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GTC ON-DEMAND

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Abstract:
We'll discuss how code teams at Lawrence Livermore National Laboratory (LLNL) are porting our production applications to Sierra, LLNL's flagship NVIDIA GPU-Based supercomputer. In general, our codes rely on a three-stage process of investment, porting, and performance tuning to achieve performance on NVIDIA Tesla V100 GPUs, while maintaining portability to our other supported platforms. We'll explain why this process poses many challenges and how LLNL code teams have worked with the Sierra Center of Excellence to build experience and expertise in porting complex multi-physics simulation tools to NVIDIA GPU-Based HPC systems. We'll also provide an overview of this porting process, the abstraction technologies employed, lessons learned, and current challenges.
We'll discuss how code teams at Lawrence Livermore National Laboratory (LLNL) are porting our production applications to Sierra, LLNL's flagship NVIDIA GPU-Based supercomputer. In general, our codes rely on a three-stage process of investment, porting, and performance tuning to achieve performance on NVIDIA Tesla V100 GPUs, while maintaining portability to our other supported platforms. We'll explain why this process poses many challenges and how LLNL code teams have worked with the Sierra Center of Excellence to build experience and expertise in porting complex multi-physics simulation tools to NVIDIA GPU-Based HPC systems. We'll also provide an overview of this porting process, the abstraction technologies employed, lessons learned, and current challenges.  Back
 
Topics:
HPC and AI, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9512
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