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GTC ON-DEMAND

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Abstract:
With every generation of GPU it becomes increasingly more difficult to keep the data pipeline full so that the GPU can be fully utilized. We'll propose a method for offloading the CPU and using the GPU to process image data to increase throughput.
With every generation of GPU it becomes increasingly more difficult to keep the data pipeline full so that the GPU can be fully utilized. We'll propose a method for offloading the CPU and using the GPU to process image data to increase throughput.  Back
 
Topics:
Deep Learning & AI Frameworks, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8906
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Abstract:

Classical algebraic multigrid (AMG) is one of the most popular algorithms used in engineering, and the engine in many successful commercial packages. Among sparse linear solvers, it is known for being fast, parallel and scalable, yet it maps to GPU architecture with some considerable difficulty. We have tackled these difficulties and currently have a full CUDA implementation of classical AMG, which has been validated against the gold-standard, Hypre. Significant effort was dedicated to reducing thread divergence and optimizing memory access, and we continue to work on performance improvements. We are aiming for a competitive AMG code for fluid dynamics applications.

Classical algebraic multigrid (AMG) is one of the most popular algorithms used in engineering, and the engine in many successful commercial packages. Among sparse linear solvers, it is known for being fast, parallel and scalable, yet it maps to GPU architecture with some considerable difficulty. We have tackled these difficulties and currently have a full CUDA implementation of classical AMG, which has been validated against the gold-standard, Hypre. Significant effort was dedicated to reducing thread divergence and optimizing memory access, and we continue to work on performance improvements. We are aiming for a competitive AMG code for fluid dynamics applications.

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Topics:
Computational Fluid Dynamics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2012
Session ID:
S2305
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