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2017 GTC San Jose

S7320 - Optimizing Efficiency of Deep Learning Workloads through GPU Virtualization

Session Speakers
Session Description

Cognitive applications are reshaping the IT landscape with entire data centers designed and built solely for that purpose. Though computationally challenging, deep learning networks have become a critical building block to boost accuracy of cognitive offerings like Watson. We'll present a detailed performance study of deep learning workloads and how sharing accelerator resources can improve throughput by a factor of three, effectively turning a four GPU commodity cloud system into a high-end, 12-GPU supercomputer. Using Watson workloads from three major areas that incorporate deep learning technology (language classification, visual recognition, and speech recognition), we document effectiveness and scalability of this approach.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Performance Optimization
Cloud Services
50 minutes
Session Schedule