Acceleration of ensemble machine learning methods using many-core devices

21st International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Tamerus, A.^1 ; Washbrook, A.^1,3 ; Wyeth, D.^2

High Performance Computing Service, University of Cambridge, Roger Needham Building, 7 JJ Thomson Avenue, Cambridge

CB3 0RB, United Kingdom^1

SUPA, School of Physics and Astronomy, University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh

EH9 3JZ, United Kingdom^2

Toshiba Medical Visualization Systems Europe Ltd., Bonnington Bond, 2 Anderson Place, Edinburgh

EH6 5NP, United Kingdom^3

关键词: Data processing models;    Decision forest;    Gpu-based;    Machine learning methods;    Many core;    Processing time;    Single-threaded;    Speed up;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/664/9/092026/pdf
DOI  :  10.1088/1742-6596/664/9/092026

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

We present a case study into the acceleration of ensemble machine learning methods using many-core devices in collaboration with Toshiba Medical Visualisation Systems Europe (TMVSE). The adoption of GPUs to execute a key algorithm in the classification of medical image data was shown to significantly reduce overall processing time. Using a representative dataset and pre-trained decision trees as input we will demonstrate how the decision forest classification method can be mapped onto the GPU data processing model. It was found that a GPU-based version of the decision forest method resulted in over 138 times speed-up over a single-threaded CPU implementation with further improvements possible. The same GPU-based software was then directly applied to a suitably formed dataset to benefit supervised learning techniques applied in High Energy Physics (HEP) with similar improvements in performance.