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GTC ON-DEMAND

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Abstract:
XGC is a kinetic whole-­?volume modeling code with unique capabilities to study tokamak edge plasmas in real geometry and answer important questions about the design of ITER and other future fusion reactors. The main technique is the Particle-­?in-­?Cell method, which models the plasma as billions of quasiparticles representing ions and electrons. Ostensibly, the process of advancing each particle in time is embarrassingly parallel. However, the electric and magnetic fields must be known in order to push the particle, which requires an implicit gather operation from XGC's sophisticated unstructured mesh. In this session, we'll show how careful mapping of field and particle data structures to GPU memory allowed us to decouple the performance of the critical electron push routine from size of the simulation mesh and allowed the true particle parallelism to dominate. This improvement enables performant, high resolution, ITER scale simulations on Summit.
XGC is a kinetic whole-­?volume modeling code with unique capabilities to study tokamak edge plasmas in real geometry and answer important questions about the design of ITER and other future fusion reactors. The main technique is the Particle-­?in-­?Cell method, which models the plasma as billions of quasiparticles representing ions and electrons. Ostensibly, the process of advancing each particle in time is embarrassingly parallel. However, the electric and magnetic fields must be known in order to push the particle, which requires an implicit gather operation from XGC's sophisticated unstructured mesh. In this session, we'll show how careful mapping of field and particle data structures to GPU memory allowed us to decouple the performance of the critical electron push routine from size of the simulation mesh and allowed the true particle parallelism to dominate. This improvement enables performant, high resolution, ITER scale simulations on Summit.  Back
 
Topics:
HPC and AI, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8909
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