4 papers
On the energy efficiency of sparse matrix computations on multi-GPU clusters
Massimo Bernaschi, Alessandro Celestini, Pasqua D'Ambra +1
We investigate the energy efficiency of a library designed for parallel computations with sparse matrices. The library leverages high-performance, energy-efficient Graphics Process…
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 for…
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…
Communication-reduced Conjugate Gradient Variants for GPU-accelerated Clusters
Massimo Bernaschi, Mauro G. Carrozzo, Alessandro Celestini +2
Linear solvers are key components in any software platform for scientific and engineering computing. The solution of large and sparse linear systems lies at the core of physics-dri…