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GTC ON-DEMAND

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Abstract:
We will present a campaign to investigate the use of supersonic retropropulsion as a means to land payloads on Mars large enough to enable human exploration. Simulations are performed on the worlds largest supercomputer, Summit, located at Oak Ridge National Laboratory. The engineering and computational challenges associated with retropropulsion aerodynamics and the need for large-scale resources like Summit are reviewed. For these simulations, a GPU implementation of NASA Langley Research Center's FUN3D flow solver is used. The development history, performance, and scalability are compared with those of contemporary HPC architectures. The use of an optimized GPU-accelerated CFD solver on Summit has enabled simulations well beyond conventional computing paradigms.
We will present a campaign to investigate the use of supersonic retropropulsion as a means to land payloads on Mars large enough to enable human exploration. Simulations are performed on the worlds largest supercomputer, Summit, located at Oak Ridge National Laboratory. The engineering and computational challenges associated with retropropulsion aerodynamics and the need for large-scale resources like Summit are reviewed. For these simulations, a GPU implementation of NASA Langley Research Center's FUN3D flow solver is used. The development history, performance, and scalability are compared with those of contemporary HPC architectures. The use of an optimized GPU-accelerated CFD solver on Summit has enabled simulations well beyond conventional computing paradigms.  Back
 
Topics:
HPC and Supercomputing
Type:
Talk
Event:
Supercomputing
Year:
2019
Session ID:
SC1926
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Abstract:
We'll describe the transition of an unstructured-grid computational fluid dynamics (CFD) code from a dense MPI model to shared memory models suitable for a many-core landscape. Unstructured grid approaches are often used in CFD to solve Navier-Stokes equations because they accommodate geometric complexity. Turbulent flows encountered in aerospace applications generally require highly anisotropic meshes, driving the need for implicit solution methodologies to efficiently solve discrete equations. We'll discuss node-level studies of computationally intense CFD kernels on traditional x86 architectures and on NVIDIA GPUs. We'll also talk about scaling studies performed at several large supercomputing facilities.
We'll describe the transition of an unstructured-grid computational fluid dynamics (CFD) code from a dense MPI model to shared memory models suitable for a many-core landscape. Unstructured grid approaches are often used in CFD to solve Navier-Stokes equations because they accommodate geometric complexity. Turbulent flows encountered in aerospace applications generally require highly anisotropic meshes, driving the need for implicit solution methodologies to efficiently solve discrete equations. We'll discuss node-level studies of computationally intense CFD kernels on traditional x86 architectures and on NVIDIA GPUs. We'll also talk about scaling studies performed at several large supercomputing facilities.  Back
 
Topics:
Computational Fluid Dynamics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9369
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Abstract:

Exascale-class simulations will be achieved through a combination of high concurrency and energy efficiency. Although accelerator architectures like GPUs are so equipped, the task of adapting a feature-rich legacy application to modern HPC hardware can be daunting. We present the implementation of such GPU capability in the NASA Langley FUN3D computational fluid dynamics solver. With this effort, a thousand of today's 6-GPU nodes can do the work of over a million CPU cores for a fraction of the energy cost.

Exascale-class simulations will be achieved through a combination of high concurrency and energy efficiency. Although accelerator architectures like GPUs are so equipped, the task of adapting a feature-rich legacy application to modern HPC hardware can be daunting. We present the implementation of such GPU capability in the NASA Langley FUN3D computational fluid dynamics solver. With this effort, a thousand of today's 6-GPU nodes can do the work of over a million CPU cores for a fraction of the energy cost.

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Topics:
Science and Research
Type:
Talk
Event:
Supercomputing
Year:
2018
Session ID:
SC1811
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Abstract:
In the field of computational fluid dynamics, the Navier-Stokes equations are often solved using an unstructured-grid approach to accommodate geometric complexity. Furthermore, turbulent flows encountered in aerospace applications generally require highly anisotropic meshes, driving the need for implicit solution methodologies to efficiently solve the discrete equations. To prepare NASA Langley Research Center''s FUN3D CFD solver for the future HPC landscape, we port two representative kernels to NVIDIA Pascal and Volta GPUs and present performance comparisons with a common multi-core CPU benchmark.
In the field of computational fluid dynamics, the Navier-Stokes equations are often solved using an unstructured-grid approach to accommodate geometric complexity. Furthermore, turbulent flows encountered in aerospace applications generally require highly anisotropic meshes, driving the need for implicit solution methodologies to efficiently solve the discrete equations. To prepare NASA Langley Research Center''s FUN3D CFD solver for the future HPC landscape, we port two representative kernels to NVIDIA Pascal and Volta GPUs and present performance comparisons with a common multi-core CPU benchmark.  Back
 
Topics:
Accelerated Data Science
Type:
Talk
Event:
SIGGRAPH
Year:
2017
Session ID:
SC1710
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