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GTC ON-DEMAND

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Abstract:
We will discuss challenges and lessons learned from deploying multiple large scale HPC and AI clusters in different industries. Lessons learned will focus on end-to-end aspects of designing and deploying large scale gpu clusters including datacenter and environmental challenges, network performance and optimization, data pipeline and storage challenges as well as workload orchestration and optimization. You will learn more about open architectures for HPC, AI and Deep Learning, combining flexible compute architectures, rack scale platforms, and software-defined networking and storage, to provide a scalable software-defined deep learning environment. We will discuss strategies, providing insight into everything from specialty compute for training vs. inference to high performance storage for data workflows to orchestration and workflow management tools. We will also discuss deploying deep learning environments from development to production at scale from private cloud to public cloud.
We will discuss challenges and lessons learned from deploying multiple large scale HPC and AI clusters in different industries. Lessons learned will focus on end-to-end aspects of designing and deploying large scale gpu clusters including datacenter and environmental challenges, network performance and optimization, data pipeline and storage challenges as well as workload orchestration and optimization. You will learn more about open architectures for HPC, AI and Deep Learning, combining flexible compute architectures, rack scale platforms, and software-defined networking and storage, to provide a scalable software-defined deep learning environment. We will discuss strategies, providing insight into everything from specialty compute for training vs. inference to high performance storage for data workflows to orchestration and workflow management tools. We will also discuss deploying deep learning environments from development to production at scale from private cloud to public cloud.  Back
 
Topics:
HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8972
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Abstract:

A lattice Boltzmann method for solving the shallow water equations and the advection-dispersion equation is developed and implemented on graphics processing unit (GPU)-based architectures. The proposed LBM is implemented to an NVIDIA Computing Processors. GPU computing is performed using the Jacket GPU engine for MATLAB and ArrayFire. Mass transport with velocity-dependent dispersion in shallow water flow is simulated by combining the MRT-LBM model and the TRT-LBM model. This talk will demonstrate the GPU parallel performance for modeling mass transport phenomena in shallow water flows.

A lattice Boltzmann method for solving the shallow water equations and the advection-dispersion equation is developed and implemented on graphics processing unit (GPU)-based architectures. The proposed LBM is implemented to an NVIDIA Computing Processors. GPU computing is performed using the Jacket GPU engine for MATLAB and ArrayFire. Mass transport with velocity-dependent dispersion in shallow water flow is simulated by combining the MRT-LBM model and the TRT-LBM model. This talk will demonstrate the GPU parallel performance for modeling mass transport phenomena in shallow water flows.

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Topics:
Climate, Weather & Ocean Modeling, Developer - Algorithms
Type:
Talk
Event:
GTC Silicon Valley
Year:
2013
Session ID:
S3324
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