Open AccessMULTI-GPU TRAINING AND PARALLEL CPU COMPUTING FOR THE MACHINE LEARNING EXPERIMENTS USING ARIADNE LIBRARY
Author(s) -
Pavel Goncharov,
Anastasiia Nikolskaia,
G. Ososkov,
E. Rezvaya,
D. Rusov,
Egor Shchavelev
Publication year - 2021
Publication title -
9th international conference "distributed computing and grid technologies in science and education"
Language(s) - English
Resource type - Conference proceedings
DOI - 10.54546/mlit.2021.42.38.001
Subject(s) - computer science , preprocessor , artificial neural network , central processing unit , artificial intelligence , deep learning , data pre processing , parallel computing , machine learning , computer architecture , computer hardware
Modern machine learning (ML) tasks and neural network (NN) architectures require huge amounts ofGPU computational facilities and demand high CPU parallelization for data preprocessing. At thesame time, the Ariadne library, which aims to solve complex high-energy physics tracking tasks withthe help of deep neural networks, lacks multi-GPU training and efficient parallel data preprocessing onthe CPU.In our work, we present our approach for the Multi-GPU training in the Ariadne library. We willpresent efficient data-caching, parallel CPU data preprocessing, generic ML experiment setup forprototyping, training, and inference deep neural network models. Results in terms of speed-up andperformance for the existing neural network approaches are presented with the help of GOVORUNcomputing resources.