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GTC ON-DEMAND

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Abstract:
We'll explore the challenges of accelerating an adaptive Cartesian mesh CFD Solver, PARAS-3D, in existing CPUs and GPUs. The memory-bound nature of CFD codes is an obstacle to higher performance, and the opt-tree structure of adaptive Cartesian meshes adds the challenge of data parallelism. Cartesian mesh solvers have higher memory bandwidth requirements due to their larger and varying stencil. We'll detail how redesigning and implementing a legacy Cartesian mesh CFD solver and improving algorithms and data structures helped us achieve higher performance in CPUs. We'll also explain how we used a structure of array-based data layout and GPU features like Unified Memory and Multi Process Service to improve GPU performance over a CPU-only version.
We'll explore the challenges of accelerating an adaptive Cartesian mesh CFD Solver, PARAS-3D, in existing CPUs and GPUs. The memory-bound nature of CFD codes is an obstacle to higher performance, and the opt-tree structure of adaptive Cartesian meshes adds the challenge of data parallelism. Cartesian mesh solvers have higher memory bandwidth requirements due to their larger and varying stencil. We'll detail how redesigning and implementing a legacy Cartesian mesh CFD solver and improving algorithms and data structures helped us achieve higher performance in CPUs. We'll also explain how we used a structure of array-based data layout and GPU features like Unified Memory and Multi Process Service to improve GPU performance over a CPU-only version.  Back
 
Topics:
Computational Fluid Dynamics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9665
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