Posts by Yazhini Rajesh
Using Gradient Boosting Libraries on MI300X for Financial Risk Prediction
- 08 January 2026
In the world of machine learning, the choice of hardware can significantly impact the performance and efficiency of model training and prediction. Gradient Boosting Machines (GBMs) benefit greatly from GPU parallelization in several key algorithmic steps involving independent, repetitive computations. The most substantial speedup comes from histogram construction and best split searching, as these can be executed in parallel across features and candidate splits using thousands of GPU cores, vastly accelerating tree building. Additionally, the calculation of gradients and Hessians for each data point is naturally parallelizable and well suited to GPU architectures. Other operations—such as leaf value updates, data preprocessing (like quantization and normalization), and batch predictions—can also be distributed efficiently across GPU threads. By exploiting parallelism in these stages, GPUs dramatically reduce training and prediction time for GBMs, making them ideal for large datasets or scenarios where quick model iteration is crucial.