Simon Layton, Boston University - Nvidia

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Profiling - Breakdown by Routine ‣Generating Interpolation matrix, coarse A most expensive 1.5 1 < 1% 2% 2% < 1% 13% Triple matrix product - coarse A 0.5 0 −0.5 −1 39% Prolongate & correct 44% Restrict residual Compute R Compute A Compute P Smooth Other −1.5 −1.5 −1 −0.5 0 0.5 1 1.5 Interpolation matrix 19
Conclusions & Further Work ‣Classical AMG entirely on GPU - Validated against Hypre - Not optimised ‣Multiple approaches - Test different methods & compare 20
Page 1 and 2: Classical Algebraic Multigrid for E
Page 3 and 4: What is Multigrid and why do we car
Page 5 and 6: Matrix as a graph ‣Variables as v
Page 7 and 8: Component (2) - Selector ‣Choose
Page 9 and 10: Component (4) - Galerkin Product
Page 11 and 12: GPU Implementation - Justification
Page 13 and 14: Interpolator (again) ‣Interpolati
Page 15 and 16: Repeated Set Union Ĉ i = C s i [ [
Page 17 and 18: Results - Convergence ‣Regular Po
Page 19: Profiling ‣1,000,000 unknowns ‣

Simon Layton, Boston University - Nvidia

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