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GTC ON-DEMAND

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Abstract:
With every generation of GPU it becomes increasingly more difficult to keep the data pipeline full so that the GPU can be fully utilized. We'll propose a method for offloading the CPU and using the GPU to process image data to increase throughput.
With every generation of GPU it becomes increasingly more difficult to keep the data pipeline full so that the GPU can be fully utilized. We'll propose a method for offloading the CPU and using the GPU to process image data to increase throughput.  Back
 
Topics:
Deep Learning & AI Frameworks, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8906
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Abstract:
Next-generation GPUs have revealed that data loading and augmentation can be a major bottleneck to accelerating deep neural network training on many-GPU distributed systems. This work presents the design and implementation of a high-performance data loading and augmentation system for the Expresso deep learning framework developed by Samsung. Our system leverages multiple levels of parallelism and automatic runtime performance tuning to achieve speedups of 15.5% on average across our experiments.
Next-generation GPUs have revealed that data loading and augmentation can be a major bottleneck to accelerating deep neural network training on many-GPU distributed systems. This work presents the design and implementation of a high-performance data loading and augmentation system for the Expresso deep learning framework developed by Samsung. Our system leverages multiple levels of parallelism and automatic runtime performance tuning to achieve speedups of 15.5% on average across our experiments.  Back
 
Topics:
Artificial Intelligence and Deep Learning, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7569
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