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GTC ON-DEMAND

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Abstract:

Data is the key to AI success and an efficient data infrastructure is vital to the success of any deep learning (DL) project.  Bottlenecks in the flow of data can negatively impact model quality and time to results. Come join us to learn about: A DL data pipeline that supports the efficient flow of data from its sources in enterprise data stores and the internet of things through to deployment of a validated, working DL system How to identify DL data pipeline bottlenecks and address them by putting in place a data infrastructure that efficiently serves the different demands for capacity, performance and insight as data flows through the pipeline How to plan for seamless growth of your organization's AI data infrastructure, from experimentation through production to enterprise-wide expansion so you can start today with confidence that you are building a sound foundation for your organization's AI future  

Data is the key to AI success and an efficient data infrastructure is vital to the success of any deep learning (DL) project.  Bottlenecks in the flow of data can negatively impact model quality and time to results. Come join us to learn about: A DL data pipeline that supports the efficient flow of data from its sources in enterprise data stores and the internet of things through to deployment of a validated, working DL system How to identify DL data pipeline bottlenecks and address them by putting in place a data infrastructure that efficiently serves the different demands for capacity, performance and insight as data flows through the pipeline How to plan for seamless growth of your organization's AI data infrastructure, from experimentation through production to enterprise-wide expansion so you can start today with confidence that you are building a sound foundation for your organization's AI future  

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Topics:
Deep Learning & AI Frameworks
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91038
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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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Abstract:
We'll demonstrate some of the design choices required to provide a distributed, in-memory, GPU-accelerated, parallel mathematics library, distributed mathematics (dMath). The library considers some of the most common functionality required for effective scaling of deep learning pipelines for a variety of recognition and understanding tasks. The core of the problem is efficient implementations of common basic linear algebra subprograms (BLAS) and specific abstractions for learning at scale.
We'll demonstrate some of the design choices required to provide a distributed, in-memory, GPU-accelerated, parallel mathematics library, distributed mathematics (dMath). The library considers some of the most common functionality required for effective scaling of deep learning pipelines for a variety of recognition and understanding tasks. The core of the problem is efficient implementations of common basic linear algebra subprograms (BLAS) and specific abstractions for learning at scale.  Back
 
Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6669
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