.png){kind=icon}
.png){kind=icon}
Return
Accelerating the XGBoost algorithm using GPU computing
.png){kind=icon}
OA
AI
DOI：10.7717/peerj-cs.127.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations are parallelised, combining two approaches. An interleaved approach is used for shallow trees, switching to a more conventional radix sort-based approach for larger depths. We show speedups of between 3 x and 6 x using a Titan X compared to a 4 core i7 CPU, and 1.2 x using a Titan X compared to 2 x Xeon CPUs (24 cores). We show that it is possible to process the Higgs dataset (10 million instances, 28 features) entirely within GPU memory. The algorithm is made available as a plug-in within the XGBoost library and fully supports all XGBoost features including classification, regression and ranking tasks.
Keywords:
Supervised machine learning
Gradient boosting
GPU computing