Using HybriLIT cluster to speed up the calculations for machine learning tasks


Date and Time: Thursday, 15 February 2018, at 3:00 PM

Venue: room 310, Laboratory of Information Technologies

Seminar topic: «Using HybriLIT cluster to speed up the calculations for machine learning tasks»

Speakers: P.V. Goncharov (State Technical University of Gomel, Republic of Belarus), M.A. Matveev (JINR)


In past decades the topic of self-learning algorithms, such as deep neural networks, has gained popularity. The idea appeared in the 50s of the past decade, but hardware and software was an obstacle to research.
Appearance of parallel computing systems and frameworks such as TensorFlow, Theano and Caffe, providing parallelization at the libraries level, allowed to accelerate time of learning and inference in dozens of times when using modern graphic accelerator and CUDA technology.
Four applications were developed: for classification of compressed images, recognition of sorts of durum wheat, prediction of the level of environment pollution and two-step approach for the particle track recognition in GEM detectors. Programs were written using Python, TensorFlow and Keras libraries.
Performing computations on HybriLIT virtual machine with NVIDIA Tesla M60 graphic accelerator shows a significant gain of the speed of learning and inference.