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GTC ON-DEMAND

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Abstract:
Large-scale scientific endeavors often focus on improving predictive capabilities by challenging theory-driven simulations with experimental data. We'll describe our work at LLNL using advances in deep learning, computational workflows, and computer architectures to develop an improved predictive model the learned predictive model. We'll discuss necessary advances in machine learning architectures and methods to handle the challenges of ICF science, including rich, multimodal data (images, scalars, time series) and strong nonlinearities. These include advances in the scalability of our deep learning toolkit LBANN, an optimized asynchronous, GPU-Aware communication library, and a state-of-the-art scientific workflows. We'll also how the combination of high-performance NVLINK and the rich GPU architecture of Sierra enables us to train neural networks efficiently and begin to develop learned predictive models based on a massive data set.
Large-scale scientific endeavors often focus on improving predictive capabilities by challenging theory-driven simulations with experimental data. We'll describe our work at LLNL using advances in deep learning, computational workflows, and computer architectures to develop an improved predictive model the learned predictive model. We'll discuss necessary advances in machine learning architectures and methods to handle the challenges of ICF science, including rich, multimodal data (images, scalars, time series) and strong nonlinearities. These include advances in the scalability of our deep learning toolkit LBANN, an optimized asynchronous, GPU-Aware communication library, and a state-of-the-art scientific workflows. We'll also how the combination of high-performance NVLINK and the rich GPU architecture of Sierra enables us to train neural networks efficiently and begin to develop learned predictive models based on a massive data set.  Back
 
Topics:
HPC and Supercomputing, Accelerated Data Science, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9565
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Abstract:

Understanding the biology of cancer at the molecular scale is a critical challenge for the RAS oncogene family of cancers. We are developing an adaptive molecular dynamics simulation framework that uses multi-scale models to achieve simulation time scales that allow biologically interesting behaviors to emerge. We'll develop new deep learning techniques that can help identify phase transitions, the formation of complex structures, and the detection of interesting events between the RAS protein and cell membrane. This molecular dynamics simulation data will drive the need for new techniques in both model and data parallelism within deep learning toolkits, and require the capabilities of next-generation supercomputers such as SIERRA and Summit at LLNL and ORNL, respectively.

Understanding the biology of cancer at the molecular scale is a critical challenge for the RAS oncogene family of cancers. We are developing an adaptive molecular dynamics simulation framework that uses multi-scale models to achieve simulation time scales that allow biologically interesting behaviors to emerge. We'll develop new deep learning techniques that can help identify phase transitions, the formation of complex structures, and the detection of interesting events between the RAS protein and cell membrane. This molecular dynamics simulation data will drive the need for new techniques in both model and data parallelism within deep learning toolkits, and require the capabilities of next-generation supercomputers such as SIERRA and Summit at LLNL and ORNL, respectively.

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Topics:
Computational Biology & Chemistry, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7792
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