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GTC ON-DEMAND

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Abstract:
Research is evolving to be even more data-centric. AI is driving this change and is increasingly enabling breakthroughs. For analyzing large data, AI is helping to spot correlations and identify anomalies. For simulation and modeling, AI is reducing time to solution by orders of magnitude by replacing expensive computation with fast inferencing. This talk describes two unique platforms at the Pittsburgh Supercomputing Center that combine AI and HPC, at no cost for research and education. Bridges-AI, available today and an AI-focused extension to the Bridges supercomputer, features an NVIDIA DGX-2 and HPE Apollo 6500 servers, with 88 Volta GPUs total. Bridges-2 will build on Bridges and Bridges-AI to serve AI and AI-enabled simulation of tomorrow. To illustrate the systems impact, we will detail use cases in genomics and medical imaging, weather forecasting, agricultural sustainability, and other fields. Learn whats possible, how to get access, and of opportunities for collaboration.
Research is evolving to be even more data-centric. AI is driving this change and is increasingly enabling breakthroughs. For analyzing large data, AI is helping to spot correlations and identify anomalies. For simulation and modeling, AI is reducing time to solution by orders of magnitude by replacing expensive computation with fast inferencing. This talk describes two unique platforms at the Pittsburgh Supercomputing Center that combine AI and HPC, at no cost for research and education. Bridges-AI, available today and an AI-focused extension to the Bridges supercomputer, features an NVIDIA DGX-2 and HPE Apollo 6500 servers, with 88 Volta GPUs total. Bridges-2 will build on Bridges and Bridges-AI to serve AI and AI-enabled simulation of tomorrow. To illustrate the systems impact, we will detail use cases in genomics and medical imaging, weather forecasting, agricultural sustainability, and other fields. Learn whats possible, how to get access, and of opportunities for collaboration.  Back
 
Topics:
HPC and Supercomputing
Type:
Talk
Event:
Supercomputing
Year:
2019
Session ID:
SC1914
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Abstract:

PSC's "Bridges" was the first system to successfully converge HPC, AI, and Big Data. Designed for the U.S. national research community and supported by NSF, it now serves approximately 1600 projects and 7500 users at over 350 institutions. Bridges emphasizes "nontraditional" uses that span the life, physical, and social sciences, engineering, and business, many of which are based on AI or AI-enabled simulation. We describe the characteristics of Bridges that have made it a success, and we highlight several inspirational results and how they benefited from the system architecture. We then introduce "Bridges AI", a powerful new addition for balanced AI capability and capacity that includes NVIDIA's DGX-2 and HPE NVLink-connected 8-way Volta servers. 

PSC's "Bridges" was the first system to successfully converge HPC, AI, and Big Data. Designed for the U.S. national research community and supported by NSF, it now serves approximately 1600 projects and 7500 users at over 350 institutions. Bridges emphasizes "nontraditional" uses that span the life, physical, and social sciences, engineering, and business, many of which are based on AI or AI-enabled simulation. We describe the characteristics of Bridges that have made it a success, and we highlight several inspirational results and how they benefited from the system architecture. We then introduce "Bridges AI", a powerful new addition for balanced AI capability and capacity that includes NVIDIA's DGX-2 and HPE NVLink-connected 8-way Volta servers. 

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Topics:
HPC and AI
Type:
Talk
Event:
Supercomputing
Year:
2018
Session ID:
SC1832
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Abstract:
Tracing pathways through large volumes of data is an incredibly tedious, time-consuming process that significantly encumbers progress in neuroscience and the tracing of neurons through an organism. We'll explore the potential for applying deep learning to the automation of high-resolution scanning electron microscope image data segmentation. We've started with neural pathway tracing through 5.1GB of whole-brain serial-section slices from larval zebrafish collected by the Center for Brain Science at Harvard. This kind of manual image segmentation requires years of careful work to properly trace the neural pathways in an organism as small as a zebrafish larvae, which is approximately 5mm in total body length. Automating this process could vastly improve productivity, which would lead to faster data analysis and more breakthroughs in understanding the complexity of the brain.
Tracing pathways through large volumes of data is an incredibly tedious, time-consuming process that significantly encumbers progress in neuroscience and the tracing of neurons through an organism. We'll explore the potential for applying deep learning to the automation of high-resolution scanning electron microscope image data segmentation. We've started with neural pathway tracing through 5.1GB of whole-brain serial-section slices from larval zebrafish collected by the Center for Brain Science at Harvard. This kind of manual image segmentation requires years of careful work to properly trace the neural pathways in an organism as small as a zebrafish larvae, which is approximately 5mm in total body length. Automating this process could vastly improve productivity, which would lead to faster data analysis and more breakthroughs in understanding the complexity of the brain.  Back
 
Topics:
Artificial Intelligence and Deep Learning, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7449
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