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GTC ON-DEMAND

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Abstract:
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.
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.  Back
 
Topics:
Artificial Intelligence and Deep Learning, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7320
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