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Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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Parallel Deep Neural Network Training for Big Data on Blue Gene/Q

SESSION: Machine Learning and Data Analytics

EVENT TYPE: Papers

TIME: 11:00AM - 11:30AM

SESSION CHAIR: Hank Childs

AUTHOR(S):I-Hsin Chung, Tara Sainath, Bhuvana Ramabhadran, Michael Picheny, John Gunnels, Vernon Austel, Upendra Chauhari, Brain Kingsbury

ROOM:388-89-90

ABSTRACT:

Deep Neural Networks (DNNs) have recently been shown to significantly outperform existing machine learning techniques in several pattern recognition tasks. The biggest drawback to DNNs is the enormous training time - often 10x slower than conventional technologies. While training time can be mitigated by parallel computing algorithms and architectures, these algorithms often suffer from the cost of inter-processor communication bottlenecks. In this paper, we describe how to enable parallel DNN training on the IBM Blue Gene/Q (BG/Q) computer system using the data-parallel Hessian-free 2nd-order optimization algorithm. BG/Q, with its excellent inter-processor communication characteristics, is an ideal match for the HF algorithm. The paper discusses how issues regarding programming model and data-dependent imbalances are addressed. Results on large-scale speech tasks show that the performance on BG/Q scales linearly up to 4096 processes with no loss in accuracy, allowing us to train DNNs with millions of training examples in a few hours.

Chair/Author Details:

Hank Childs (Chair) - University of Oregon and Lawrence Berkeley National Laboratory

I-Hsin Chung - IBM Corporation

Tara Sainath - IBM Corporation

Bhuvana Ramabhadran - IBM Corporation

Michael Picheny - IBM Corporation

John Gunnels - IBM Corporation

Vernon Austel - IBM Corporation

Upendra Chauhari - IBM Corporation

Brain Kingsbury - IBM Corporation

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society