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2018 GTC San Jose
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S8674 - Node-Level Deep Learning Input Pipeline Optimization on GPGPU-Accelerated HPC Systems

Session Speakers
Session Description

Learn how to implement and analyze a simple deep learning input pipeline pattern that prevents slowdowns from input queue exhaustion on accelerated HPC systems with limited impact to model performance. Queue exhaustion occurs because the throughput-driven dequeue rate is greater than the enqueue rate, which is bound by storage access bandwidth. In this session we will describe a technique that prevents queue exhaustion by artificially slowing the effective dequeue rate, without sacrificing substantial validation set performance. An example using TensorFlow is presented, and the resultant optimization step speedup and model performance are analyzed across several HPC resource configurations.


Additional Information
Deep Learning and AI Frameworks, Supercomputing
Aerospace, Government / National Labs
All technical
Talk
25 minutes
Session Schedule