for data scientists, machine learning engineers, and system architects, this book finally addresses the challenges of hyperparameter optimization on GPUs, distributed and cloud deployments, and contemporary performance engineering approaches for low-latency and energy-efficient solutions. The text closes by mapping future directions—such as federated learning, green AI, AutoML integrations, and edge deployments—alongside case studies from industrial and scientific domains, making it an indispensable resource for professionals seeking to harness the full power of GPU-accelerated gradient boosting in real-world, large-scale environments.