4 papers
Scalable s-step Preconditioned Conjugate Gradient with Chebyshev Basis and Gauss-Seidel Gram Solve
Pasqua D'Ambra, Massimo Bernaschi, Mauro G. Carrozzo +1
We present a variant of the s--step Preconditioned Conjugate Gradient (PCG) method that combines a Chebyshev--stabilized Krylov basis with a Forward Gauss--Seidel (FGS) iteration f…
Inexact Gauss Seidel and Coarse Solvers for AMG and s-step CG
Stephen Thomas, Pasqua D'Ambra
Communication-avoiding Krylov methods require solving small dense Gram systems at each outer iteration. We present a low-synchronization approach based on Forward Gauss--Seidel (FG…
Streaming Krylov-Accelerated Stochastic Gradient Descent
Stephen Thomas
We present SKA-SGD (Streaming Krylov-Accelerated Stochastic Gradient Descent), a novel optimization approach that accelerates convergence for ill-conditioned problems by projecting…
Optimal Polynomial Smoothers for Parallel AMG
Pasqua D'Ambra, Fabio Durastante, Salvatore Filippone +2
In this paper, we explore polynomial accelerators that are well-suited for parallel computations, specifically as smoothers in Algebraic MultiGrid (AMG) preconditioners. These acce…