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GTC On-Demand

Computational Fluid Dynamics
Presentation
Media
Using Kokkos for Performant Cross-Platform Acceleration of Liquid Rocket Simulations

We'll demonstrate acceleration of a large, preexisting Fortran fluid dynamics solver using Kokkos, a C++ library that enables a single codebase to achieve high performance on multiple parallel architectures, including NVIDIA GPUs. We'll describe the complete process: identifying performance-critical physics subroutines, porting and optimizing these routines, integrating Kokkos C++ with the main Fortran code in a minimally invasive way, and tuning cluster-level performance. We'll compare the performance achieved when Kokkos uses NVIDIA Tesla K40 GPUs, Knight's Corner Xeon Phis, and Xeon CPUs. We'll also present some GPU-specific optimizations. For "trivially parallel" physics calculations, assigning one NVIDIA CUDA thread to each grid point may not be ideal. If a small team works cooperatively on each grid point, performance can improve due to the larger amount of effective cache available to each team.

We'll demonstrate acceleration of a large, preexisting Fortran fluid dynamics solver using Kokkos, a C++ library that enables a single codebase to achieve high performance on multiple parallel architectures, including NVIDIA GPUs. We'll describe the complete process: identifying performance-critical physics subroutines, porting and optimizing these routines, integrating Kokkos C++ with the main Fortran code in a minimally invasive way, and tuning cluster-level performance. We'll compare the performance achieved when Kokkos uses NVIDIA Tesla K40 GPUs, Knight's Corner Xeon Phis, and Xeon CPUs. We'll also present some GPU-specific optimizations. For "trivially parallel" physics calculations, assigning one NVIDIA CUDA thread to each grid point may not be ideal. If a small team works cooperatively on each grid point, performance can improve due to the larger amount of effective cache available to each team.

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Keywords:
Computational Fluid Dynamics, Tools and Libraries, Computer Aided Engineering, GTC Silicon Valley 2017 - ID S7148
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Computational Physics
Presentation
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GPU-Accelerated String Method for Defect Annealing in Copolymer Self-Assembly
Diblock copolymers possess fascinating self-assembly properties that can be leveraged for a variety of industrial applications, most notably nanolithography. However, such efforts are often impeded by the formation of metastable defect structures. We present a GPU-accelerated method to quantify the difficulty of defect removal, guiding experiments toward optimal polymer chemistry. We demonstrate that this problem is ideally suited to NVIDIA GPUs' massively parallel architecture.
Diblock copolymers possess fascinating self-assembly properties that can be leveraged for a variety of industrial applications, most notably nanolithography. However, such efforts are often impeded by the formation of metastable defect structures. We present a GPU-accelerated method to quantify the difficulty of defect removal, guiding experiments toward optimal polymer chemistry. We demonstrate that this problem is ideally suited to NVIDIA GPUs' massively parallel architecture.  Back
 
Keywords:
Computational Physics, Life & Material Science, GTC Silicon Valley 2015 - ID P5308
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